Cash crops are agricultural crops intended to be sold for profit as opposed to subsistence crops, meant to support the producer, or to support livestock. Since cash crops are intended for future sale, they translate into large financial value when considered on a wide geographical scale, so their production directly involves financial risk. At a national level, extreme weather events including destructive rain or hail, as well as drought, can have a significant impact on the overall economic balance. It is thus important to map such crops in order to set up insurance and mitigation strategies. Using locally generated data—such as municipality-level records of crop seeding—for mapping purposes implies facing a series of issues like data availability, quality, homogeneity, etc. We thus opted for a different approach relying on global datasets. Global datasets ensure homogeneity and availability of data, although sometimes at the expense of precision and accuracy. A typical global approach makes use of spaceborne remote sensing, for which different land cover classification strategies are available in literature at different levels of cost and accuracy. We selected the optimal strategy in the perspective of a global processing chain. Thanks to a specifically developed strategy for fusing unsupervised classification results with environmental constraints and other geospatial inputs including ground-based data, we managed to obtain good classification results despite the constraints placed. The overall production process was composed using “good-enough" algorithms at each step, ensuring that the precision, accuracy, and data-hunger of each algorithm was commensurate to the precision, accuracy, and amount of data available. This paper describes the tailored strategy developed on the occasion as a cooperation among different groups with diverse backgrounds, a strategy which is believed to be profitably reusable in other, similar contexts. The paper presents the problem, the constraints and the adopted solutions; it then summarizes the main findings including that efforts and costs can be saved on the side of Earth Observation data processing when additional ground-based data are available to support the mapping task. Since cash crops are destined for future sale, they hold a relevant financial potential during their growth season, which is constantly in danger due to various types of threats, including weather-related threats. Among these latter threats, we should surely account extreme weather events, including:Events like those outlined above may have a significant impact on those nations whose Gross Domestic Product (GDP) is largely supported by agricultural activities. Mapping cash crops at a national, or even super-national level when regional strategies are concerned, is a necessary action to assess exposure. Thus, cash crop mapping is indeed a crucial part of any large-scale risk assessment actions intended to define a risk-mitigation—or at least an insurance—strategy to be put in place.avoidance of local datasets; local datasets, such as municipality-level records of crop seeding, or surveying results from local authorities, are obviously not reusable out of their own geographical scope, but especially they may be severely inhomogeneous across different countries or regions. Global datasets were to be used, albeit at the cost of lower precision, accuracy, or resolution.leveling of expected quality; highly-refined components of the risk equation are of little use where they have to be forcibly combined with much coarser ones in the same risk computation.The work described in this paper was carried out on the Caribbean area, but designed with a global perspective in mind, in order to make it reusable anywhere else in the world. This produced the following implications:Typically, when large-scale mapping of vegetation is required, a popular approach to the problem is based on spaceborne remote sensing , for whiSpaceborne remote sensing complies with the requirements set out above, as it is inherently global, and the widest range of possible classification approaches, from the coarsest to the finest, from the leanest to the most data-hungry, ensures a suitable approach can be defined for each expected quality level.In our case, we complemented the selected classification approaches through smart fusion of unsupervised classification results with environmental constraints and other geospatial inputs including ground-based data. This paper describes the tailored strategy developed on the occasion as a cooperation among different groups with diverse backgrounds, a strategy believed to be reusable in other, similar contexts. The paper presents the problem, the constraints and the adopted solutions; it then summarizes the main findings including that efforts and costs can be saved on the side of Earth Observation data processing when additional ground-based data are available to support the mapping task. the general model used for overall risk assessment, andthe accuracy level of the other datasets incorporated in our production.At a global scale, natural disaster financing or insurance mechanisms are in big need of datasets for risk assessment, especially for developing countries where organized, systematic data collection cannot always be guaranteed due to lack of resources or structural issues see ,4,5). In,5. In4,5it was important to balance the different components of the risk model in order to avoid mixing datasets that score too differently in terms of accuracy and precision; in this case, finer data or finer models would indeed be underused;our goal was to hit the optimal trade-off between data availability and the specific needs of the vulnerability component in order to capture the main factors affecting risk.Given this context and the linked requirements, we came up with the following guidelines for our work:In the following chapter, we will specify the case analyzed, and present our solution, specifically developed from the given conditions and the guidelines above.The main aim of the work is to map the crop areas over a large territory through the combination of two mapping techniques, each individually incomplete for the purpose. The first technique is based on spaceborne remotely sensed data that is able to cluster the territory into homogeneous areas by analyzing the satellite images over a relatively long time interval, but it is not able to identify the crop kind. The second technique is able to identify the crop kinds that potentially could grow in the territory given the agro-climatic conditions but is not able to determine which are actually grown. The combination of the space-based crop mapping and the agro-climatic mapping aims at a crop classification sufficiently accurate to provide the exposure information for weather related risk assessment. Our fusion concept is illustrated in Spaceborne remote sensing is generally more efficient and effective than traditional methods, like field surveying, in describing the Earth surface. This is due to its ability to map and monitor the spatial distribution of land cover continuously and consistently at a variety of spatial and temporal scales, even though it may come at the expense of slightly worse thematic accuracy, depending on the approach used . Severalopen availability;high temporal frequency .As mentioned above, we focused on outlining homogeneous areas rather than directly classifying crop types. As expected, single-date spectral responses are very similar to each other, so multitemporal trends had to be exploited. In this context, MODIS data was the first choice, supported by two very important strongpoints it possesses in this context:lower temporal frequency; 5-days revisit time may appear very short, but in areas where cloud coverage is frequent such as the predominantly tropical Caribbean areas, the daily revisit time of Terra/Aqua can be crucial in preventing data gaps;higher spatial resolution, while unnecessary to the foreseen application, results in gigantic files to be stored and processed. This is only worthwhile when the additional data makes a difference in terms of separability of relevant land cover classes.Given the scarce spectral separability of the species to be mapped, temporal frequency becomes of paramount importance here as discrimination will have to rely upon phenological cycles as reflected in temporal trends of vegetation indexes. Terra and Aqua were launched in December 1999 and May 2002, respectively, as the first items in a series of multi-instrument spacecraft forming NASA’s Earth Observing System (EOS). The latter includes a science component and a data information system (EOSDIS) supporting global observations of both land and water and distributing data through its Distributed Active Archives . CoperniAs a consequence, we selected MODIS as the main data source for our work, and Sentinel-2 as an integrative source where necessary; k-means classification applied to the time series arranged in vectorial form. This resulted in a partition of the land areas into homogeneous regions. An urban mask obtained by classification of the multispectral dataset [The geographic scope of the contracted study, highlighted in blue in  dataset  and a re dataset  allowed As it was mentioned in The methodology described in the Definition of the agro-climatic conditions required for each crop to achieve its potential production through the regression analysis of reference data from public sources. The parameters that define the agro-climatic conditions are: (1) annual precipitation, (2) monthly temperature (minimum and maximum), (3) elevation over sea level, and (4) edaphology.Estimation of crop potential areas that are those cropland areas where all the ranges of agro-climatic conditions are fulfilled. In this step, the agro-climatic parameters defined previously and the cropland classification are contained in different layers that are spatially crossed to unify all in a single element .The final aim of the procedure described in this chapter is to obtain maps, for each country, where the areas classified as croplands are assigned potentials of production of certain types of crops. To carry out this mapping technique, two processes are basically performed:To perform the agro-climatic mapping, we needed data about elevation above sea level, monthly average temperature (°C), annual average precipitation (mm) and units of dominant soils (edaphology) for the entire region. For many countries, no information on the selected crops could be found; in these cases, the climatological conditions were estimated based on the data of neighboring countries or regions presenting similar climatic characteristics.The temperature and precipitation data were downloaded from the WorldClim’s database, which offers a wide range of monthly precipitation data, as well as monthly minimum and maximum average temperature data with a resolution of 30 s or 1 km approximately ,35. The Conditions for the same crop can even variate from one country to another. With the aim of knowing the different geo-climatic conditions of each crop in each region, an exhaustive search of reference information was performed to locate main areas of the different cash crops. Generally, these cartographic data are not very detailed, neither when obtained from remote sensing, nor from site inspections. Basically, they consist of schematic maps with general polygons without a scale or an adequate resolution obtained from national sources, for example, the Protected Areas Conservation Trust in Belize; the Ministry of Agriculture, Natural Resources and Rural Development in Haiti; the System of Information of Forest Resources of Costa Rica and the Guyana Lands and Surveys Commission, among others; and from global sources such as the United States Agency for International Development (USAID); the United States Geological Survey (USGS); the Famine Early Warning System Network (FEW Net); the Food and Agriculture Organization (FAO); the World Bank and the Interamerican Development Bank (BID).As is shown in x-axis represents the values of the agro-climatic variable and the y-axis represents the accumulated area up to