The difference between the shear performances of Perfobond Leiste (PBL) shear connectors embedded in steel fiber-reinforced cementitious composite (SFRCC) structure and normal strength concrete (NC) structure was investigated by push-out tests and finite element (FE) simulations. Push-out tests were carried out on nine steel-SFRCC specimens and nine steel-NC specimens. The mechanical behavior of the PBL shear connector was examined according to the failure modes, load-slip curves, and strain distribution laws of the push-out specimens. Experimental results revealed that the extension of cracks in SFRCC was hindered by steel fibers, and the number and width of cracks in SFRCC were smaller than those in NC. The failure mode of the steel-SFRCC specimens and the single-hole steel-NC specimens was the shear failure of the penetrating reinforcement, whereas that of the multi-hole NC specimens was concrete slab cracking. The ultimate shear bearing capacity of PBL shear connectors in the steel-SFRCC specimens was 47.8% greater than that in the steel-NC specimens. Furthermore, an FE model verified by the test results was established to conduct parametric analyses. It was found that the hole diameter and thickness of the steel plate and the yield strength of the penetrating rebar greatly affected the shear bearing capacity of PBL shear connectors. Finally, based on the experimental and FE simulation results, an expression for calculating the ultimate shear bearing capacity of PBL shear connectors in the steel-SFRCC composite structure was developed by considering the bearing effects of concrete dowels, penetrating rebars, and end parts. In modern bridge constructions, steel–concrete composite structures have been developed based on the reliability and economy of steel and concrete. Generally, steel girders and concrete slabs are connected by shear connectors. Under an applied load, concrete slabs become compressed and steel beams become elongated ,2,3. In PBL shear connectors are not single force-bearing components; thus, their stress mechanism is complicated. Numerical simulations and experimental investigations on the material properties and component sizes of PBL connectors have been conducted. Candido et al.  performeSteel–NC composite beams with PBL connectors have different degrees of concrete voids and cracking, which yield adverse effects on structural stress and durability ,20. HighSFRCC is a multiphase heterogeneous composite material consisting of fine aggregates, coarse aggregates, cement, and randomly distributed steel fibers. Steel fibers can refine the microstructure of concrete and reduce internal defects. Tiberti et al.  exploredTo investigate the failure mechanism of PBL shear connectors in SFRCC and determine the calculation formula of shear bearing capacity, nine steel–SFRCC composite structures and nine steel–NC composite structures with PBL shear connectors were designed in the present work. The effects of penetrating rebar diameter, the number of steel plate openings, and concrete type on the ultimate shear bearing capacity, shear stiffness, failure modes, load-slip curves, and load–strain curves of the specimens were discussed, and the main differences between the stress characteristics of the steel–SFRCC and steel–NC structures were revealed. Moreover, the effects of SFRCC strength, steel plate opening diameter and thickness, and penetrating reinforcement yield strength on the ultimate shear capacity of PBL shear keys were systematically analyzed by a FE model (verified by test results). Finally, an expression for calculating the ultimate shear bearing capacities of PBL shear connectors in the steel–SFRCC composite structure was developed.According to Eurocode 4 , nine stThe fabrication process of a test specimen is displayed in 3 cubic specimens and 150 × 150 × 550 mm3 prism specimens were fabricated to test the compressive and tensile strengths of concretes. The tensile strength of HRB400 steel bars was measured with a universal testing machine produced by Hailian , .  China),  and the The push-out test device produced by Hailian used in this experiment is displayed in The typical failure modes of the steel–NC and steel–SFRCC structures are displayed in The failure modes of the SFRCC specimens were different from those of the NC specimens. The failure of the single-hole steel–NC specimen happened due to the bending shearing of the penetrating reinforcement, whereas the multi-hole steel–NC specimen fractured because of the cracking of the concrete slabs. In contrast, the failure of the SFRCC specimens could be attributed to the bending shearing of the penetrating reinforcement. During the test, the shear force was transmitted between the PBL plates and the concretes, generating local stress in the composite structure. The local concrete was subjected to large pressure and splitting force. As SFRCC has high strength and can bear large loads and the bridging effect of steel fibers can effectively limit the expansion of cracks, the failure of PBL connectors in steel–SFRCC occurred due to the bending shearing of the penetrating reinforcement. Due to the insufficient strength of NC, the concrete at the bottom of the PBL steel plate was crushed by excessive local pressure during the test, generating a large number of cracks in the concrete; thus, the concrete plate cracked and could not create effective constraints on PBL shear connectors. Therefore, shear connectors with high bearing capacity should be used to achieve the high-strength performance of SFRCC.The load–slip curves of the push-out test specimens are exhibited in When the failure occurred, the ultimate slip amount of the steel–SFRCC composite structure was larger than that of the steel–NC composite structure. This happened because due to the presence of steel fibers in the steel–SFRCC composite structure, cracks were not developed rapidly after their appearance; thus, the structure continued to bear the load. However, the failure of the steel–NC composite structure was faster due to the second-order effect of cracks.Eurocode 4  stipulatThe secant slope corresponding to the interfacial slippage of 0.5 mm was taken as the shear stiffness value (K) . The calTaking the three-hole specimen as an example, the load–strain curves of the concrete slabs in the steel–SFRCC and steel–NC composite structures were plotted . In the The load–strain curves of H-shaped steel beams in the steel–SFRCC and steel–NC composite structures are displayed in Strain gauges in the penetrating reinforcement were arranged along the axial direction of the reinforcement to monitor the stress state when the penetrating reinforcement was subjected to axial deformation or bending deformation. It is observable from When PBL shear connectors were stressed, due to the low strength of concrete, concrete dowels were easily crushed and plastic flow occurred in the core stress area of the connectors; thus, the penetrating reinforcement was squeezed to the edge of the perforated steel plates and came in contact with the steel plate. When the penetration reinforcement diameter was small, it was cut off by the perforated steel plates. When the perforated steel plate was thin, it was squeezed and deformed by the penetrating reinforcement. The strain values at steel plate openings in the two types of composite structures are presented in The FE model was built by ABAQUS 6.14-4 and a half model was developed according to the geometrical symmetry of the push-out test specimens. The H-shaped steel beam, the concrete, the penetrating reinforcement, and the perforated steel plate were simulated by the solid element, whereas the truss element was applied to simulate the embedded reinforcement. The three-line constitutive model proposed by Shi  was adopTo ensure that the model did not have sharp and deformed meshes during mesh generation, its different parts were cut several times and local seeds were laid out before mesh generation. Moreover, the irregular joint parts were divided into geometric components of regular shapes; especially, densified seeds were distributed around circular holes. The concrete dowels and the concrete were simulated by a “tie” contact. The perforated steel plates and the H-shaped steel plates were welded and considered a whole without any contact. The surface-to-surface contact was adopted between the penetrating reinforcement and the concrete dowels, hard contact was used in the normal direction, and a penalty function with a coefficient of 0.5 was adopted in the tangent direction. Hard contact was applied between the PBL shear connectors and the concrete in the normal direction, and a penalty function with a coefficient of 0.1 was used in the tangent direction. Hard contact was also used between the perforated steel plates and the concrete dowels in the normal direction, and a penalty function with a coefficient of 0.904 was adopted in the tangent direction. Embedded restraints were introduced between the stirrups and the concrete. A symmetrical constraint was utilized to the center of the steel beam and a vertical constraint with six degrees of freedom was used for the concrete base plate. In order to facilitate load application and result extraction, a reference point RP1 was established at the top of the specimen, a coupling constraint was adopted between RP1 and the top surface of the model, and displacement loads were applied at RP1 according to a smoothed magnitude function .A comparison between the experimental and simulated load–slip curves is presented in The parametric analysis was conducted based on the control variable method and the influences of different parameters on the shear bearing capacities of PBL shear connectors were examined by the FE model. Four different penetrating reinforcement diameters  were adopted for the parametric analyses .(a)Concrete strength: The ultimate shear capacity of the PBL connector increased with the increase in concrete compressive strength; however, the increment range gradually became smaller. When the concrete compressive strength increased from 80 MPa to 140 MPa by 20 MPa increments, the ultimate shear capacities of PBL connectors increased by approximately 18.84%, 14.36%, and 12.32%. High-strength concrete enhances the shear capacities of concrete dowels and limits the extension of cracks; however, when the concrete strength increases to a certain value, the shear bearing capacities of connectors are determined by other parameters such as the penetrating rebar and steel plate.(b)Yield strength of the penetrating reinforcement: When the yield strength of the penetrating reinforcement strength increased from 335 MPa to 400 MPa, the shear capacity of PBL connectors increased by approximately 4.96%. When the reinforcement strength increased from 400 MPa to 500 MPa, the shear capacity of PBL connectors increased by approximately 9.25%. When the area of the penetrating rebar was fixed, the strength increment of the penetrating steel bar improved the ultimate shear capacities of PBL connectors after the failure of concrete dowels; thereby, the overall shear capacity of the structure was enhanced.(c)Steel plate hole diameter: The shear capacity of PBL connectors increased with the increase in the steel plate opening diameter; however, the increment was small (within 5%). The shear capacity of PBL connector was improved to a certain extent because the increase in the PBL opening diameter increased the shear resistance area of concrete dowels.(d)Steel plate thickness: The shear capacities of the end-bearing PBL connectors increased with the increase in the perforated steel plate thickness. When the steel plate thickness increased from 10 mm to 16 mm by 2 mm increments, the shear capacities of PBL connectors increased by approximately 6.15%, 8.32%, and 2.79%. The steel plate thickness affected the compressive area of the concrete dowels and the penetrating reinforcement and directly bore the compressive force induced by the concrete under the perforated steel plate.Numerous formulas have been established to calculate the ultimate shear capacities of PBL connectors in steel–NC and steel–UHPC composite structures. The shear capacities of the PBL shear keys were mainly composed of three parts: the action of penetrating reinforcement The shear bearing capacity, ductility, and shear stiffness of PBL shear connectors in SFRCC were better than those in NC. The ultimate shear bearing capacities of single-hole PBL shear connectors mainly depended on the penetrating reinforcement diameter, whereas that of multi-hole PBL shear connectors depended on both the penetrating reinforcement diameter and the concrete strength.(2)The failure mode of the single-hole steel–NC specimen was the shear failure of the penetrating reinforcement, and that of the multi-hole steel–NC specimen was concrete cracking. Penetrating rebar shearing and yielding were the main failure modes of the steel–SFRCC specimens, and their steel plate opening failure characteristics were prominent. In comparison with the steel–NC specimens, the steel–SFRCC specimens had slower crack development, smaller crack number and width, and better concrete integrity. However, the single-hole and multi-hole specimens had the problems of excessive concrete strength and the detachment of the perforated steel plates, respectively.(3)The shear bearing capacity of PBL shear connectors was affected by various factors, such as SFRCC strength, steel plate opening diameter and thickness, and penetrating reinforcement yield strength. Among them, SFRCC strength and penetrating reinforcement yield strength were the main factors, whereas steel plate opening diameter and thickness were secondary factors. Considering the contribution of various factors, an expression for calculating the shear bearing capacities of the PBL shear connectors in the steel–SFRCC composite structure was proposed and had higher precision.