a certain value of the agro-climatic variable, therefore, a 100% in the y-axis corresponds to the total area of the reference map used. The CAA were computed as follows: at first (1) to filter the agro-climatic layers through the mask of the reference maps, then (2) to obtain the histogram of areas through counting the areas/pixels associated to agro-climatic values, and finally (3) to accumulate the areas of the histogram, in terms of percentage, according to the values of the agro-climatic variable. In To define the geo-climatic conditions through the masks of the reference areas, the climatic data along with elevation data was spatially crossed with the reference areas in order to establish the maximum and minimum value inside those areas. These ranges of the agro-climatic variables for each crop and country was obtained by means of curves of accumulated area (CAA). In the CAA, the binary layers, obtaining a single layer that represents with a value of 1 the pixels/areas where the agro-climatic conditions are fulfilled.As it is shown in MODIS MCD12Q1 , which supplies global maps of land cover at annual time steps and 500 m spatial resolution for 2001 to 2013 [In order to limit the analysis to agricultural/vegetation areas, we sought cropland area maps that could be used as a mask, discarding classes without vegetation: urban, water bodies or any other class having no relation to agriculture. The selected product was  to 2013   scheme, in which seventeen land covers were identified, eleven of them being vegetation, three terrain classes and three more vegetation-free classes. Their stated objective was to provide a global land cover dataset that was more up-to-date, of known accuracy and with higher spatial resolution and greater internal consistency than any other existing dataset. The scheme is based on definitions of three canopy components: above-ground biomass, leaf longevity, and leaf type process. The land-cover categories identified by the IGBP are related to the needs of gas exchange studies; vegetation attributes for modeling Net Primary Production (NPP); burn emissions and gas exchange; wetlands cover and wetland water regimes; changes in vegetation/land-cover over time; biological attributes; physical attributes, and landscape characteristics ,39,40,41The University of Maryland land cover classification (UMD) dataset, with fourteen classes, two of which have no vegetation. The approach taken involved a hierarchy of pairwise class trees where a logic based on vegetation form was applied until all classes were depicted. Minimum annual red reflectance, peak annual Normalized Difference Vegetation Index (NDVI), and minimum channel three brightness temperature were among the most used multitemporal metrics. Depictions of forests and woodlands, and areas of mechanized agriculture are in general agreement with other sources of information, while classes such as low biomass agriculture and high-latitude broadleaf forest are not .The LAI/FPAR scheme, with nine vegetation classes and two vegetation- free ones. This scheme uses a method for the estimation of global leaf area index (LAI) and fraction of photosynthetically active radiation absorbed by the vegetation (FPAR) from atmospherically corrected Normalized Difference Vegetation Index (NDVI) observations. LAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one half of the total needle surface area per unit ground area in coniferous canopies. FPAR is defined as the fraction of incident photo-synthetically active radiation (400–700nm) absorbed by the green elements of a vegetation canopy. The method requires stratification of global vegetation into cover types that are compatible with the radiative transfer model .The Net Primary Production scheme (NPP) with nine vegetation classes and two vegetation-free ones. NPP defines the rate at which all plants in an ecosystem produce net useful chemical energy. In other words, NPP equals the difference between the rate at which plants in an ecosystem produce useful chemical energy , and the rate at which they expend some of that energy for respiration. The Primary Production products are designed to provide an accurate regular measure of the growth of the terrestrial vegetation. Version-55 Terra/MODIS NPP products are validated to Stage-3; this means that its accuracy was assessed and uncertainties in the product were well-established via independent measurements made in a systematic and statistically robust way that represents global conditions. These data are deemed ready for use in science applications .The Functional Type Plant scheme (FTP) with nine vegetation classes, two vegetation-free and one ice-water class. While most land models developed for use with climate models represent vegetation as discrete biomes, this is, at least for mixed life-form biomes, inconsistent with the leaf-level and whole-plant physiological parameterizations needed to couple these bio-geophysical models with bio-geochemical and ecosystem dynamics models. In the calculation of this scheme, the authors present simulations with the National Center for Atmospheric Research land surface model (NCAR LSM) that examined the effect of representing vegetation as patches of plant functional types (PFTs) that coexist within a model grid cell. This approach is consistent with ecological theory and models and allows for unified treatment of vegetation in climate and ecosystem models .The n method . In addiNPP scheme that does not include any class related to croplands; therefore, it was not considered in the process. The classes corresponding to each crop are obtained and combined according to its leaf morphology. The obtained combinations for each one of the selected crops are listed in The different classes included in the classification schemes are shown in The spectrally homogeneous areas extracted from satellite imagery , and then the identified homogeneous classes were applied to the areas/pixels with agro-potential uncertainty .The first criteria to classify a homogeneous class was the potential crop type with the higher percentage of suitable sub-area for the areas without uncertainties. After obtaining the homogeneous classes related to each type of crop, we merged the areas/pixels with uncertainties, and then we summed the areas covered by the different selected homogeneous classes to estimate the amount of covered area of each crop, making sure at the same time that they were close to the reported statistics of harvested area , and that the identified areas covered the checkpoints.In cases where the obtained total area was far below the reported statistics, we returned to the first step and included the following homogeneous class with higher area percentage to the analyzed crop, provided it was not selected for another crop, and repeat the process until we were close to the target. In this case, the group of homogeneous classes selected were labeled as the analyzed crop. In the contrary case, when the total labeled area was far above the statistic, we discarded the selected homogeneous class and chose another class with smaller coverage area and repeated the process.Crop areas are mutually exclusive, so if an homogeneous class or a group of homogeneous classes were labeled as a certain type of crop, they can not be labeled as another type. Once the assignation was completed and the conditions had been verified, the next step was to extract from the homogeneous areas the assigned classes and create the final map of crop areas.A test was carried out on the region of San Miguel, El Salvador, which was first analyzed using high-resolution satellite imagery processing through fusing two different sources of multispectral spaceborne remote sensing images  and crowdsourcing data .In The approach described in Panoramio, owned by Google , which possesses a database of geo-located tag photos uploaded by users around the globe. Panoramio only allows outdoors photos, which is advantageous to us because it implies removing a lot of data clutter. Every time a user uploads a picture, it is revised for compliance with this criterion, which ensures we only find relevant pictures when searching in agricultural areas. The site could be accessed as a layer in Google Earth and Google Maps. The total number of checkpoints used in the region can be seen in The final areas obtained for each crop are spatially compared with checkpoints and the reference areas from In order to have both spatial and numerical certitude, the final areas were compared with the statistics of the harvested area of the countries of the region to verify that the error be under acceptable levels. As it can be seen in In this work, we present a methodology to map cash crops on a large geographical scale, for purposes of weather-related risk assessment. Cash crops are agricultural crops grown to be sold for profit, so losses on cash crops due to weather-related events such as flood, drought, and hail, can produce a large financial impact. Such impact may be especially dangerous for countries that strongly depend on their production like the Caribbean islands and Central American countries, so risk assessment and consequent insurance is necessary to keep those countries somehow safer from the financial effects of extreme weather events.A founding part of risk assessment is exposure mapping, i.e., mapping the spatial distribution of resources that are at stake. Mapping agricultural resources on a large area such the one addressed in this study may be very costly and may weigh excessively on the overall balance of cash crop businesses.Confronted with this problem, we developed a novel technique for large-scale crop mapping, largely relying on open data and algorithms offering low computational burdens. Specifically, two mapping techniques were merged, unsupervised classification on time-series of multispectral data, and climate-based zoning. Each of these techniques individually would be insufficient for cash crop mapping, but a suitable combination leads to our goal while curbing the cost of data procurement and processing. The space-based mapping technique clusters’ homogeneous crop areas, i.e., areas showing an evolution in time of vegetative parameters that is “sufficiently homogeneous” within each region and “sufficiently different” across regions to conclude that areas of homogeneous crop species were identified. The agro-climatic technique can instead identify crops potentially grown at each different environmental condition. Each technique individually provides results with a certain degree of ambiguity, but merging information from both results in sensible crop maps, through cross-resolution of conveyed ambiguities.We tested the developed methodology over the Caribbean and Central America countries. The resulting cash crop maps have been validated both visually and by comparison with FAO’s data. On 294 different checkpoints and reference areas, the comparison of the mapped crops with geo-referenced pictures available via Google Panoramio showed very good agreement. In order to provide an additional assessment of the quality of results, FAO’s data of the harvested area by countries and crops were compared with mapping results, showing mismatch figures below 10%. This mismatch level is considered sufficient for risk assessment purposes because weather threats to crop yield  tend to affect medium- to large-sized areas, thus averaging out local classification errors.With respect to other possible approaches to large-scale crop mapping, the main advantages of the proposed method are its lower cost  lower computational burden and its flexibility .In the context of risk assessment, the importance of exposure data is fundamental. However, the accuracy and resolution of these data should be coherent with the purposes and the requirements of the risk model applied. Often, the approximations involved in the vulnerability or hazard component of the model would make overcast the accuracy and the resolution of exposure crop data. The developed methodology allows for a direct application on other territories since it only makes use of globally accessible and available data and of unsupervised automatic techniques.