Cleaning is an important task that is practiced in every domain and has prime importance. The significance of cleaning has led to several newfangled technologies in the domestic and professional cleaning domain. However, strategies for auditing the cleanliness delivered by the various cleaning methods remain manual and often ignored. This work presents a novel domestic dirt image dataset for cleaning auditing application including AI-based dirt analysis and robot-assisted cleaning inspection. One of the significant challenges in an AI-based robot-aided cleaning auditing is the absence of a comprehensive dataset for dirt analysis. We bridge this gap by identifying nine classes of commonly occurring domestic dirt and a labeled dataset consisting of 3000 microscope dirt images curated from a semi-indoor environment. The dirt dataset gathered using the adhesive dirt lifting method can enhance the current dirt sensing and dirt composition estimation for cleaning auditing. The dataset’s quality is analyzed by AI-based dirt analysis and a robot-aided cleaning auditing task using six standard classification models. The models trained with the dirt dataset were capable of yielding a classification accuracy above 90% in the offline dirt analysis experiment and 82% in real-time test results. Cleaning is an inevitable routine associated with every domain. According to the recent market studies, the professional cleaning industry is steeply growing and expected to reach a market size of USD 88.9 billion by 2025 ,2. The gEven though there are proven AI models that could yield superior accuracy, there is no comprehensive dataset that the experts can use to train the AI models for dirt analysis.The dirt particles are often detected by typical computer vision-based analysis. However, capturing the finer features of the dirt is essential for AI-based analysis.The domestic dirt has identical visual features, which makes classification challenging for the AI models, which demands a high-quality dataset highlighting the visually distinct features of the dirt particles.Dirt analysis is one of the critical elements for cleanliness auditing. The major challenges in the field of dirt analysis targeting cleaning auditing include the following:Gathering of magnified images of domestic dirt particles using adhesive dirt lifting;Analyze the usability of an acquired dataset in training and classifying the domestic dirt in standard classification models, using a cross-validation technique;Analyze the performance of the proposed scheme in a real-time scenario by rolling out the trained classification model for real-time dirt composition estimation for an in-house developed audit robot.This research work presents a novel domestic dirt image dataset for cleaning auditing application including AI-based dirt analysis and robot-assisted cleaning inspection. Unlike the conventional vision-based dirt detection algorithms, whose application is limited to cleaning robots, the proposed method presents a dirt detection and classification strategy for cleaning auditing, where an audit sensor gathers the dirt samples via adhesive dirt lifting. As part of this work, a comprehensive dataset consisting of microscope images of commonly occurring domestic dirt is acquired using the sample audit sensor. To distinguish the features, each dirt image is analyzed in under The rest of the article is structured as follows. Despite the importance, cleaning auditing is analyzed in very few domains. For example, the formation of tests for cleaning quality analysis in the fish processing industry is reported in . Lewis eThis work adopts our in-house developed cleaning auditing robot (BELUGA) for dirt dataset collection. In order to classify the domestic dirt, it is essential to build a comprehensive dataset of domestic dirt. The domestic dirt is referred to as particulate contaminates  that are carried by the airflow and settled down in undisturbed air. The samples are collected from indoor and semi-indoor regions by attaching the sensors to the BELUGA robot. The overview of the sample collection procedures and method for dirt inference is depicted in The audit robot is the carrier of a sample audit sensor, and it helps perform the dirt sample gathering from a vast area of space where the manual sample gathering is not feasible. The audit robot called BELUGA is an in-house developed autonomous cleaning audit robot integrated with the sample audit sensor. The The locomotion unit of the robot is composed of a pair of brushless DC (BLDC) motors that form a differential drive wheel configuration. The third point of contact of the robot is made using a free rotating castor wheel. The BLDC motor drivers established a closed-loop velocity control for the driving wheels. The velocity feedback is obtained from the BLDC motor drivers using the incremental encoders associated with the motor. The velocity feedback from the motors is used for computing the odometry information. The communication with the drive motors is established using MODBUS-RTU communication. The robot uses a 24 V DC lithium-ion phosphate battery to power all subsystems. The main supply from the battery is regulated and distributed further to power-sensitive components. A 2D LiDAR is the primary perception sensor associated with the robot, which is supported by a depth camera to add 3D perception capabilities and detect obstacles below the level of LiDAR. The LiDAR used in the robot is SICK TIM 581 with a range of 20 m in semi-outdoor conditions. The Intel Real-sense D435i is the depth camera with a resolution of The robot performs autonomous navigation in real time using the input from the perception sensors and odometry information. The robot possesses an embedded computer with an Intel core i7 processor and runs with an operating system of Ubuntu 20.04. The perception and navigation algorithms run alongside the ROS middleware in the embedded computer. Apart from the navigation sensors, the robot is integrated with the sample audit sensor. The communication between the sample audit sensor and the embedded PC is established through USB and RS485 communication. The USB established a communication link with the digital microscope, and RS485 is used to control the servo motors’ actuation inside the sensor. On the BELUGA robot, the sensor is attached to a removable sensor bay, which allows detaching the sensor from the robot.The major components associated with the sample audit sensor are the adhesive tape, winding motor, pressing motor, pressing ramp, and a digital microscope. The gathering of the dirt samples is done by the synchronized motion of winding motors and pressing motors. The winding motors and pressing motors are digital servos that can be position-controlled according to the data passed over the RS485 communication protocol. The winding motor of the sensor rotates an adhesive tape, and the pressing motor actuates the pressing ram that exerts downward force to press and release the sticking surface of the tape against the surface. The winding motor displaces the tape surface toward the field of view of the digital microscope to capture the magnified image of the surface of the tape. The pressing of the adhesive tape is completed for a 2 cm × 2 cm area; hence, analysis of the dirt is localized to the same surface area. Every sampling completed by the BELUGA robot takes 13 to 16 s, which includes 4 s to lower the sensor bay to the floor, 2 s to conduct a pre-sampling winding, 4 s for stamping action, and 3 to 6 s to perform a post-sampling winding. The post-sampling winding varies from 3 to 6 s since the length to displace varies as the adhesive tape radius decreases linearly as more and more samples are taken. The movement of the adhesive tape is guided through silicon idlers so that the adhesive tape will not become stuck while displacing the stamped area to the field of view of the microscope. This section describes the experimental and analysis procedure of dirt dataset collection and evaluation methods. The experiment has been performed in four phases. The first phase involves dataset curation. The second phase validates the collected dirt dataset through a k-fold cross-validation scheme and converging time of the various image classification algorithm. The third phase involves validation of the dirt dataset through the AI-based dirt classifier algorithm. The final phase involves validating the trained dirt classification models with the BELUGA robot on a real-time field trail.The BELUGA robot is set to exploration mode, where it explores the region using the frontier exploration method, and the sample gathering is completed every 10 s. Frequent occurrence during sample collection;Presence in every location;The health and environmental factors associated with identified dirt particles ,25,26.The sample collection procedure is repeated for 2 days in each location, and the composition of dirt is analyzed. The main dust particles lifted by the adhesive tape include ashes, hair , tiny bits of paper, sand, soil, etc. The bits of paper are collected from all the six locations taken for the data collection. The traces of sand and soil particles were also captured from all locations—however, the sand and soil particle concentration was higher in the walkways and corridors. The traces of food particles were also identified from every location; however, the most frequent occurrence of food particles in the collected samples was from the food court and pantry. Paint particles are captured in the sample collection completed in warehouses often, and it is observed that more traces are captured when the robot completes sampling near the corners and walls of the warehouse. Traces of seeds, lint, etc. were also identified from the collected samples across all locations. From the observations made from the dirt sample collection, the specified classes for the dirt data includes ash, hair, sand, soil, paper, paint, food, and fiber. These classes are finalized for the dirt dataset considering the following:The identified dirt classes for the dataset are provided in the Upon identifying the distinct dirt classes, the image captured by the microscope at a resolution of The quality of the dataset is analyzed by inspecting the accuracy of the state-of-the-art AI-based image classification models trained using it. The quality of the collected dirt dataset was evaluated through training accuracy with a k-fold cross-validation scheme and statistical measure. For analysis, we choose two sets of classification models. The first set is composed of three less dense neural network architectures, and the second set is composed of three dense-layer neural networks. For the less dense models, we took VGG-11 ,28, VGG-Select the fold k groups, which are also known as folds.Split the dataset to Select For every iteration, a new model is trained independent of the previous iteration.k times; in every iteration, the remaining fold will serve as the test set.Repeat the training and cross-validation The accuracy is determined on the The k-fold cross-validation is completed by following the procedures given below:The six-image classifier model was trained with an early stopping condition to avoid the over-fitting of the model. The models are trained