Continuous fibre reinforced composites are now firmly established engineering materials for the manufacture of components in the automotive and aerospace industries. In this respect, composite fabrics provide flexibility in the design manufacture. The ability to define the ply shapes and material orientation has allowed engineers to optimize the composite properties of the parts. The formulation of new numerical models for the simulation of the composite forming processes must allow for reduction in the delay in manufacturing and an optimization of costs in an integrated design approach. We propose two approaches to simulate the deformation of woven fabrics: geometrical and mechanical approaches. Composite materials with glass, carbon or aramid fibres and polymeric matrices are known to have high specific stiffness and, in combination with automatic manufacturing processes, make it possible to fabricate complex composite structures with high levels of weight and cost efficiency. As known, the substitution of metal alloys by composite materials, in general, reduces structure mass by 20–30%. This increase is also due to the wide variety of semi-products  available permitting the elaboration of new structures. Fabrication processes, also, have undergone substantial evolution in recent years. Although the traditional lay-up process will remain the process of choice for some applications, new developments in resin transfer moulding, sheet moulding compound, low temperature curing prepregs and low pressure moulding compounds are reached new heights of sophistication, and are now being exploited in high technology areas such as the aerospace industry ,2,3,4,5.The simulation of the manufacturing of a textile reinforced composite part with a Liquid Composite Moulding-like process, which involves draping (or deep-drawing) and impregnation of the preform, includes several stages. First, a mould is designed with CAD software, and the CAD model is meshed. Then, a draping (or deep-drawing) simulation tool is used to compute the deformations of the textile layers inside the mould. As a result, for every element of the mesh, textile parameters like the shear angle and the thickness of the layer are available. With these parameters given, the local  properties for every element is determined (pre-processing), and given as input for the macroscopic structure after resin polymerization simulation. The result of the macro-simulation is then post-processed to optimize the mechanical properties of composite structure. The numerical simulation of composite forming is an efficient means of evaluating factors related to manufacturing processes and an efficient help to design pre-forming sequence for the manufacturing of fabric reinforced composites. It is thus possible to detect the main problems occurring during the shaping deformation and to obtain good quantitative information on the forming process ,7,8,9,10The choice of manufacturing process depends on the type of non-polymerized matrix (or resin) and fibres, the temperature required to form the part and the cost effectiveness of the process. Often, the manufacturing process to be used is the initial consideration in the design of composite parts. Each manufacturing process imposes particular limitations on the structural design. It is necessary to develop specific algorithms taking into account not only the appropriated mechanics of the semi-products but also the associated process. Different levels of modelling intervene in the design process see : architeLaying-up or draping is the most common method of producing thin composite parts ,20,21,22In this study, we propose two numerical approaches to simulate the forming of composite fabrics: geometrical and mechanical approaches. The geometrical approach is well adapted to pre-dimensioning level. It is based on geometrical aspects of the warping. Our method is based on a modified "MOSAIC" algorithm, which is suitable to generate a regular quad mesh representing the lay-up of the curved surfaces (giving the exact fibre orientations). The method is implemented in the GeomDrap software  which isThe mechanical approach is described on a meso-structural approach for finite deformations and geometrical non-linearity. The not polymerized resin has a viscous behavior and the reinforced fibers are treated as either unidirectional non linear elastic behavior. The bi-component finite elements for modeling composite fabric behavior are based on an association of 3D membrane finite elements representative of resin behavior and truss finite elements representative of warp and weft fibers behavior. The efficiency of the proposed model resides in the simplicity of its finite element discretization and the performance of its mechanical background ,36,37,38However, due to the imposition of large displacements, the bi-component finite element mesh representing the workpiece undergoes severe shear of fibres hence, necessitates remeshing or the generation of a new mesh for the deformed/evolved geometric representation of the computational domain ,42,43,44Starting point associated with a node of fabric Initial warp and weft orientation In this section, we present the mathematical formulation of the geometrical draping of composite fabric and then we propose a new algorithm scheme to solve the numerical draping problem. Let us denote by Σ the non developable tool surface of the part to drape and we assume that a geometrical mesh fabric Φ . The proAssociate the starting point on geometrical part mesh of the mould Compute numerically step by step the warp nodes of the corresponding fabric mesh Compute numerically step by step the weft nodes of the corresponding fabric mesh x0 and the nodes associated with nodes Compute numerically cell by cell all the other nodes of To ensure a unique solution, we suppose that the starting points (corresponding to the point of impact of the machine to drape) ng steps : Associai.e., a mesh of the surface). Based on this latter approach, we propose a new algorithm in which the geodesic lines on the surface are approximated by the polylines plotted on the surface using linear orthogonal transformation in ℜ3 to setting flat the surface locally (these polylines become a straight line segment after these transformations). This allows us to determine the The nodes of proposed ,46,47,48d vertex ,32,45,47i.e., far enough from the free edges of the fabric). Also, for the composite fabrics based on high modulus, the compressive as well as bending stiffness are negligible compared to the in-plane membrane stiffness. The assumption is that each cross connexion of straight warp and weft fibre before deformation remains cross connected during the transformation. The basic assumptions for the mechanical forming are that the woven fabric is considered as a continuous 3D surface. The warp and weft fibres are assimilated as a truss which connecting points are hinged and the membrane resin is coupled kinematically to the fabric at these connecting points.The deformation modes of composites fabrics during the forming process are different than those of sheet metal ,53,54,590 as : Each fibre (warp and weft) point  X→m see . At the Using the above assumptions, the mechanical behaviour of composite fabric depends on the relative movement of fibres and the deformation of resin. The problem of the integration of strain rate tensors is a central one in large deformations. The rate equations for finite strains use objective derivatives . The fraThe longitudinal fibres stress The behaviour in tension of the fibres is supposed elastic non-linear (due to the fabric weaving) and is related to the elastic modulus Each material point is moving as in a continuum, ensured by the non-sliding of fibers due to fabric weaving and resin behavior see . TherefoThe effect of spatial equilibrium of composite material on the actual configuration is established in terms of nonlinear equations: kinematic non-linearity, material non-linearity and contact with friction non-linearity. It is linearized for each load increment by an iterative Newton-Raphson method. According to the different modes of deformation occurring in the composite fabric during the shaping process, bi-component finite elements are used to characterize the mechanical behavior of thin composite fabric before non-polymerized matrix. The bi-component element is based on an association of linear membrane finite element  combined with a complementary truss linear finite element. The global stiffness of composite fabric is obtained by discreet summation of fibres (warp and weft) and not polymerized matrix stiffness. The governing equilibrium nonlinear equations (6) are solved using dynamic explicit integration. This approach has proven to be, in particular, suitable to highly non-linear geometric and material problems and where a large amount of contact between different structural parts occurs ,19,56. TDefinition of a physical size map based on the adaptation of the mesh element size with respect to one of the mechanical fields.Definition of a geometrical size map based on the geometric curvature of the boundary.Threshold refinement: numerical values of the estimated local error are used both to select the elements to be refined and to decide into how each new elements may be subdivided.Refinement with solution estimation: a solution approximation and its local error on a mesh are estimated from a previous approximation solution.Let us recall that the adaptive remeshing of fabrics during draping of deep-drawing is never approached in the literature. In this work, we propose specific algorithm based on an iterative step to simulate numerically the forming process of composite fabric using commercial software ABAQUS. At first, a coarse initial mesh of the fabric is generated with bi-component finite elements . Then, remeshing procedure is applied after each displacement according to the following scheme:A physical size map is defined by calculating a physical size for each element of the part. This physical size is defined with respect of one of the mechanical field. In this paper, the shear stress of the not polymerized resin has been chosen. Minimal element size is associated to maximal shear stress  and a maximal element size for minimal shear stress and for the other elements, a linear size variation can be used. For a given element, if the ratio between the average size of its edges and its physical size is greater than a given threshold, the element must be refined.The geometrical size map indicates if a boundary element must be refined or not. The geometric curvature is estimated at each boundary vertex of the domain. If this curvature has been modified during the deformation of the computational domain and exceed a value limits given by the user (two degrees in our case), all elements sharing this boundary vertex must be refined.Coarsening step during which the mesh is coarsened with respect to the physical size map,Refinement step during which the mesh is refined according to the geometrical size map and then to the physical size mapDefine the deformed truss finite elements representing the fixed warp and weft fibres discretization.Transfer the mechanical field of non-polymerized resin (stress) and fibres (tensile force) from the old mesh to the new mesh.The adaptive remeshing technique consists in improving the mesh by coarsening and refinement methods in order to conform to the geometry of membrane and truss elements during deformation. Four consecutive steps are executed:There is only one element subdivision which allows preserving the element shape quality: the uniform subdivision into four new elements. For this subdivision, a node is added is the middle of each edge of the element. Boundary elements which belong to the geometrical size map are first refined. The refinement is then applied according to the physical size map. In this case, the refinement procedure is repeated as long as the physical size map is not reached. After each refinement procedure , an iterative refinement to restore mesh conformity is necessary. Indeed, after applying the subdivision according to the geometrical or physical criteria, adjacent elements to subdivided elements must be modified see . A proceDue to the importance of the composite fabric behaviour on material formability, tensile test of pre-impregnated fabric is proposed in order to study the influence of fibre orientation, fibre undulations due to fabric weaving and resin behaviour. At processing, the impregnated fabric is idealised as a viscous material subject to the kinematic constraints of incompressibility and inextensibility in the fibre direction. The pre-impregnated fabric tested in this study was a satin 5 with aramid woven fabric. The fabric was impregnated with epoxy resin using a hot-melt pre-pregging process. A lower loading velocity will generate lower viscous forces at the intra-ply shearing of fabric. In this experiment, a displacement in the vertical direction is imposed at the moving extremity of the rectangular composite specimen with three layers of fibres . The mechanical properties of the pre-impregnated fabric have been experimentally determined by . The mec.50s see .In the numerical modelling, the behaviour of the resin is assumed to be isotropic viscoelastic and that of the fibre, be elastic linear or elastic non-linear. Exponential law is used to describe the geometrical undulation of fibres and Dirichlet Prony series is used to approximate the creep functions relaxation of the not polymerized resin (Equation 5) .The uniaxial tensile tests  are carrThe effect of the imposed load velocities  on the composite fabric for 45° of fibre direction is given in sh = 0 (the undulation effect is negligible) and εsh = 0.005. This figure describes, at the beginning of the curves, the viscoelastic phase followed by a kinematic stage which increases with the angle of loading. The end part of these curves represents the hardening phase with a very high stiffness due to fibre elongation. Notice that the woven fabric material is highly anisotropic and the initial directions of the fibre influence the final results.The agreement between predicted and experimental values is good and proves the validity of the proposed model of pre-impregnated woven fabric behaviour. The numerical model described above clearly shows the strong non linearity of this behaviour law. It takes into account the mechanical characteristics of a viscoelastic resin, the anisotropic behaviour of fabric and the geometrical non linearities due to the high deformability of fibres (straightening and relative rotation).In order to determine and analyse the undulation and interaction effects, a biaxial tensile apparatus  has beenFor a given fabric, the response is strongly influenced by the strain ratio in warp and weft directions due to large fibre undulations. Especially, the non-linearity in warp and weft direction is increasing. The tendency of the fibre to straighten is more impeded when the strain in the perpendicular direction is large see . The biaThe numerical analysis of composite fabric deformation by deep-drawing process is performed using the ABAQUS/EXPLICIT FEM-package ,58. The The evolution of the predicted shear angle variations with and without remeshing procedure is compared in We consider the geometrical draping of the surface corresponding to a complex CAD. The centroid of this piece is chosen as the starting point for which two different fibre orientations: (0°/90°) and (-45°/45°) are specified. The second example concerns the geometrical draping of a car hood. The centroid of the part is chosen as the starting point which the (0°/90°) and (-45°/45°) fibre orientations are specified. In this example, the draping of an initially plane square textile fabric (500 × 500 mm) over a circular or square table is considered. An efficient numerical approach  has been presented to simulate accurately the draping or deep-drawing of composite fabric. This numerical methodology is shown to be very helpful for composite forming process. An adaptive remeshing technique for composite fabric forming process with refinement and coarsening procedures has been proposed. The implementation with continuum triangular and quadrilateral elements and truss element in the ABAQUS code allows us to validate the proposed approach for several types of problems.