in NVIDIA GeForce 3080 GPU with a batch size of 32 and a learning rate of ard pass . CompariThe dirt dataset’s efficiency was validated through a statistical measure function. Here, the models trained using our dirt dataset classification accuracy were chosen as the evaluation matrix to assess the dirt dataset quality. Accuracy Equation , precisiEquation  were useA set of 500 images from each class is used to compute the confusion matrix parameter (The offline test results show that all the six classification frameworks show an accuracy above In addition to the offline test, the usability of the curated dataset is analyzed in a real-time cleaning inspection use case with the BELUGA robot. We have chosen an environment for testing which is similar to the chosen regions for data collection. DarkNet53 was used for rolling out the real-time inference considering the best performance in k-fold cross-validation and offline testing. Since the BELUGA robot’s embedded computer is devoid of GPU, the inference is completed by establishing a communication with a remote server running with GPU. Using the BELUGA robot, the five dirt samples are collected each from the food court, walkway and indoor office space. For every sample image captured by the digital microscope after dust lifting, the sample image is divided into 16 images, matching the training dataset. The models loaded with weights were trained, and the classification of dirt is completed for every 16 images from the collected sample. Out of the 15 sample images, which were divided into 240 test image samples, 203 samples reported the right classification with an admissible accuracy of Overlapping of multiple specks of dirt classes in a sample image shown in e;The shaking of adhesive tape during the actuation of the sensor may result in blur images that eventually lead to a wrong classification;Encountered dirt specks with very close visual resemblance make it indistinguishable for the model to classify.The experiments results conducted showed the prepared dataset’s usability on popular deep learning models such as VGG-16, VGG-11, MobileNet V2, ResNet50, ResNet101, and Darknet53. Upon rolling out the trained with the prepared dataset for real-time inference, an admissible accuracy was observed. During the course of our dataset curation, certain limitations identified include the narrow field of view of the camera and the overlapping of multiple dirt classes in the sample image. Moreover, certain dirt particles share similar textures, making them difficult to be distinguished. Although the data inference is substantially faster, the dirt data collection is found to be slow, since it involves adhesive dirt lifting. Unlike the offline test results, false-positive occurrences are reported in the real-time test results shown in d, which Combining microbial and chemical analysis in the process of sample auditing;Incorporating novel autonomous algorithms toward dirt exploration;A comprehensive study comparing the different algorithms with respect to cleaning auditing;Exploring the usability of the current dataset for instance segmentation of dirt particles;Improving the current dataset by expanding the number of dirt classes and open-sourcing the dataset.A dirt image dataset was proposed for AI-based dirt classification for automated cleaning auditing. Our in-house developed cleaning inspection robot BELUGA was used to gather the dirt sample images from a semi-indoor environment. We identified nine visually distinct dirt classes, and 3000


Wireless sensors are the basic requisite of today’s smart infrastructure based on internet of things (IoTs), 5G and wireless sensor networks (WSNs). WSNs are widely used in industrial applications, precision agriculture and animal tracking systems, environment monitoring, smart grids, energy control systems, smart buildings and entertainment industry etc. The distributed and dynamic scheme of WSNs establishes very unique demands in developing clustering and routing protocols. In order to meet the demand of efficient WSNs, most important requirement is energy management and extension of network lifetime. So energy constraints issue is one of the most emerging area for research to reduce the complexity of network functioning. Due to the complexity of this task we need more robustness optimizer algorithms which can tackle these types of tasks. In this article we are trying to develop one improved version of chimp optimizer for energy constraint issues. In this modification have been integrated the chimp optimizer with dimension learning based hunting (DLH) search technique, known as Improved Chimp Optimizer Algorithm (IChoA). Here the DLH search strategy helps in maintaining diversity and improves the balance between exploitation and exploration. To compute the robustness in solving the optimizer issues, IChoA has been tested on 29-CEC-2017 test suites and energy constraint issues. Experimental solutions obtained by proposed methods are verified with recent methods. All simulation shows that the IChoA method can be most effective in solving the standard complex suites and energy constraint issues. WSN is equipped with self-supported battery power and has limited computation power, battery life and memory resources, so maximizing the network lifetime, energy conservation measures are essential for enhancing the performance of these networks. WSN has the ability to sense, process and communicate. The most challenging concern in WSN design is how to save node energy while maintaining the desirable network performance2. Any WSN can only meet its mission as long as it is considered alive and hence the lifetime is an important parameter for the efficient working of WSN and can be extended by jointly applying various energy efficient techniques. Clustering is one of the approaches to enhance the network lifetime by introducing energy balancing and data dissemination amongst the sensor nodes (SNs)3. For clustered networks, data collected by each node within the cluster is forwarded to cluster head (CH) and then the data aggregated by CH is transmitted to base station (BS) over the long distances and most of the energy is drained during this process by cluster heads (CHs) as compared to other sensors. Its mandatory to avoid this fast energy depletion of CH. The optimal choice of CHs, their reconfiguration in different rounds and maintaining cluster are the main issues to be considered during development of the clustering algorithms4.Recently, WSN has increased attention among users and researchers as a powerful technological platform with tremendous applications. It has become a significant technology for understanding many applications including both simple phenomena monitoring applications and heavy duty data streaming applications such as military operations, environment monitoring and surveillance systems5, Mean Shift Clustering Algorithm (MEAN-SHIFT)6, Threshold-Sensitive Energy Efficient Sensor Network (TEEN)7, a Hybrid Energy-Efficient Distributed Clustering Approach (HEED)8, K-Mean Clustering Algorithm (K-MEAN)9, Linked Clustering Algorithm (LCA)10, Energy-Efficient Clustering Scheme (EECS)11, Hierarchical Clustering Algorithm (HC)12, High Energy First Algorithm (HEF)13, Energy Residue Aware (ERA)14, Fuzzy C Mean Clustering Algorithm (FCM)15, and many more. All methods above provide the improvement in power utilization in communication and lengthen the network lifetime in one way or the other depending upon the scenario of WSN. Also, clustering enhances the stability and ensures the reliability of the network. these are traditional approaches for WSN and further can be enhanced by using optimization algorithms.Another challenge is optimization of the energy consumption which can make the wireless communication system perform better. So, conservation and optimization of energy is the recent research area still need to explore for better performance evaluation of WSNs. There are many existing algorithms or protocols aiming at improving the performance of sensor networks, such as Low-Energy Adaptive Clustering Hierarchy (LEACH)16 and the solution is directly associated to the optimization. This is not possible to replace batteries in remote areas due to hostile geographical locations.So, battery life in sensor node is a crucial concern to be addressed. With a very little infrastructure for WSN, the deployment of the sensor nodes is random and the base station is either inside the monitoring area or very near to it17. There are several optimization algorithms to suit the different problems and choosing a proper algorithm is very important.The use of Optimization algorithm is to obtain the optimum or best result under a given circumstances. The word optimum is taken as maximum or minimum depending on circumstances. The sensor network protocols have to focus primarily on saving the power for lengthening network life and other issues are, attaining high quality QoS, low bandwidth, processing speed and limited storage in each node18. While the proper and precise solution may be provided by the accurate algorithm for calculating global optimum value and time for execution of numerous variables is increased exponentially. On the other hand stochastic techniques are capable of finding the nearest optimal solution within stimulated period of time. They can be classified as heuristic and metaheuristic algorithms. The metaheuristic algorithms can be further divided into two groups such as nature inspired algorithms and non nature inspired algorithms. Numerous metaheuristic algorithms are nature inspired such as Particle Swarm optimization (PSO), Artificial Bee Colony (ABC), Genetic Algorithm (GA), Ant colony optimization (ACO) and many more where as a few are non nature inspired such as tabu search, adaptive dimension search and iterative local search etc.19.Most of the WSN problems have many difficulties consisting of large space solutions, non linear limits, non-convex search landscape, energetic / strident objective functions and maximum computational cost. These can be solved by utilizing the optimal solutions for the variable result to minimize or maximize an objective function irrespective of varying limit values20.Some more nature inspired optimization algorithms incorporating evolutionary model and swarm Intelligence are used for WSN are Firefly Algorithm (FA), BEES optimization Algorithm (BOA), Particle Swarm Frog leaping hybrid optimization algorithm, Elephant swarm optimization, Cuckoo search (CS), and Bat Algorithm (BA)An improved algorithm namely IChoA that includes features from ChoA and DLH search strategy is proposed.IChoA is presented for solving 29-CEC-2017 test functions.The proposed algorithm is presented for deal with energy constraint issues.IChoA based clustering save energy spending the density criterion and residual energy of nodes.IChoA based routing achieves data sending using intra-cluster multipath communications.Statistical and qualitative experimental analyses assess the effectiveness of the ICHOA method compared to recent algorithms.This paper introduces an improved version that integrates the phases of Chimp Optimizer and dimension learning based hunting (DLH) search strategy. In this modification the DLH phase plays a role