The main challenge in analyzing compliant sensor systems is how to calculate the large deformation of flexural complements. Our study proposes a new model that is called the spline pseudo-rigid-body model (spline PRBM). It combines dynamic spline and the pseudo-rigid-body model (PRBM) to simulate the flexural complements. The axial deformations of flexural complements are modeled by using dynamic spline. This makes it possible to consider the nonlinear compliance of the system using four control points. Three rigid rods connected by two revolute (R) pins with two torsion springs replace the three lines connecting the four control points. The kinematic behavior of the system is described using Lagrange equations. Both the optimization and the numerical fitting methods are used for resolving the characteristic parameters of the new model. An example is given of a compliant mechanism to modify the accuracy of the model. The spline PRBM is important in expanding the applications of the PRBM to the design and simulation of flexural force sensors. A flexural force sensor is a mechanism that uses the deformation of its flexible components to gain force signals. For rigid body mechanisms, there are some inevitable expectation, such as high precision, speed, efficiency, and performance, which represent great challenges for sensor systems design. Frictions and clearances are two main factors influencing the accuracy and dynamic performances of moment and force sensors. In addition, the assembly requires an enormous portion of the product expenses. These problems are not easy to solve for traditional rigid body mechanisms. Nevertheless, compliant mechanisms possess many advantages for expense reduction and performance improvement of sensor function, such as fabrication processes, wear, friction, and noise reduction. All of these characteristics make compliant mechanisms promising candidates to be used in force sensor systems ,2,3.The main challenge in analyzing compliant mechanisms is how to simulate the large deflection of flexural components. There have been several efforts ,5,6 to sAs the 1R and 3R PRBM contain pin joints, they can thus function as the bending deformation. When the combined loads are subjected to a flexible beam, it is impossible to calculate the axial deformation by PRBMs with revolute pairs only. In Reference , the latA beam can be considered as a dynamic system with elasticity and mass for which the number of degrees-of-freedom (DOF) is infinite. The number of DOF can be reduced by a few variables in the modeling strategies. Shabana ,22 propoThis paper introduces a new PRBM that possesses a higher accuracy in static and quasi-static analyses compared to those reported in previous studies. This study is structured in six sections. After this introduction, the second section proposes a detailed definition of the spline PRBM. In the third section, the kinematic equations of a compliant beam are deduced through a generic approach. In the fourth, the optimization and numerical fitting methods are applied to resolve the characteristic parameters of the Spline PRBM. In the fifth, a performance comparison among the R, Revolute-Revolute (RR), PR, PRR and spline PRBM with an example in a compliant mechanism is presented. The result reveals the advantages of the novel spline PRBM in simulating flexural beams. In the last section, the conclusions are summarized.Quin  introducConsidering the generic implementations of compliant mechanisms, a simplified modeling with only four control points can be used, thus ensuring simulation accuracy. In addition, the widely used Bezier interpolants will be applied in this study.The parametric expression X-axis. In In Assuming that the axial deflection is neglected, the arc length of the spline is equal to the original length, as represented by the following equation:As shown in Based on Lagrange equations, these coordinates can be used in deducing the equations of the motion of a spline curve:Since the spline curve is continuous, the kinetic energy The elastic energy They can be written as integrals, respectively:All of these strains can be approximated as:All of the elements used to evaluate Equation (4) are included in Equations (6) and (9).The end point’s slope angle in the spline PRBM is Because the slope angle Therefore, the two torsion spring angles Considering the combined loads applied at the end of the spline PRBM, the lateral force and torque can be presented as follow:The torques From Equation (14), we have:Let:And:The resistance to flexibility and deflection can be replaced by the dimensionless torsion spring stiffness coefficient Therefore:It can be obtained from the above equations that:However, as mentioned in There are, in total, five characteristic parameters in the spline PRBM: the characteristic radius factors In this section, this study proposes an effective approach to obtain the spline PRBM’s characteristic parameters. The effective approach uses the following steps. First, the characteristic radius factors An optimization method is used here to obtain the optimal characteristic factors By using the optimization procedure, the three optimal characteristic radius factors can be determined as follows:Since the arc length of the spline should be equal to the original beam length, the effects of revolute angles The characteristic radius factors The spline PRBM is now complete, with all five characteristic parameters determined.In order to test the accuracy of the spline PRBM, in this section this study presents its application to a compliant mechanism. The compliant mechanism is a planar mechanism consist of a crank and a slider in which the slider is pivoted on the rigid crank and attached to the foundation frame, as shown in The effect of twisting deformation can be neglected because the mechanism is planar. Referencing The dimensional variables that should be monitored are the distance Once the dimensional and material variables are given, the spline PRBM can be calculated. The results can be plotted as 20 snapshots. However, the results of R, RR PRBMs deviate from the actual value in the large deflection area. This is because the R, RR models possess low degrees of freedom and have low simulation accuracy. Compared with the R, RR PRBMs, the prismatic pair and compression spring improve the PRR PRBM’s accuracy. Although the PRR PRBM has great simulation accuracy, the spline PRBM’s result is much more accurate in simulation. This result indicates the effectiveness of the spline PRBM method.This study now discusses the simulating errors of all five PRBMs to reveal further advantages of the spline PRBM. The relative angle error between the simulation’s angle and the actual angle is defined as:The relative errors of the five models are shown in Based on the numerical results shown, it can be summarized that the PRR PRBM possesses a higher simulation precision in tracking the actual angle due to the high number of DOFs. When the number of DOFs is the same, a prismatic pair helps the PRR and PR PRBM to perform better than the 2R and 1R PRBM. However, the spline PRBM makes a significantly improvement in simulation.This study proposed a spline PRBM based on dynamic spline and the 2R RPBM. The spline PRBM considers stretching deflection and twisting deflection as well as bending deflection, and because of its stretching, twisting, and bending deflection, the spline PRBM is also suitable for 3D structures with complex shapes. Optimization and numerical fitting methods were utilized to determine all of the spline PRBM’s characteristic parameters. This study showed the superiority of the new spline PRBM when compared with the other PRBMs. The spline PRBM provides a more accurate simulation model in static and quasi-static analyses. In addition, it can be used in dynamic simulations by employing dynamic equations. Therefore, the new spline PRBM has superior application potential in the design and analysis of force sensor systems.