for maintaining diversity and also improves the balance between exploitation and exploration. Numerical and statistical experiments have been done for comparing the performance of the metaheuristics. To sum up, the major contributions of this research work are:The rest of the paper is organized as follows: In “The life span of the battery of each sensor device is limited which severely constrains the operation of the sensor network. Therefore, proper schemes must be proposed to tackle this problem so that the exploitation of sensor networks can become practical. The key point to develop a clustering algorithm is distribution strategy of nodes, cluster formation method, data dissemination process, communication protection, stability, synchronization etc. The exhaustive literature is reviewed in this section as follows:21 designed concentric layered architecture (CLA) for optimal location of layers and numbers of SN in every layer in static and random WSN multi level clustering. CLA divided the entire area into layers depending upon nodes in every layer and node density. CLA divided the WSN into concentric layers for numerous SNs so that the outer later consisted of two times compared to internal layers to avoid intra cluster load. CLA evaluated for PSO-C and simulation results showed that it performed appreciably in terms of energy efficiency and prolonged lifetime compared to PSO-ECHS, BERA, and PSO-C.Singh et al.22 invented an energy efficient clustering Protocol to enhance for performance of heterogeneous WSN (EECPEP-HWSN). The proposed protocol divided the nodes into three different levels known as normal, advance and super nodes. The Cluster formed and CH elected on the basis of starting energy, hop count and remaining residue power. The EECPEP-HWSN protocol outperformed in comparison to existing protocols 188% better than LEACH, 150% than DEEC and 141% than SEP. Also, contributed to improve network lifetime, stability, energy efficiency and remaining energy in the network.Purkar et al.23 proposed multilevel clustering protocol (MLCP) for load balancing and energy efficient data aggregation in large scale scalable WSNs. Hierarchal clustering architecture developed for clustering and routing issues. CH selection done using hybrid algorithms of dragonfly and PSO. MLCP considered various parameters of network such as node degree, inter and intra cluster distance for scalability, load balancing and energy efficiency. The simulated results for MLCP proved the increase of 90% in lifetime and 19.36% in energy consumption in comparison to existing techniques BERA, PSO-ECHS and PSO-C for achieving load balancing and energy efficiency.Singh et al.24 introduced energy efficient clustering and shortest path routing Protocol (EECSRP) for WSNs by reducing network overhead, enhancing network lifetime, maximum PDR and energy conservation. The CH selected on the basis of residue energy, node degree and RSS . Also, RSS based network distribution implemented for estimating gradient based on demand path selection between SN and BS. During the clustering process, when CH residual energy attained the threshold level and reduced the transmission for control packets. To avoid the data collision and MAC layer conflict BS collected data from every CH. The simulated results provided consistent performance of the network in terms of average energy consumption, pack delivery ratio, controlled overhead and average end to end delivery compared to the existing protocols i.e. R-LEACH, CCMAR and EESAA.Inam et al.25 modified the GSA  for energy efficient clustering in WSNs. The proposed algorithm efficiently organized these clusters and calculated optimum number of CH depending upon the link quality and energy consumption. A new fitness function is proposed to organize the compact clusters by modifying GSA for calculating the mass values of power distance sums scaling technique. The fuzzy controller employed for identifying performance metrics of this technique for exploitation and exploration. The method presented minimized number of active CH to reduce energy conservation. The simulated results discovered that modified GSA performed better than existing metahueristics such as LEACH PSO-C, CSA, GSA and PDSS-GSA to enhance the network lifetime for energy efficient WSNs.Mood et al.26 developed a new GA based energy efficient clustering weighted clustering (EEWC) for heterogeneous WSN. New weighted fitness objective function provided the modified steady state phase of LEACH for three important clustering constraints named as density, partition and numeral cluster heads. The proposed optimization function used to find the finest sets for CH in SS phase of LEACH by virtue of GA. The simulated outcomes improved the performance of WSN as compared to SEP, ERP and IHCR in terms of network lifetime, stability and complete residue energy.Raju Pal et al.27 presented a DCAGBS approach  to improve the network life time of the WSNs. The clusters formed dynamically with the help of BS and here BS sent three information data for the time to reconfigure SKIP value  in accordance with fuzzy logic approach named as TAGAKI-Sugeno-Kang model. The concluded results showed that the centralized techniques are not feasible as they relayed too much information on severs that in turn increased the size and cost of the network so proposed distributed fuzzy based system for section of CH with dynamically configured BS. The proposed DCAFBS algorithm compared with the existing distributed and centralized methods  to evaluate the network lifetime.Delgado et al.28 proposed a EESCP  for balanced clustering based on inter an intra cluster distance to form clusters of equal size among the entire network. PSO based Dragon fly algorithm is employed for selecting cluster heads and cost of communication is reduced by minimizing the intra cluster distance to achieve the maximum network coverage. The simulated results implemented for various network densities and sizes and EESCP outperformed in comparison to other algorithms such as BBO, DE, ABC, PSO in regards to balanced cluster formation, rotation of cluster head, network lifetime, energy consumption and throughput. Also, the WSN function performed robustly for different network densities and sizes.Singh et al.29 presented eeTMFO/GA  for CH selection and the fitness function evaluated for residue energy, average cluster distance, node density, packet transmission and delay in transmission. The proposed optimizer MFO for CH election provided the overall enhancement of reliability and security of the network. The implemented results performed better for stable network and energy consumption compared to existing protocols such as 56.09% from HEED, 60% from LEACH and proved the better performance 42.26% and 16.36% with ABC and QABC respectively. Also, eeTMFO/GA showed huge reduction in electing malicious sensor nodes to be selected as CH as compared to ALM, TCM.Sharma et al.30 proposed a distributed clustering algorithm with normalized information measures for sensor networks. Initially, normalized information distance (NID) selected for cluster data and label by minimizing the objective function. And then by introducing minimum normalized information distance based algorithm (MNID) by minimizing NID for model selection by exploring the capabilities of non Gaussian data clustering. The MNID algorithm implemented for distributing clustering employed for finite time multi agent consensus to calculate the global parameters where local variable exchanged for single hop neighbors. MNID algorithms simulated for both centralized and distributed network coverage by numerically tested for both synthetic and real data. The proposed algorithm outperformed in comparison to existing algorithms in terms of network performance.Qin et al.31 investigated clustering protocols for network lifetime and SNs energy based on HEED for WSNs and summarized the advantages and disadvantages. The focus aimed to enhance network lifetime and energy consumption parameters for quality of service for both homogeneous and heterogeneous WSN. The analysis provided helpful information based on various HEED algorithms such as DWEHC, RUHEED, HEED-NPF, HELPER-HEED, U-HEED, H-HEED, D-HEED AND fuzzy based H-HEED.Ullah et al.32 proposed an improved grey wolf optimizer (I-GWO) for solving the engineering design optimization problems by developing premature convergence, lack of population diversity and imbalance between exploitation and exploration capability of GWO algorithm. I-GWO provided DLH (dimension learning based hunting) approach inherited from independent hunting of wolves in nature which provided balance in-between global and local search for maintaining diversity. The results for I-GWO simulated for uni-modal, multi-modal, hybrid and composite bench mark functions and are compared to various existing techniques such as PSO, KH, GWO, WOA,EEGWO, HGSO and proved that proposed I-GWO outperformed for various statistical tests and experiments for well suited for engineering problems and power flow issues.Nadimi-Shahraki et al.33 proposed a novel bio-inspired algorithm (NBA) protocol by combining LEACH with dragonfly algorithm for CH selection. The dragonfly algorithm utilized for deploying sensor nodes and selecting best possible route for delivery of packets to BS. The proposed algorithm performed better in expanding network lifetime as compared to existing algorithms such as LEACH, MS-LEACH, SFFA and ESO-LEACH) in terms of dead nodes, PDR and number of live nodes.Devassy et al.34 proposed two nature inspired routing algorithms for attaining energy efficiency and secure WSN named as intelligent opportunistic routing protocol (IOP) and trust based secured secure intelligent opportunistic routing protocol (TBSIOP). The proposed algorithms compared with ACO and PSO terms of energy efficiency, average risk level, network lifetime, end to end delivery and packet delivery ratio.Bangotra et al.35 for handling the complex real issues. This metaheuristic method is motivated by sexual inspiration and specific intelligence of chimps which is famous for their own group hunting. The robustness of this method shows yourself different from others. During finding the best target it works under four categories as barrier, driver, attacker and chaser respectively. The details of these categories are illustrated under the following steps as;Freshly, chimp optimizer is a newly population based nature inspired algorithm is developed by Khishe et al.n, c,m and a is denoted the number of iterations and coefficient vectors. The coefficient vectors are amended by the Eqs. .6\do.6\do9).6With the help of previous equations, the position of each member is evaluate by the following Eq. ;14\documLastly, the following Eq.  has beenThe pseudocode of CHoA algorithm is reported in Algorithm 1.The various complex domain real world optimization tasks related to energy constraint issues in wireless sensor networks are huge challenges for the optimizer methods. Last few decades, a large number of robust optimizer methods have been developed by scientists. But due to their various shortcomings these are not able to prove their own robustness for complex domain issues. Each optimizer method may be faced with some shortcomings for finding the best solution in the complex domains, so being that reasons these methods could be failure to trap the best optima location in the search domain for these complex suites.Hence, due to the current demand of the various domains of engineering, computer science and many more, we need a huge amount of most powerful optimizer methods which would fulfill the current and future demands related to Wireless Sensor Networks (WSN), engineering applications and many other optimization tasks. As an example, these methods could easily tackle complex tasks, if the exploration and exploitation phases of these optimizer methods would be most powerful.In this work, we are trying to introduce a newly enhanced version of chimp optimizer for tackling the energy constraint issues in wireless sensor networks. This version also helps to overcome the various shortcomings of the chimp optimizer such as imbalance amid the exploitation and exploration, diversity and premature convergence respectively. The proposed method benefits from a new drive technique known as dimension learning based hunting (DLH) search technique congenital from the specific chasing performance of the search members in search space. This strategy applies to construct a local region for every search member in the search space in which the best region information can be shared amid search members. Under this methodology, the DLH strategy helps in maintaining diversity and improves the balance between exploitation and exploration.The implementation stages of the proposed method are illustrated in detail by the following steps;n=30), constants  are are19) aith member. In the same way, the subsequent matrices can be express for the target by mathematical Eqs. 17\documeThe following Eqs.  and 21)21) are uDLH strategy helps to specifically chase the goal of the population members in the search area. This strategy applies to construct a local region for all chimps in the search area in which the best region or neighbors information can be shared amid chimps. Further the following subsection signifies how DLH and canonical ChoA portions create different members.a and c coefficient vectors are estimated via Eqs. . The. The9). via Eqs.  and 4)..a and c via Eqs. –13). Fi. Fia and via Eq. .In basic ChoA method the place is amended via first four member of the crowd. With this fact the original algorithm offers the crowd fails diversity too early, slow convergence, and population members can stuck in the native optima. To minimizing these drawbacks, in IChoA the DLH part supports in the distinct chasing of population agents that is learned by its neighbors.In DLH strategy, all aspect of the fresh place is amended via a mathematical equation in which this diverse population agent is learned via its dissimilar neighbors and a randomly chosen member from the population. Then, further ical Eq. .22\documThen, the near search members of d by Eq.  with resd by Eq. .Here \dod is illustrate the dimension.After that, the multi-neighbors learning is executed by the following mathematical formulation of Eq. ;24\documOver this part the finest population agent is designated via comparing the fitness outcome of dissimilar crowd members So, according to the previous mathematical equation if the fitness output of the chosen population agent is Last of all, the bring to an end conditions is taken for amending the fresh place of the population member in the search domain. This process is continuously repetitive, up until it does not satisfy the standards of prevention.The pseudocode of Improved Chimp Optimizer is reported in Algorithm 2.36, Chimp35, SBPSO37 and AOA38 respectively. Further, the performance of the presented method has been discussed by following subsections;For evaluating the robustness of the proposed method has been verified by various recent meta-heuristics such as SCAThe algorithms are coded in the RMatlab-2018a software and these runned on Core i3 8th Gen system, 8 GB Ram with 64 for bit operating system for comparing the robustness and effectiveness of these. During this evaluating procedure has been fixed the values of crowd (30), total iter (500), dimension of functions (10–100), lb and ub taken between − 100 to 100 etc.39. The 3-D graphs of these suites are illustrated by Fig. For examine the robustness and efficiency of the IChoA method, a well-known standard CEC’2017 test suites have been applied and reported in Table  1The trial must be done multiple n-times to create constant or stable statistical outputs for validating the accuracy and robustness of the metaheuristics. In this work, the same procedure has been applied for validating the performance of the algorithms. Output of the algorithms are verified in the terms of least and highest objective value, mean and standard deviation respectively. Experimental outputs and convergence performance of the metaheuristics are illustrated by Tables Experimental results and convergence graphs show that the IChoA method is giving the effective and highly accurate solutions on 29-CEC’2017 standard test suites as comparison than others. In addition, the results have been discussed in full details in the following subsections.This subsection is split into different categories as firstly, the uni-modal suites outputs denote the exploitation ability of the algorithms. Secondly, the various local optima are involved in multimodal suites which examine the ability of algorithms for many optima in the search space. Thirdly, the hybrid and composite test suites have been utilized to assess exploration ability of the algorithms.Uni-modal test suites which involve only single global and these have been utilized for testing the exploitation phase. Experiments showed that the IChoA method has demonstrated superior exploitation ability as comparison than other recent metaheuristics. Results of Tables As stated formerly, these suites are more compatible for evolutionary and benchmarking techniques. Experiments illustrate that the IChoA method is extremely functional. All simulations have proven that the enhancement of the ChoA method is more effective and successful in finding the best global optima for the single objective suites.Multi-modal test suites which involve various local optima in which the number of decision variables rises exponentially against the dimension of the test suite as compared to the uni-modal test suite. Normally these are used for verifying the suitability of the standard test suite techniques. Experiments show that the IChoA method proves the sophisticated and strong detection behavior.Hybrid and Composite test suites which involve large numbers of global and local optima. Generally these suites are used for verifying the accuracy of the algorithms in local and global optima survivors and balance amid exploitation and exploration respectively. Experiments give strong evidence that the IChoA is able to create a strong balance between exploitation and detection. With this fact, we can say that during finding the best optima score in the search area which showed the strong exploration and exploitation behavior of the proposed method. This strategy helps to create a new best location for all chimps in the search area at each generation and chimps amend their own current locations to the best location or with superior fitness in many points of view that make it possible for IChOA algorithm to reach the best score. Numerical and Statistical measurements show the strong detection behavior of the IChoA method in finding the best global values in the complex space. Because in hybrid and composite test suites involves more complex space so highly exploration and exploitation behavior based methods are needed for finding the accurate and best solution for these kinds of issues. Experiments give strong proof that the IChoA leads to best global optima with the help of their best scan behavior.In addition, IChoA method involve a strong local optima avodance from which helps in finding best optima fastly.In this subsection, we are discussing the statistical accuracy for the global optima score and performance of the proposed optimizer. The average value of each algorithm has been reported in Table In Fig. The convergence performance graphs of algorithms are drawn against the number of the iterations and best solution so far etc and presented in Figs. 40,this behaviour can guarantee that the evolutionary methods eventually touch to a target and find the local or global in a search domain. So, IChoA improves the fitness score for all chimps in a search area and assurance traps accurate targets for complex suites as iteration rises. Here can be said that this happens by this modification of the ChoA method. As each chimp travels from highest optima to lowest optima score, therefore with the hypothesis of growing in ChoA, the overall champs and their fitness values are amended against each iteration.As per V.D. Berg et al.With this strategy we save the best score and apply these scores for finding the next best location for the entire swarm in the search area. Experiments and convergence graphs shows that the IChoA method is capable of giving the higher grantee finds the optima against the least number of iterations in a complex space as comparison to others. This behavior illustrates the fast convergence speed of the proposed method. So, the proposed strategy is to help the chimps for finding the better regions fastly of the complex suites.To design WSN, clustering module and the routing module is discussed here for the proposed network architecture. Proposed architecture utilizes dynamic clustering algorithm with hierarchical cluster formation to achieve the energy efficient routing. To start with the designing of the network, initial network parameters are created and listed in Table To support the data aggregation in WSN, the network nodes can be accommodated in the small groups called the Clusters. Clustering can be defined as the division of the nodes in the groups on the basis of some mechanism. Clustering is done to achieve the energy efficiency and the scalability of the network. Formation of the cluster also involves the assigning the role to the node on the basis of their perimeters. Optimal clustering has a strong influence on the performance of WSN. Data aggregation is enabled at CH in order to discard the unwanted and uncorrelated data, thus saving the sensor node energy. As just CHs have to continue the local route set-up, routing can be managed easily and so just small routing information is required, this again enhances the scalability of the network to a significant extent. As the SNs communicate only with their CHs, communication bandwidth is also conserved thus avoiding the exchange of redundant messages within themselves.The Energy constraint is a big challenging task in the field of WSNs. During these efforts we are trying to propose a new Low energy, routing and efficient scalable clustering protocol based on Metaheuristics in WSNs for improving the lifetime and enhancing the wireless sensor networks (WSNs) quality of service. In this proposed strategy has been taken new constants for optimizing the cluster heads selection. The proposed algorithm introduced various characteristics including energy, load