A review of the research activities and achievements at Shenzhen University is conducted in this paper concerning the creation and further development of novel microcapsule based self-resilience systems for their application in concrete structures. After a brief description of pioneering works in the field starting about 10 years ago, the principles raised in the relevant research are examined, where fundamental terms related to the concept of resilience are discussed. Several breakthrough points are highlighted concerning the three adopted comprehensive self-resilience systems, namely physical, chemical and microbial systems. The major challenges regarding evaluation are emphasized and further development concerning self-resilience in concrete structures will be addressed. Being a typical composite material, modern concrete (mostly reinforced by steel) has already been used as a construction material in civil engineering for more than a century, and it is believed to do so for another century because of the widespread availability of its constituents, and its cost-effectiveness, versatility, durability and adaptability. However, there are two major issues that raise serious concern among civil engineers worldwide ,2, i.e.,The first one is related to the performance degradation of a concrete structure under environmental actions. Over the years, there has been a growing understanding that all concrete structures deteriorate over time. Deterioration will change the performance of a concrete structure under various actions. Numerous forms of performance degradation have been observed in concrete structures due to the attack of corrosive ions, gases and other types of aggressive actions in the marine environment. When deterioration occurs, expensive repairs and maintenance have to be undertaken in order to maintain an adequate level of performance and the service life of concrete structures. It has been estimated that the annual cost of repair of concrete structures in Europe is in excess of $20 billion . In ChinDue to the shortage of natural resources, the increase in population and deterioration of environmental conditions, the second issue concerning sustainability emerges. It is often the case that the large-scale demolition of obsolete structures and their replacement with new ones are out of the question either due to economic reasons or a shortage of resources .In recent years, so-called self-healing materials have been exploited in civil engineering, which have the built-in capability to repair structural damage autogenously or with the minimal help of an external stimulus ,6. By apA microcapsule based novel intelligent resilience system for concrete structures has been recently established and further developed at Shenzhen University. Its short history and development will be briefly reviewed in this paper. Starting from the principles of self-resilience, the logical path of development of such a system is examined. Fundamental terms related to the resilience concept and strategy are discussed. Remarkable breakthroughs regarding material systems, design procedures and numerical models are highlighted. The major challenges and further development concerning self-resilience in concrete will be addressed.Since 2008, attempts have been made to introduce a novel microcapsule based self-healing system for concrete at Shenzhen University ,10,11. TThe urea formaldehyde resin was used for the wall of the microcapsule, and bisphenol—an epoxy resin E-51 diluted by n-butyl glycidy ether (BGE)—was adopted as the healing-agent inside the microcapsule. A combination of latent curing agent MC120D and tetraethylene penamine (TEPA)—a type of liquid curing agent functioning at normal temperature was used for curing the healing product. There were basically four stages in forming microcapsules as shown in Results showed that the microcapsule obtained with the adopted production process can be used for the self-healing system in concrete. In the broken specimen of concrete after hardening, it could be clearly seen that the microcapsules remain undamaged during the producing process see c. The trIt was found that the diameter, shell thickness, and surface texture of the microcapsules play major roles regarding the performance of self-healing system ,16,17,18The shell thickness and the surface texture of microcapsules can be controlled by varying core/shell ratio and synthesis temperature, respectively. This is illustrated in On the basis of the fundamental understanding of characteristics of microcapsules and their influences on the performance of a self-healing system, further optimizations and improvements have been made since 2008 at Shenzhen University. One way is to narrow the size distribution in order to improve the efficiency of the physical trigger . BesidesThe healing efficiency with respect to the mechanical and permeability related performance of the adopted self-healing system in concrete was evaluated by experiments ,10,11,12As shown in The two different healing mechanisms were evaluated, namely mechanical performance recovery  and permeability related performance recovery. Details of evaluation can be found in references ,10,11,12From Dafterpreloading/Dhealed) − 1) × 100%, where Dafterpreloading and Dhealed are the diffusion coefficient obtained after the preloading and after healing, respectively. The tendency of recovery shows a linear increase for the adopted parameters, such as the dosage, size of microcapsules and the pre-loading level, whereas the healing temperature demonstrates as strong an effect on the recovery  test. The recovery is calculated as  are classified and it is clear that the degradation of performance of a concrete structure starts at the material level. Anything related to durability should be implemented at this point. Therefore, degradation-related resilience is paramount and forms the foundation for the strategy development of the targeted self-resilience system ,4,21.The principle of resilience consists of two important aspects see a: namelyIt has to be emphasized that the self-resilience system is based on the microcapsule technology. In other words, the container of healing materials is restricted to the microcapsule, however, the type of materials of microcapsules could be essentially different , depending on the targeted recovery performance index.As far as the healing-agent is concerned, there are a large range of options, from organic, bacteria to in-organic materials.With respect to the trigger, not only is the physical trigger  further explored, but the so-called chemical trigger has drawn major attention in our current research. This is in turn related to the resilience principle, namely, healing and recovery at a multi-scale.Physical system Chemical system Microbial system In summary, three relatively independent but strategically integrated approaches have been adopted aiming at the formation of a microcapsule based comprehensive self-resilience system. The targets are to heal the mechanical damage or to recover lost functionality corresponding to impermeability or corrosion protection b:PhysicRelying on several cohesive research projects funded by the Nature Science Foundation of China, several breakthroughs have been made recently at Shenzhen University. They will be briefly highlighted in the following section.A microcapsule based self-recovery system with a chemical trigger (in this case pH sensitive) has firstly been proposed and later on further developed at Shenzhen University ,25,26,27To prepare the microcapsules, sodium monofluorophosphate and microcrystalline cellulose were mixed into Polysorbate 80. Then, the small spherical particles were molded by the extrusion–spheronization method, which is able to control the diameter of a microcapsule. Finally, the spray drying method was adopted to fabricate microcapsules, spraying the liquid PS onto the surface of spherical particles. Three types of microcapsules with different amounts of shell materials were fabricated; the mass percentages of shell materials were 10%, 20% and 43%. For example, 10% refers the percentage of increased weight of microcapsules, according to that of spherical particles molded by the extrusion–spheronization method.The results of studies on the surface damage of microcapsules at different pH values by means of SEM are illustrated in 2+ released from the capsule cores.The release mechanism is further studied after the trigger is excited . To examAccording to the calibration curve, the amount of healing material released from the microcapsules at every designated pH level was calculated, as the pH value was measured. 2PO3F) as the healing material was adopted. The system is triggered by pH value variation. The study focused on examining the healing mechanism of the system for chloride-induced corrosion of rebar in a simulated concrete environment [To evaluate the recovery efficiency, a self-recovery system with sodium monofluorophosphate /calcium hydroxide was used to recover the protection ability against corrosion based on increasing hydroxyl content in concrete . The capA novel idea related to the performance self-recovery was first proposed and patented by Shenzhen University. By means of a chemical trigger (chloride sensitive), a self-resilience system based on the microcapsule technology has been proposed and developed, where the capacity to resist the penetration of harmful agent (chloride ion) into concrete will be restored. As the concentration of this harmful agent reaches a certain level, the microcapsule will be broken and the healing material inside the microcapsule will take action to remove the harmful agent. A remarkable breakthrough was made in 2015 related to this . Various+, Pb2+). When these microcapsules come into contact with chloride ions, the metal ions will be extracted to disintegrate microcapsules (see 2+) to form hydrogel. In our first try, Ag+ is chosen to be coordinated with alginate to form the wall of microcapsules. Silver alginate  can also form a characteristic “egg-box” structure. Each alginate molecular chain can be linked with other chains, forming a three-dimensional gel network  was employed to detect concrete specimen embedded with Ag-alg microcapsules. 3) precipitation, which is a common phenomenon in the natural environment. Certain mineralization bacteria can make use of carbon source to produce CO2 or CO32−, which subsequently reacts with calcium ions to form calcium carbonate precipitate on the surface of concrete cracks, thus sealing the concrete cracks. At the same time, the metabolism of mineralization bacteria creates an alkaline environment, which is further in favor of the process of calcium precipitation. Moreover, bacteria also provide nucleation sites for calcium precipitation. In addition to the ability to induce calcium precipitation, self-healing bacteria should be alkaliphilic and spore-forming due to the harsh environment inside concrete.Self-healing of concrete cracks by bacteria is based on microbial-induced calcium carbonate  of the strains were evaluated , and strThe features of the precipitated products were further studied . As show3, indicating that the crystal is CaCO3. Furthermore, the XRD analysis confirmed that the crystal induced by H4 is calcite [Element composition analysis via energy dispersive spectroscopy (EDS) revealed that the crystal is primarily composed of calcium, carbon and oxygen with a weight ratio closely matching that of CaCO calcite .2+ concentration were also studied [2. Therefore, H4 is a promising bacterium for self-healing of concrete cracks, considering that its CPA can remain at greater than 80% even if the pH reaches 11.0. In addition, it was found that the presence of excessive Ca2+ not only inhibits the bacterial-induced calcium precipitation process but also results in waste of the Ca2+ resource, and maintenance of a Ca2+ concentration lower than 30 mM is a good strategy. Fortunately, due to the poor solubility of calcium hydroxide, the free Ca2+ concentration of the pore solution inside the concrete is normally less than 30 mM [2+ source into concrete for the microbial self-healing process, as used in selected previous studies, might not be necessary from this point of view.Influential factors in the process of bacteria-induced calcium precipitation such as the carbon source, nitrogen source, pH and Ca studied . Experiman 30 mM , making 3 cannot be precipitated deep inside the crack might be because of the shortage of oxygen. It is well known that both water and O2 need to be present inside the crack to activate the bacterial