on the node, weight of a node, scalability and balanced cluster respectively. Here the proposed method, gives the optimize path from the search agents of the cluster to the cluster head by selecting the most efficient path, going over sensors taking not as much of distance to attain data and extra energy. The brief details of these characteristics have been illustrated by the following mathematical equations.41;n and The average energy of each sensor has been evaluated by the following equ. 41;26\docW).This parameter and constant is calculated by the following equ. in WSNs has been tested and verified by the various methods such as GA, OBCA, ALO, Chimp, SCA and IChoA respectively. The robustness of the algorithms have been verified in the terms of energy consumption, balanced clustering, network life, throughput, best least and highest fitness values etc. In the following subsections the brief explanation has been presented on the output solutions of the algorithms.The communication plot for packet transmission is shown in Figs. Here the throughput of the network is verifying the total number of packets acknowledged at base station in its period. The solutions of the algorithms are reported in Table In this subsection, we are comparing the total time consumption of the algorithms between all the sensor networks and cluster heads of the network. Experimental results are illustrated in Table In the literature various methods are applied for measuring the lifetime of a network. Under this work we are applying the various recent robust optimizers for evaluating the lifetime of the network. Numerical results of all the algorithms on In this subsection, we are measuring the total packet loss during the transfer of data of one source to another source. The results of the table shows that the IChoA is able to transfer the highest data with the least number of packet loss. Experimental results and graphs of algorithms in Table In this phase, we are measuring the energy consumption of algorithms by all the sensor networks and cluster heads of the network per round. The results of the algorithms are reported in Table Generally the least mean The least standard score presents to the convergence speed and stability of the algorithms. Results of tables, shows that the proposed method is finding the best destination for energy constraint issues against the least standard score. Hence on the basis of obtained solutions we can conclude that the IChoA algorithm traps the destination fastly with at least time as comparison to others. Additionally, the least standard scores prove the stability of the proposed method for energy constraint issues.The simulated performance evaluation of the proposed IChoA algorithm for computing analysis of various parameters such as network lifetime, average execution time, packet loss energy consumption and throughput are provided by Table The performance in percentage is listed in Table The proposed IChoA is using CH is selected with the less execution time only of the order of 1% in comparison to the other existing optimization algorithms GA (6%), ALO (9%), OBCA (3%), Chimp (41%) and SCA (64%). Also, the proposed IChoA algorithm consumes less time as compared with other approaches that leads to increase in the network speed which in turn decreases the average computation time. The proposed IChoA provides only 1% times the packet loss that is appreciable for the network data aggregation as compared to existing optimization algorithms GA (5%), ALO (10%), OBCA (2%), Chimp (24%) and SCA (64%). The energy consumption for the proposed IChoA is very less as compared to the other simulated methods and enhances the longevity of the network by consuming only 1% of the energy as compared to the other existing optimization algorithms GA (26%), ALO (34%), OBCA (7%), Chimp (23%) and SCA (24%). The throughput for the proposed IChoA is very high as compared to the other simulated methods and the packets reach the destination node successfully with minimum delay to increase capacity of the network by providing only 89% of the throughput as compared to the other existing optimization algorithms GA (14%), ALO (17%), OBCA (69%), Chimp (17%) and SCA (10%).Summing up, all the efforts show that the IChoA method is able to tackle complex real suites and energy constraint issues. Hence, this strategy could be helpful for resolving the complex issues related to different domains of the different fields.WSNs that proliferate various real world applications which can be serious such as surveillance of military or army battle field, health-care and face the task of restricted energy capacity. To resolve the issues of the energy constraint based on chimp optimizer, namely improved chimp optimizer (IChoA) is developed. This strategy has been introduced by integration of Chimp Optimizer and dimension learning based hunting (DLH). The DLH phase helps the ChoA algorithm for maintaining diversity and also improves the balance between exploitation and exploration.To evaluate the effectiveness of the proposed method, it has been tested on 29-CEC-2017 and issues of energy constraints. Accepting the accurate choice of modification, fitness functions and IChoA operations have controlled longer system operability and extreme data rate with least difficulty. All simulations prove that the robustness and performance of the IChOA is superior to other recent methods in terms of minimum and maximum objective function values, mean, average, energy consumption, loss packets, average time, throughput and network lifetime respectively.In future direction, the enhanced and hybrid optimizers will be introduced for complex energy constraints and engineering applications.


Asphalt mixture is a skeleton filling system consisting of aggregate and asphalt binder. Its performance is directly affected by the internal load transfer mechanism of the skeleton filling system. It is significant to understand the load transfer mechanisms for asphalt mixture design and performance evaluation. The objective of this paper is to review the research progress of the asphalt mixture load transfer mechanism. Firstly, this paper summarizes the test methods used to investigate the load transfer mechanism of asphalt mixtures. Then, an overview of the characterization of load transfer mechanism from three aspects was provided. Next, the indicators capturing contact characteristics, contact force characteristics, and force chain characteristics were compared. Finally, the load transfer mechanism of asphalt mixtures under different loading conditions was discussed. Some recommendations and conclusions in terms of load transfer mechanism characterization and evaluation were given. The related work can provide valuable references for the study of the load transfer mechanism of asphalt mixtures. Asphalt mixture is a typical composite material comprising aggregates, asphalt binders, fillers, and voids. The extrinsic uncertainty, irregularity, vagueness, and nonlinearity of asphalt mixtures’ properties  are the reflection of its microstructural complexity. The skeleton structure is regarded as the main body to transfer external loads in asphalt mixture and the aggregate skeleton contributes the most to the high temperature stability of asphalt mixture. Therefore, the performance of asphalt mixtures can be evaluated from the load transfer mechanism of the materials. The study of the load transfer mechanism of the meso-structure of asphalt mixture provides a new perspective for the study of classical problems, and can also induce new understanding. In the past decades, a variety of methods have been introduced to study the load transfer mechanisms, such as the photoelastic experiment method, digital speckle method, digital image processing (DIP) technology, discrete element method (DEM) simulation, finite element method (FEM) simulation, and the combination of multiple methods ,10,11,12The load transfer mechanism mainly refers to the characteristics of load transfer in an asphalt mixture under external load ,13. AggrThere are different load transfer mechanisms of asphalt mixtures under different test states, from compaction to service to destruction. The common compaction methods are the Marshall impact compaction (MIC) method , rotary The objectives of this review are the following, and the flowchart of this paper is shown in (1)Present the comprehensive review of the test methods and techniques for the load transfer mechanism of asphalt mixtures.(2)Collect and discuss the characteristic parameters of the load transfer mechanism.(3)Compare the load transfer mechanism of asphalt mixtures under different loading conditions including compaction degree, different loading frequency, tension–compression, shear condition, and bending condition.(4)Provide recommendations to select and improve the load transfer characteristics and evaluation parameters of the load transfer mechanism in asphalt mixtures.This paper mainly reviews and summarizes the meso-scale load transfer  and related evaluation indicators to provide a theoretical reference for the investigation of the macro-scale properties of asphalt mixture at the level of meso-scale mechanical mechanisms.For this review, the search papers across the three databases, Web of Science (WoS), Scopus, and China national knowledge infrastructure (CNKI), were used. The keywords searched mainly include asphalt mixture, meso-scale structure, load transfer, contact, contact force, force chain, pavement, aggregate blend, DEM, and FEM, etc. Most of the selected articles in this paper are published as journal articles.For the load transfer mechanism, the most common studies fall into three categories: contact , contacti~jC represents the contact number between particles of iA and jA, m is the smallest particle size in the aggregate blend, M is the largest particle size in the aggregate blend, and i represents the particle size, i = 1, 2, …, M.(1) Contact: This reflects the contact information between particles in asphalt mixtures. The contact skeleton of load transfer is formed between particles. Typically, the total contact number in asphalt mixtures can be defined by Equation (1) .(1)[Cm~(2) Contact force: This represents the force transferred between particles through each contact point.(3) Force chain: This presents the path of transferring external loading in asphalt mixtures and consists of the contact between particles and contact force.There is no standardized test method to assess the load transfer mechanism of asphalt mixtures. According to the previous study, the test methods of load transfer mechanism characterizations can be divided into simulation methods and laboratory test methods. The basic information regarding the corresponding methods to investigate the load transfer mechanism is presented in For the laboratory test methods, the use of image acquisition and DIP to analyze the contact characteristics of asphalt mixtures is more accurate ,17,37, bThis section provides a generalized overview of the characterization of contact, contact force, and force chain. The typical evaluation indicators are also illustrated and compared as follows.Aggregate contact characteristics, including the contact number, contact distance distribution, contact length distribution, and contact orientation, etc., are descriptions of the adjacent aggregate particle interaction, which play an important role in the load transmission in asphalt mixtures. The studies of contact in asphalt mixtures fall into two categories: (1) parameterization of aggregate contact ,55,56,57Initially, contact points are usually used to describe contact characteristics and evaluate the meso-structure performance of asphalt mixtures . HoweverDEM is a useful method for examining the meso-mechanical characteristics of granular materials and is crucial for understanding the meso-structures of asphalt mixtures. The contact properties of asphalt mixtures based on DEM have been the subject of numerous studies, including those on the impact of coarse aggregate morphology on the mechanical properties of the skeleton ,73,74,75To sum up, the contact skeleton structure formed by the particles in contact with each other is the load transfer path. The internal force transfer is the key to reveal the effect of the contact structure on mechanical properties. Then, it is essential to obtain the internal contact force response of asphalt mixtures to investigate the load transfer mechanism.In many studies, the contact force between particles refers to the normal component ,7,9,22. P(f)) decreases with the increase of nf  when nf ≤ 0.75. When 0.75 < nf ≤ 1.65, P(f) is directly proportional to nf, and when nf > 1.65, P(f) is inversely proportional to nf; P(f) remains essentially unchanged at nf ≥ 4.Due to the anisotropy of the contact force distribution, there is a certain deficiency to characterize the contact force distribution by the strong or weak alone. Therefore, a series of studies are carried out to characterize the contact force distribution, mainly including two categories: contact force probability distribution ,83 and cSome researchers also used various statistical parameters to characterize the features of contact forces. Zhu et al.  defined From the above studies, a lot of research has been carried out on the characterization of contact force distribution. The contact force distribution can only explain the overall force state of the asphalt mixture, but it cannot fully reveal how the load is transferred in the asphalt mixture and whether the load is transferred uniformly. The composition of asphalt mixtures has an influence on the contact force distribution, and the load-bearing capacity of each size aggregates also has effects on the load transfer mechanism. Based on current studies, the contact force characteristics of asphalt mixture need to be further analyzed.Numerous studies have found that the discontinuous and non-uniform arrangement of particles forms complex contact networks, which is the load transfer path ,88. HoweInitially, laboratory experimental methods were used to study force chain characteristics in granular materials. He et al.  utilizedThe mechanical characteristics of granular materials are influenced by force chains, which are a key component of the granular material mechanics theory . SeveralThe study of force chains can systematically reflect the load transfer mechanism of the asphalt mixture, which effectively avoids the limitation of evaluating the overall load-bearing capacity due to the unilateral analysis of contact or contact force. However, the evaluation characterization of force chains has not formed a completed system, and the characterization of force chains is only at the stage of basic statistical analysis.According to previous studies, a series of indicators have been proposed to characterize the load transfer mechanisms of asphalt mixtures. The typical indicators for load transfer quantitative characterization are summarized in Based on the review reported above, the existing parameters are insufficient for investigating the characteristics of the load transfer mechanism of each particle size. The contact structure is a complex topological structure where contact forces are transferred at various nodes to form force chains. Through the above research summary, no definite quantitative index is given to clearly define the load transfer characteristics. Meanwhile, it does not reveal the essential issues of grade design, material selection, and service performance quality of asphalt mixtures from the level of load transfer mechanism. Further studies are still needed to be carried out to combine the contact, contact force, and force chains, and thus, to reveal the relevant mechanical mechanisms in depth.There are different load transfer mechanisms in asphalt mixtures from compaction to service to destruction. Many researchers have conducted studies to analyze the load transfer mechanism of asphalt mixtures under different test states. Firstly, the load transfer mechanisms of asphalt mixture during the compaction process are summarized. Then, the internal load transfer mechanism of asphalt mixture under different test conditions is introduced. A comparative analysis of the load transfer mechanism under different test conditions is also carried out.In the process of asphalt mixture compaction, with the application of external load and different load frequency, the spatial position of aggregate changes , then thThe load transfer characteristics in compaction are usually investigated by simulating laboratory experiments. The Marshall impact compaction (MIC) method is most commonly used for fabricating asphalt mixture specimens. However, it was found that the load transfer characteristics inside the specimen under double-sided Marshall impact compaction and single-sided Marshall impact compaction are different . The midThe load transfer in the compaction process of laboratory specimen cannot completely reflect that of actual pavement in the field. Therefore, it is necessary to establish the relationship between field compaction and laboratory compaction for understanding the load transfer mechanism of asphalt mixtures. Dan et al. ,33 desigAccording to previous studies, there are different test conditions for the study of the load transfer mechanism of asphalt mixtures. Under different external loads and dynamic loading frequencies, the load transfer response in the asphalt mixture is different. Furthermore, the contact forces in the top part of the sample are always higher than those in the lower part . Chang eThe tension–compression conditions, shear conditions, and bending conditions are the typical loading conditions of asphalt pavement . Ma et aChen et al.  utilizedA random heterogeneous DEM model was employed by Xue et al.  to simulFrom above review, the researchers have studied the load transfer characteristics of asphalt mixtures under different test conditions from various loading aspects. It is indicated that the load transfer mechanism of asphalt mixtures under different test conditions are not the same. Although the analysis of the load transfer mechanism was carried out in the mentioned studies, only a preliminary statistical analysis of contact force distribution characteristics under the corresponding test conditions were carried out. There is no relevant evaluation system of the load transfer mechanism under different loading conditions established. The meso-scale load transfer mechanism under different loading conditions is the mechanical response of the macro-scale properties of the asphalt mixtures. It is necessary to carry out further relevant studies to investigate load transfer mechanisms of asphalt mixtures and establish the corresponding evaluation system of the load transfer mechanism, which will provide a theoretical basis for explaining the macro-mechanical properties from the meso-scale mechanical mechanism.Quantitatively capturing the relationship between the load transfer mechanism and the mechanical response of asphalt mixtures can provide a meso-mechanical basis for optimizing asphalt mixture design to improve the performance of asphalt pavement. This paper reviews the research progress of the load transfer mechanism of asphalt mixtures. Some conclusions are drawn as follows.(1) The study of the load transfer mechanism consists of three main aspects: contact characteristics, contact force characteristics, and force chain characteristics. Various technical methods for studying the load transfer mechanism are summarized and comparatively analyzed. With the comprehensive analysis of different methods used in characterizing the load transfer mechanism, the X-ray CT and DIP and numerical simulation is highly recommended to be used for investigating the load transfer mechanism of asphalt mixtures.(2) A systematic summary analysis of load transfer mechanism evaluation indexes revealed that the application of several evaluation indicators in combination could be better for characterizing load transfer mechanisms, and the statistical methods can obtain better typical quantitative indicators.(3) The meso-scale load transfer mechanism under different loading conditions is the mechanical response of the macro-scale properties of the asphalt mixtures. So, it is important to carry out further relevant studies to investigate load transfer mechanisms of asphalt mixtures and establish the corresponding evaluation system of the load transfer mechanism, which can provide a theoretical basis for explaining the macro-mechanical properties from the meso-scale mechanical mechanism.(4) To date, systematic evaluation methods on the load transfer mechanism of asphalt mixtures are not well developed. These should be considered to efficiently obtain how the load transfer mechanism functions during the actual service of asphalt mixture through the analysis algorithm. Further, a reasonable evaluation system of the load transfer mechanism should be established in order to realize the effective evaluation of the actual road structure’s load-bearing capacity.Extensive research has been carried out in the past to study the load transfer mechanisms of asphalt mixtures. The following points enlist the recommendations for future studies.(1) The X-ray CT and DIP and numerical simulation is highly recommended to be used for investigating load transfer mechanism of asphalt mixtures.(2) The contact structure is a complex topological structure where contact forces are transferred at various nodes to form force chains. The load transfer  mathematical model can be established according to the statistics method and graph method. Meanwhile, the optimal load-bearing structure of asphalt mixture can be quantified and analyzed by using the topology theory. It can provide the theoretical guidance for the mixture design.(3) It is strongly recommended that several evaluation indicators  are used in combination to characterize load transfer mechanisms.(4) Based on the quantitative definition of the load transfer structure and characteristics, a series of studies should be conducted to explore the mechanism of the relationship between load transfer characteristics and performance. Numerical simulation experiments are carried out to explore the load transfer mechanism, and corresponding laboratory experiments are carried out to explore the macro-scale performance, so as to establish the mathematical model between the load transfer mechanism characterization indicator and macro-scale performance.