self-healing concrete. While water is sucked into the tiniest microcracks due to capillary suction, the question may arise as to whether adequate oxygen will be available deep inside the cracks. Given that both bacterial spores and nutrients were provided in the self-healing system, lack of oxygen inside the concrete structure might be a major inhibition factor. It was observed that there exists a major drawback in the current microbial self-healing system, i.e., the calcium precipitation seems to only happen on the concrete surface area nearby the crack. The reason why CaCOTo deal with the problem mentioned above, a pioneer attempt at Shenzhen University has been made to develop a strategy to supply oxygen for the microbial calcium precipitation performance . FirstlyThe calcium precipitation of the binary self-healing system was evaluated in bacteria medium solution, and the result was shown in 3 started to form in the tubes where bacteria were incubated. In the beginning stage of calcium precipitation, no difference in the formation of CaCO3 could be found between the tubes with or without oxygen supply. However, for the tubes with oxygen supply, the amount of insoluble Ca2+ increased sharply from 7 days. After 32 days, insoluble Ca2+ reached 27.5 mM  was very low, which might account for the lower calcium precipitation by the bacteria in the absence of oxygen supply  are able to germinate, grow, and induce calcium precipitation effectively, and the stable oxygen supply is important for microbial calcium precipitation. Further study will be related to the longevity issues of the adopted bacteria/ORT system.Generally speaking, natural polymers are biocompatible while synthetic polymers are deleterious in various degrees for microorganisms like bacteria. That is why most of the existing biological microcapsules are walled by natural polymer materials. However, natural polymers are usually hydrophilic, i.e., they take up water and cause the shell of the microcapsule to swell. It is clear that this is not suitable for self-healing systems for concrete. Various attempts were made at Shenzhen University to find harmless and waterproof microcapsules which are suitable for application in microbial self-healing systems ,41,42,43490 are positively correlated with the bacterial concentration. After encapsulation, the fraction of surviving spores is over 90%, indicating the process has a mild effect on spores’ viability  mixed with spores. The shell material was ethyl cellulose (EC) in the first attempt  and lateThe survivability of the encapsulated bacteria was studied, together verifying the protection functionality of the microcapsule. It was found that pure spore has the highest mineralizing activity. Broken microcapsules have higher mineralizing activity than non-broken ones, which indicates that EC microcapsules can effectively protect microorganisms in this system b,c.The trigger mechanism and the working process was further studied by means of the optical microscopy and the high resolution X-ray Computed Tomography (XCT) . The craThe behavior and performance of the microcapsule based self-resilience system have to be evaluated through effective and meaningful ways . Up to now, various techniques have been applied to appraise the obtained system with success to some extent. In addition to the traditional macro test methods  ,46,47,48The whole healing process and healing effect of a microcapsule based self-healing system in concrete was monitored and qualitatively as well as quantitatively evaluated by means of XCT and an image analysis technique. The experimental procedures are illustrated in The crack-healing process and healing effect were evaluated with the help of the reconstruction of inner microstructures  and quantitative analysis. With the help of an image analysis technique, the total volume of air void (cracks and pores) was labeled and calculated. As shown in The evaluation of performance characterization of self-resilience system remains to be a major challenge. Due to the inhomogeneity and randomness, bulk behavior of the concrete after resilience is difficult to experimentally evaluate at a macro level. The only feasible way to do this is to examining the individual behavior of a single unit in the randomly distributed system on a fundamental level. The relevant material properties are then determined experimentally with the help of specific facilitated test set-ups. Afterwards, numerical methods have to be exploited by means of the obtained key parameters at the micro level to simulate the bulk behavior of the resilient system.The behavior of a single microcapsule in concrete was numerically studied at Shenzhen University . The effIt is evident that the key parameters have to be experimentally determined numerically to obtain a realistic picture. To realize this, the following working activities have to be fulfilled: (a) working out feasible and workable micro testing facilities; (b) quantifying the relevant key parameters for various components  quantifying the physical and chemical trigger mechanism; and (d) examining the healing and recovery performance of a single microcapsule system.An attempt in this direction was made recently at Shenzhen University ,52,53. AThe evaluation of a self-resilience system plays a paramount role as far as the successful application of such a system in concrete structures is concerned. Current approaches and methodologies are strongly based on the traditional macro level methods, which make it difficult to appraise newly created systems in an appropriate way. To deal with this challenge, a new RILIEM TC SHE was set up recently (in June 2016), which aims at solve this problem through international cooperation. (i)establish the relationship between the multi-actions and the action effect for concrete;(ii)link resilience requirements with environmental action and the related degradation mechanisms;(iii)incorporate existing self-healing and self-recovery systems to enhance the long-term performance of concrete structures by creating resilience to physical and chemical attack, through the addition of agents within microcapsules, that are able to mitigate the action effects in a range of aggressive environments;(iv)set up facilities and procedure to evaluate self-healing and self-recovery effects at the micro level;(v)quantify key parameters related to fundamental material properties in order to supply the basic input for numerical simulations;(vi)validate the design procedure and numerical model using data from bespoke tests at multi-scales;(vii)demonstrate the self-resilience system by using experimental and numerical data.(viii)realize applications in practice.A comprehensive self-resilience system was developed protecting against attacks in aggressive environments, resulting in concrete structures with durable and sustainable properties that far exceed those of conventional ones. Multi-scale self-healing and self-recovery systems, embedded in concrete, will provide the required resilience. Research at Shenzhen University will focus on the following key issues to address the major challenges related to the development and application of a microcapsule based on self-resilience systems in concrete structures, namely to:


The central nervous system (CNS) is believed to utilize specific predefined modules, called muscle synergies (MS), to accomplish a motor task. Yet questions persist about how the CNS combines these primitives in different ways to suit the task conditions. The MS hypothesis has been a subject of debate as to whether they originate from neural origins or nonneural constraints. In this review article, we present three aspects related to the MS hypothesis: (1) the experimental and computational evidence in support of the existence of MS, (2) algorithmic approaches for extracting them from surface electromyography (EMG) signals, and (3) the possible role of MS as a neurorehabilitation tool. We note that recent advances in computational neuroscience have utilized the MS hypothesis in motor control and learning. Prospective advances in clinical, medical, and engineering sciences and in fields such as robotics and rehabilitation stand to benefit from a more thorough understanding of MS. The nervous system has two parts: 1) the CNS and 2) the peripheral nervous system. Neural signals from the CNS have repeatedly been observed to activate specific muscle groups during the performance of motor tasks, referred to as motor primitive (MP). How the CNS makes this selection from a seemingly vast pool of such primitives to attain a behavioral goal is a complicated question in the field of motor control. The task is computationally challenging, and for over 50 years, researchers since Bernstein have investigated how the CNS reduces the degrees of freedom by focusing on a smaller set of variables [ the CNS  the periThere is an ongoing debate about the origin of MS whether they have a neural origin or a nonneural origin, that is, whether they are encoded in the CNS or activated because of task constraints. With advances in EMG and functional magnetic resonance imaging (fMRI) data analysis, the current view leans towards the organization of MS as spatiotemporal components in the brain and SC, thus supporting their neural origin. We examine the various techniques and algorithms used for the extraction of MS and present a mathematical model of MS. We also review how the central pattern generators (CPGs) in the absence of peripheral feedback trigger the MS. Further, MS as a physiological marker in stroke patients and its future prospects in neuro-rehabilitation, robotics, and sports science are discussed.The presence of MS in motor tasks has been supported in experimental studies for several decades. MS have been associated with the stable force produced by the limbs during natural motor behavior. Maton and Bouisset observed that synergistic groups of muscles produced different external forces during supination or pronation and the external force produced was the sum of the forces of various muscles . FurtherMotor primitives usually develop from a neonatal stage to toddler stage . Some ofMPs are classified into kinematic (stable correlation between joint angles), dynamic (stable correlation between joint torques), and neuronal, based on previous studies , 27. TheWe have already discussed the presence of CFFs or MPs in the spinal circuitry, which gave us an idea that endpoint forces are dependent on coactivation of muscles. The discussion will now focus on how these primitives are organized in the spinal circuitry and how they are combined to produce various movements. Georgopoulos et al. observed that during reaching movements, the cellular discharge was higher in the rhesus macaque motor cortex when the movement was in the desired direction . It was It is interesting that in vertebrates, summation of the motor unit action potentials (MUAPs) is observed using glutamate iontophoresis or electrical stimulation of neurons , 42, whiThe modular organization of MS in the SC is linked to their neural origin , 43, 44.d'Avella et al. extracted MS during different natural behaviors in frogs . Their rThe synergies extracted during postural tasks in humans had a low-dimensional space . That stKutch and Valero-Cuevas, using cadaveric and computational models, showed that the biomechanics of the limbs required change in muscle-tendon length to a low dimensionality in different movements. Each muscle group varied the length change individually leading to the emergence of nonneural coupling between muscles and the presence of dimensionality reduction constraints during isometric force production in different directions . The musCriticism of the synergy hypothesis because of lack of dimensionality could be due to the data processing approach and noise. These factors make it harder to reveal the reduced dimensionality in EMG signals , 69. BizWith these evidences, it would be correct to infer that MS have neural basis. The feedback from the peripheral nervous system assists to acquire MP into the spinal circuitry for a certain task. The stored MP will assist the CNS to adjust biomechanics of the limbs to accomplish the task . In shorRecent research has often employed brain fMRI to study MS. When performing a task, the primary motor cortex (M1) region exhibits changes on a cellular level by a high neuronal discharge rate in a preferred direction , 36, 66.Using transcranial magnetic stimulation (TMS), it was found that the human cortical region is associated with the activation of different synergistic groups of muscles in the lower portion and this was validated with EMG and fMRI recordings . The corThe feedback mechanism is important for movement, as in grasping MS are modulated with respect to the variation in the shape of object among monkeys , 89. In Neural signals are transmitted from the basal ganglia and thalamus to the midbrain locomotor region (MBLR) and to the SC for CPG generation , 76 Fig. ProchazMuscle synergies as a linear combination are usually extracted using matrix factorization algorithms like the independent component analysis (ICA), nonnegative matrix factorization (NNMF), and factor analysis (FA) . The appE is the EMG signal with n sensors (or muscles) and a total of m samples, W is the synergy matrix with reduced dimensionality, s is the number of components or dimensions to be extracted from the EMG, C includes coefficients of neural command vectors and e is the residual error. (a)Time-variant synergies:where N is the number of synergies and E(t) is the activation of muscles at time t. The individual synergy vectors are shifted in amplitude and scaled in time by the activation coefficients [(b)Time-invariant synergies:Equation  gives a ficients . (b)TimThe spatial patterns W are the task-dependent control inputs whereas the activation coefficients C are the task -independent predefined modules .Artificial neural networks (ANN) have been similarly employed to extract MS . The spaPrincipal component analysis sometimes gives negative values in the synergy subspace, which represents the inhibitory response of the spinal circuitry . KrishnaThe dimensionality of control commands  is not exactly the same as the elements of the state space ; therefore, the exact number of synergies needs to be determined prior to extracting them using different computational algorithms . The MS R-squared curve or VAF (variance accounted for) curve gives a more accurate estimate. By definition, VAF and R-squared are similar , but in standard Pearson correlation, the total sum of squared errors for VAF is with respect to zero not the same with that for R-squared which is related to the mean.Bartlett's test, Akaike information criteria, Bayesian information criteria, Laplacian information criteria, and Likelihood ratio test can all be used to identify the correct number of synergies for Gaussian noise-corrupted data but fail to do so for signal-dependent noise. For signal-dependent noise, an ad hoc procedure based on log-likelihood curves may identify the correct number of synergies . For estN synergies (1–10) were extracted from EMG data set with n muscle and m samples  is kept unfixed and allowed to be estimated. The EMG data is reconstructed again by multiplying the newly estimated activation coefficient vector and fixed synergy vector E = W × C. For each synergy point, the data is reconstructed; then, R-squared or VAF curve is plotted against the N number of synergies (1–10) using the expression . For determining the applicable MS from raw unshuffled EMG data,  samples . The algn is the number of muscles and m is the number of samples. There are four main ways to calculate the number of MS based on the VAF curve, which are as follows:Here, x-axis of the graph reduces the mean square error and the curve becomes a straight line. The point at which it attains the plateau is chosen for the correct number of synergies [(1) Best linear fit (BLF) method: Cheung presented that moving along with a greater number of synergies or components on the ynergies .(2) Knee point (KP) method: Cheung et al. presented another method in which the point or the number is chosen where the curve has an increase of smaller than 75% .(3) Elbow method: Tresch et al. suggested the point at which the change in the slope of curves is maximum .(4) Threshold method: Torres-Oviedo et al. used a threshold basis of 0.9 (90%) on the VAF curve to find the number of synergies for extraction .N, N-1, and Nth synergy which are pseudo-randomly shuffled 100 times [This method uses different decoding algorithms , quadratic discriminant analysis (QDA), naive Bayesian (NB) algorithm, and K-NN clustering) and statistical testing. The number of synergies is chosen at a point where there is no statistical significance after adding more synergies to the decoding parameters of the algorithm. The statistical significance is computed between the decoding parameters of 00 times .There is no perfect algorithm for MS estimation because with biomechanical constraints, the accuracy of the algorithms gets reduced . In the Muscle synergy structures based on motor control outputs are beneficial for recognizing alterations in the brain for various motor tasks. Thus, MS prove to be important in the field of human locomotion and neurorehabilitation as motor impairments can be understood by means of the patterns or structures of MS , 106–108We have emphasized the impact of MS with respect to the level of impairment or cortical lesion, but the alteration in MS can also result with time among stroke patients. There are three main stages of stroke categorized on the basis of duration by computed tomography (CT).The time period for the stroke is from 48 hours to several weeks. The upper-limb MS in such stroke is very similar to that in healthy patients, as the previous study also revealed, but the neural drives or activation coefficients displayed alteration because of the presence of a cortical lesion or damage in the cerebral hemisphere . HashiguThe time period is very short which is less than 24–48 hours. Any voluntary movement is less likely.The time period ranges from months to years. In chronic stroke, fractionated and merged MS were observed . In suchThe studies discussed so far show consistency to the Brunnstrom approach that includes stages of motor recovery. The MS count remained static in pain with altered spatiotemporal patterns in the upper limb . In stagBased on the above approach, Brunnstrom and Fugl-Meyer et al. developed the scoring system for the quantitative clinical assessment of five domains for rehabilitation , 117. ThIn patients with a neurological disorder, the imbalance of muscle coordination is compensated by other muscle groups. For example, in chronic stroke, trunk synergies are compensatory to overcome aberrant coupling in the arm for better control , 118. SiFractionated MS are observed in poststroke individuals after several years. It can become the flexible control strategy for movement as synergies are added. The formation of new synergies, which were evident in chronic stroke, can be useful for neurorehabilitation as these synergies were adapted for improved controllability of limbs , 121. A Muscle synergies have been analyzed thus far during normal physical activities, but during peak physical activities, the role of MS with respect to the task is also of great interest. The distribution of relative weight of the muscles in the synergy space helps in understanding the movements. Modulating compensatory synergies that minimizes the chances of injury by specific training is a better solution in sports rehabilitation and performance. Matsunaga et al. analyzed the MS before and after 10 minutes of running . SimilarSome sports like gymnastics require high postural stability. Trunk muscles play a vital role in human postural control , 124. AmShaharudin et al. analyzed a group of untrained individuals performing rowing movements on slide and fixed ergometers . The resIn this section, we will discuss the application of MS in robotics. A low-dimensional controller controls the dynamics of the limbs without losing the performance. A number of studies have utilized the hypothesis of MS to build artificial limbs , 127. BeWe have presented recent research results related to MS in motor control, neurorehabilitation, robotics, and sports science. This review paper concludes that the modular organization of MS in the CNS and their combination lead to a variety of natural motor behaviors. These predefined encoded primitives reduce the dimensionality of the behavior for better task control , 61, 130Temporal synergies are crucial in understanding the neural basis of MS . The casBesides stroke, other neurological disorders like cerebral palsy, dystonia, and spinal injury have been investigated by the hypothesis of MS, which allow flexibility in neurorehabilitation or diagnosis in these and other disorders , 134–138


Deep learning has recently attracted much attention due to its excellent performance in processing audio, image, and video data. However, few studies are devoted to the field of automatic modulation classification (AMC). It is one of the most well-known research topics in communication signal recognition and remains challenging for traditional methods due to complex disturbance from other sources. This paper proposes a heterogeneous deep model fusion (HDMF) method to solve the problem in a unified framework. The contributions include the following: (1) a convolutional neural network (CNN) and long short-term memory (LSTM) are combined by two different ways without prior knowledge involved; (2) a large database, including eleven types of single-carrier modulation signals with various noises as well as a fading channel, is collected with various signal-to-noise ratios (SNRs) based on a real geographical environment; and (3) experimental results demonstrate that HDMF is very capable of coping with the AMC problem, and achieves much better performance when compared with the independent network. Communication signal recognition is of great significance for several daily applications, such as operator regulation, communication anti-jamming, and user identification. One of the main objectives of signal recognition is to detect communication resources, ensuring safe, stable, timely, and reliable data exchange for communications. To achieve this objective, automatic modulation classification (AMC) is indispensable because it can help users identify the modulation mode within operating bands, which benefits communication reconfiguration and electromagnetic environment analysis. Besides this, AMC plays an essential role in obtaining digital baseband information from the signal when only limited knowledge about the parameters is available. Such a technique is widely used in both military and civilian applications, e.g., intelligent cognitive radio and anomaly detection, which have attracted much attention from researchers in the past decades ,3,4,5,6.Existing AMC algorithms can be divided into two main categories , namely,Although researchers have developed various algorithms to implement AMC of digital signals, there are no representative data sets in the field of communication. Meanwhile, these methods are suitable for complex communication equipment and struggle in real-world applications where channels are variable and difficult to predict, because (1) their samples are purely theoretical without the information of real geographical environment; (2) they usually separate feature extraction and the classification process so that information loss is inevitable; and (3) they employ handcrafted features which contribute to the lack of characterization capabilities. In this paper, we propose to realize AMC using convolutional neural networks (CNNs) , long shCNNs exploit spatially local correlation by enforcing a local connectivity pattern between neurons of adjacent layers. The convolution kernels are also shared in each sample for the rapid expansion of parameters caused by the fully connected structure. Sample data are still retained in the original position after convolution such that the local features are well preserved. Despite its great advances in spatial feature extraction, CNNs cannot model the changes in time series well. As is known to us, the temporal property of data is important for AMC applications. As a variant of the recurrent neural network (RNN), LSTM uses the gate structure to realize information transfer in the network in time sequence, which reflects the depth in time series. Therefore, LSTM has a superior capacity to process the time series data.(1)CNNs and LSTM are fused based on the serial and parallel modes to solve the AMC problem, thereby leading to two HDMFs. Both are trained in the end-to-end framework, which can learn features and make classifications in a unified framework.(2)The experimental results show that the performance of the fusion model is significantly improved compared with the independent network and also with traditional wavelet/SVM models. The serial version of HDMF achieves much better performance than the parallel version.(3)We collect communication signal data sets which approximate the transmitted wireless channel in an actual geographical environment. Such datasets are very useful for training networks like CNNs and LSTM.This paper proposes a heterogeneous deep model fusion (HDMF) method to solve the AMC problem in a unified framework. The framework is shown in The rest of this paper is organized as follows: AMC is a typical multiclassification problem in the field of communication. This section briefly introduces several feature extraction and classification methods in the traditional AMC system. The CNN and LSTM models are also presented.Traditionally the features and classifier are separately built for an AMC system. For example, the envelope amplitude of signal, the power spectral variance of signal, and the mean of absolute value signal frequency were extracted in  to descrClassical algorithms have been widely used in the AMC system. Panagiotou et al. considered AMC as a multiple-hypothesis test problem and used decision theory to obtain the results . They asRecently, accompanied with a probabilistic-based output layer, sparse autoencoders based on deep neural networks (DNNs) were introduced for AMC ,20. ThesThe advantage of CNNs is achieved with local connections and tied weights followed by some form of pooling which results in translation-invariant features. Furthermore, another benefit is that they have many fewer parameters than do fully connected networks with the same number of hidden units. In , the autVarious models have been used to process sequential signals, such as hidden semi-Markov models , conditiThe samples in this paper were collected via a realistic process with due consideration for the communication principle and real geographical environment. The received signal in the communication system can be expressed as follows:The digital modulation signal The sampling rate of data is 20 times as much the carrier frequency and 60 times as much as the symbol rate; in other words, a symbol period contains three complete carrier waveforms and a carrier period is made of 20 sample dots. Meanwhile, the carrier frequency scope is broadband, in the frequency range of 20 MHz to 2 GHz.The Longley-Rice model (LR) is an irregular terrain model for radio propagation. We use this method for predicting the attenuation of communication signals for a point-to-point link. LR is proposed for different scenarios and heights of channel antennas in the frequency range of 20 MHz to 20 GHz. This model applies statistics to modify the characterization of the channel, which depends on the variables of each scenario and environment. It determines variation in the signal by the prediction method based on atmospheric changes, topographic profile, and free space. The variations are deformed under actual situation information, such as permittivity, polarization direction, refractive index, weather pattern, and so on, which have deviations that contribute to the attenuation of the signal. The attenuation can be roughly divided into three kinds according to transmission distance as follows:As one of the most common types of noise, AWGN is always true whether or not the signal is in the communication system. The power spectrum density is a constant at all frequencies, and the noise amplitude obeys the Gauss distribution.CNNs are a hierarchical neural network type that contain convolution, activation, and pooling layers. In this study, the input of the CNN model is the data of the signal time domain waveform. The difference among the classes of modulation methods is deeply characterized by the stacking of multiple convolutional layers and nonlinear activation. Different from the CNN models in the image domain, we use a series of one-dimensional convolution kernels to process the signals.Each convolution layer is composed of a number of kernels with the same size. The convolution kernel is common to each sample; thus, each kernel can be called a feature extraction unit. This method of sharing parameters can effectively reduce the number of learning parameters. Moreover, the feature extracted from convolution remains in the original signal position, which preserves the temporal relationship well within the signal. In this paper, rectified linear unit (ReLU) is used as the activation function. We do not use the pooling layer for dimensionality reduction because the amount of signal information is relatively small.Traditional RNNs are unable to connect information as the gap grows. The vanishing gradient can be interpreted as like the process of forgetting in the human brain. LSTM overcomes this drawback using gate structures that optimize the information transfer among memory cells. The particular structures in memory cells include the input, output, and forget gates. An LSTM memory cell is shown in The iterating equations are as follows:Standard LSTM usually models the temporal data in the backward direction but ignores the forward temporal data, which has a positive impact on the results. In this paper, a method based on bidirectional LSTM (Bi-LSTM) is exploited to realize AMC. The core concept is to use a forward and a backward LSTM to train a sample simultaneously. Similarly, the architecture of the Bi-LSTM network is designed to model time domain waveforms from past and future.The HDMFs are established based on the fusion model in serial and parallel ways to enhance the classification performance. The specific structure of the fusion model is shown in The modulated communication signal has local special change features. Meanwhile, the data has temporal features similar to voice and video. The fusion models exploit complementary advantages on the basis of these two features.The six layers of CNNs are used to characterize the differences between the digital modulation modes in the fusion model. The kernel numbers of the convolutional layers are different for each layer. The number of convolutional kernels in the first three layers increases gradually, which transforms single-channel into multichannel signal data. Such a transformation also helps to obtain effective features. Conversely, the number of convolutional kernels in the remaining layers reduces gradually. Finally, the result is restored to single-channel data. Although the data format is the same as the original signal, local features of the signal are extracted by multiple convolution kernels. This leads to the representation for the final classification based on CNNs. The remaining part of the fusion model uses the two-layer Bi-LSTM network to learn the temporal correlation of signals. The output of the upper Bi-LSTM is used as the input for the next layer.The parallel fusion model (HDMF). The two networks are used to train samples simultaneously. The output of each network is then transformed into an 11-dimensional feature vector by the full connection layer. The resulting feature vectors represent the judgment of the modulation modes of the training samples by the two networks. We then combine the two vectors based on the sum operation as:The loss function of the parallel fusion model consists of two parts, which are balanced by the given parameters.In Algorithm 1, we show the optimization of the parallel fusion model.The serial fusion method (HDMF). This is similar to the encoder–decoder framework. In this study, the encoding process is implemented by CNNs; afterwards, LSTM decodes the corresponding information. The features are extracted by the two networks, from simple representation to complex concepts. The upper convolutional layers can extract features locally. Then, the Bi-LSTM layers learn temporal features from these representations.For both kinds of fusion models, the final feature vectors are the probabilistic output of the softmax layer. The fusion models are trained in the end-to-end way even when different neural networks are used to address the AMC problem.Algorithm 1. Training HDMF 1: Initialize the parameters While the loss does not converge, do2: 3: 4: Compute the total loss by 5: Compute the backpropagation error 6: Update parameter 7: Update parameters 8: Update parameter End while9: The geographic simulation environment is shown in Detailed descriptions of the datasets are shown in Algorithm 2. Communication signal generation1: Open the real geographic environment through the control in Visual Studio.2: Real-time track transmission and simulation of unmanned aerial vehicle (UAV) flight.3: Add the latitude and longitude coordinates of the radiation and the height of the antenna.4: Build an LR channel model based on the parameters of coordinate, climate, and terrain, etc.5: Generation of baseband signals randomly and in order to generate various modulation signals by MATLAB.6: The communication between Visual Studio and MATLAB is by means of a User Datagram Protocol (UDP), and the real sample data is generated and finally stored.We used TensorFlow  to impleThe fundamental loss and the softmax functions are defined as follows:pagation  is calcuUsing CNNs and LSTM to solve the AMC problem, the classification accuracies of CNNs are here reported for varying convolution layer depth from 1 to 4, number of convolution kernels from 8 to 64, and size of convolution kernels from 10 to 40. The classification accuracies of Bi-LSTM were tested with varying layer depth from 1 to 3 and number of memory cells from 16 to 128. The Bi-LSTM used in the fusion model contained two layers. The number of convolution layers was 6. The number of convolution kernels in the first three layers was 8, 16, and 32, and the size of the convolution kernel was 10. The number of convolution kernels in the remaining layers was 16, 8, and 1, and the size of the convolution kernel was 20. The Bi-LSTM model consisted of two layers with 128 memory cells.For SNR from 0 dB to 20 dB, the classification accuracy of CNN and Bi-LSTM models is shown in The training parameters and computational complexity of CNNs are shown in We have compared five methods, including both traditional and deep learning methods, based on the same data sets. The classification performance is as follows.The modified classifiers are established based on the fusion model in serial and parallel modes to increase the classification accuracy. As a result, we compare the classification accuracy of the methods on the basis of deep learning with the traditional method using wavelet and SVM classifiers. The results are shown in The performances of the classifiers show that deep learning achieves high classification accuracy for AMC. Waveform local variation and temporal features can be used to identify modulation modes. In comparison with CNN and Bi-LSTM, the performance of the HDMF methods is improved significantly because the classifiers can recognize the two features simultaneously. However, the performance of the serial fusion is considerably higher than that of the parallel fusion because the parallel method belongs to decision-level fusion. The fusion can be viewed as a simple voting process for results. The serial method belongs to feature-level fusion, which combines the feature information to obtain the classification results.In this study, the modulation mode of the samples includes two forms, namely, within-class and between-class modes. The probability matrices show the identification results of the modulation modes by the serial fusion model when the SNR is 20, 10, and 0 dB, respectively; the results are shown in In this study, we proposed methods on the basis of deep learning to address the AMC problem in the field of communication. The classification methods are based on the end-to-end process, which performs feature extraction and classification in a unified framework, unlike the traditional methods. First, the communication signal dataset system was developed based on an actual geographical environment to provide the basis for related classification tasks. CNNs and LSTM were then used to solve the AMC problem. The models are capable of obtaining high-performing filters which significantly improve the capacity for feature representation for AMC. Furthermore, the modified classifiers based on the fusion model in serial and parallel modes are of great benefit to improving classification accuracy when the SNR is from 0 dB to 20 dB. The proposed methods in this paper achieve a much lower computational complexity during testing when compared with the training process. The serial fusion mode has the best performance compared with other modes. The probability matrices significantly reflect the shortcomings of the classifiers in this study. We will overcome these shortcomings with further research on AMC in the future ,42.