In the current research article, the two-dimensional, incompressible, steady fluid flow is considered. The heat transfer rate of water-based aggregated fluid between converging/diverging channels of shrinking/stretching walls due to the effects of thermal radiation has been examined. The strong static magnetic field is applied perpendicular to the radial direction. The modeled governing equations are transformed into non-linear dimensionless ordinary differential equations by considering appropriate similarity transformations. Since the obtained ODEs are strongly non-linear and the exact solution of these equations is not possible, thus we applied the numerical method RK4 combined with the shooting technique to handle the equations. The impacts of several influential parameters on velocity, temperature, and entropy generation profiles are examined graphically. The incompressible, viscid 2D fluid flow between convergent/divergent channels whose walls are separated through fixed angle and determined by sink or source at the apex is defined as the Jaffery-Hamel flow. It has large number of applications in mechanical, civil, chemical, and aerospace engineering as well as in physical and biological fields. Blood flow through capillaries and arteries, flow of rivers and canals are also the examples of convergent/divergent channel flow. The innovative work about convergent/divergent flow was initiated by Jeffery and Shaw3 examined the impacts of incompressible MHD flow of copper nanoparticles on entropy generation by using joule heating effects and viscous dissipation passing through convergent/divergent channels. They investigated that by augmenting the values of volume fraction by nanoparticles, the values of Reynold’s number upsurges. They also observed the opposite behavior of flow in converging and diverging channels by giving different values to the influential parameters. The convergent/divergent channels of stretched walls were considered by Gerdroodbary et al.4 in which they discussed the effects of thermal radiations on Jaffery-Hamel flow. They used nonlinear boundary layer theory for the governing equations. They also discussed the skin friction and heat transferring phenomenon. They investigated the improvement in the temperature field by incrementing thermal radiation parameter. Asghar et al.5 considered MHD Jaffery-Hamel flow in converging/diverging channels due to the effects of boundary stresses. They used traction BCs to solve the governing equations. They developed some analytical scheme to obtain the solution of nonlinear 3rd ordered ODE’s. They observed that the boundary stresses support the boundary layers. They also examined the properties of slip and non-slip conditions of inertial flow. Khan et al.6 considered the stretched convergent/divergent channel and analyzed the effects of Soret and Dafour impacts on Jaffery-Hamel flow of 2nd grade fluid. They solved the problem numerically as well as analytically. For analytical solution they used HAM procedure and for numerical purpose they used RKF. The effects of different pertinent parameters on Nusselt number, skin fraction and Sherwood numbers are also discussed. The study of converging/diverging channels in case of MHD Jeffery-Hamel flow was considered by Asadullah et al.7. Several research articles about convergent/divergent channel are available in which various flow properties have been inspected under the effects of MHD and other external forces. Enough material about this topic is available in11 and the references therein.AlamPolymers are the complex structure of non-Newtonian fluids which exhibits rheological effects. Hydrogen, hydrocarbons, and different mixtures of carbon materials are used for the manufacturing of polymers. It has lot of applications in biological, chemical, and mechanical fields. In chemical engineering polymer films are used for the conservation/generation of energy. Different polymer structures are used for the transportation of small molecules through polymeric materials. In biological field polymers are used for the growth of multi-functional bio-interfaces, which are applicable to a range of biomedical applications. Formation of sensors, new optical and biomedical tools is also the applications of polymers. Polymers also play an important role to overcome the consumption of energy in case of turbulent flow.12. They used nonlocal lubrication theory of fluids with microstructures and calculated the liquid draining rate of thin film between two solid smooth surfaces. They observed that for all film thicknesses down to zero, the proposed viscosity model of nonlocal lubrication theory has the best settlement with the experimental results. The thermal radiation effects of MHD micropolar nanofluids channel flow with porous walls was explored by Alizadeh et al.13. They inspected the properties of nanoparticles volume fraction parameter, micro-polar parameter, radiation parameter and magnetic parameter on temperature field, velocity field and Nusselt number. Khan14 analyzed the effects of shear stresses and the rate of heat transferring through the hybrid nano polymers (CuO-TiO2-polymers) by using novel models of hybrid nanoparticles. He also observed that the resistive effects due to permeability decreases in case of nano polymers with CuO as compared to the hybrid nano polymers (CuO-TiO2-polymers) and by increasing the values of vortex viscosity parameter, the angular velocity decreases. It is also noted that the change in angular velocity is more effective in case of hybrid nano polymers as compared to nano polymers. Nadeem et al.15 considered 3D micropolar fluid flow on Riga plate and observed the behavior of heat transferring and thermal conductivity through the fluid. They considered the flow through exponentially stretched surface. They divided their analysis in two parts i.e., PEST  and PEHF . They observed that by enhancing the radiation parameter both PEST and PEHF reduces. Iram et al.16 considered joule effects, viscous dissipation and 1st ordered chemical reaction in micropolar fluid flow and noticed the changes in thermal conductivity and concentration gradient. They perceived that for positive values of chemical reaction, the concentration field rises and vice versa. A number of studies20 on polymers are presented in which various aspects have been discussed.The rheological effects of polymers with spherical solid structures known as micropolar fluids were debated by Nayak et al.Aggregates are the groups of large number of colloidal inertial particles. The addition of such aggregates shows significant role to the shear forces and thermal conductivity of the fluid flow. e.g., sedimentation of particles in oceans, separation of solid liquid particles, flocculation of cells etc. Flow characteristics of nanoparticles also enhances with aggregation. Brownian motion of the fluid particles plays a significant role in the development of aggregates.21 calculated the viscosity of nanoparticles by stable molecular dynamics under the effects of nanoparticles aggregates on Green–Kubo equation. They observed that the nanoparticles viscosity enhances with the nanoparticle’s aggregation. Ritschel and Totsche22 considered 3D natural fluid flow in permeable system and observed the effects of micrometer size aggregates on flow regimes. They considered the networks of typical porous medium to model the soil aggregates. They observed that by developing the fundamental aggregation properties, the transporting properties of fluid flow from soil pore space upsurges. Thomas Kurobe et al.23 used the solutes like O2, CO2 mineral nutrients between environment and the aggregates and analyzed the heterogeneity of melted substances into the ambient water. They designated the fluid flow and the solute supply around sinking aggregates by resolving the governing and advection–diffusion equations numerically. Roberto Camassa et al.24 considered the matter aggregation in fluid system under the effects of gravitational forces. They observed that particles suspended in stratified fluid self assembles and makes large aggregates without adhesion. They also investigated that the particles possessing the same heights results in the attractive horizontal forces. They solved the system of equations numerically. A lot of material on aggregation is available in Ref.30.Bao et al.As during any thermal process, a lot of energy losses due to which the production rate decreases. To improve the efficiency of any thermal system it is essential to search out the factors of energy losses. Entropy is the measure of uncertainty (energy losses) in a system. Entropy helps us to identify the factors that are responsible for energy losses. The second law of thermodynamics states that all the real processes in the universe are irreversible. Entropy generation is an important tool to measure the irreversibility of real systems.31 examined several causes for entropy generation in thermal engineering where entropy production abolishes available work of a system. Bejan32 also investigated the entropy generation effects on fluid flow by considering the force convective heat transfer, temperature gradient and viscosity effects. Thacher33 considered the idea of entropy rate to examine the effectiveness of thermal electrical generators and heat pumping. Mukherjee et al.34 discussed the second law of thermodynamics for spinning flow in cylindrical pipe whose walls are at constant temperature. Carrington et al.35 discussed the causes of entropy generation by the heat and mass transferring for both inlet and outlet flows by using control volume method. The entropy generation effects on transparent plate in permeable medium along with solar radiations effects were analyzed by Dehsara et al.36. They discussed the results numerically. Shukla et al.37 considered Al2O3-Cu water-based hybrid nanofluid through stretching/contracting walls under the effects of thermal radiations; they noticed that the volume fraction of hybrid nanoparticles shows a significant role for reduction of entropy generation. For solving the modeled problem, they used PHAM technique. Many researchers discussed about entropy generation on MHD flow, whose detail can be seen in the articles41 and the references therein.BejanIn the current study, we considered the two-dimensional aggregated water-based fluid between converging/diverging channel of stretching/shrinking walls. The steady and incompressible flow is considered under the effects of strong thermal radiative effects. The numerical scheme RK4 is implemented to obtain the solution of nonlinear ODEs. The Bruggeman model is used to obtain the thermal conductivity of aggregation. The effects of different parameters like radiation, volume fraction by nanoparticles, Reynolds number, sweep angle, stretching/shrinking parameter, Eckert number on velocity field, temperature field and entropy generation are discussed graphically.43,Here we considered two-dimensional steady flow between converging/diverging channels. The non-parallel walls of the channel make a sweep angle of measurement mentclass2pt{minimThe appropriate BCs at shrinking/stretching walls are:To convert the above system into dimensionless form, the following similarity transformations are applied.In above Eqs. –7, \documentclass2pt{minimIn the above Table 144:The Bruggeman model was used for transforming the Maxwell model to obtain the thermal conductivity of aggregation. The aggregated thermal conductivity model was displayed asBy the applications of transformations , Eqs. 11–5 becomThe transformed dimensionless boundary conditions corresponding to Eqs.  and 6)  6) are aIn Eqs. –12 the d45 is expressed as:The rate of entropy generation for the recent problem by following BejanUsing the similarity transformations  and Eq. In the current exploration the modeled physical problem is based upon conservation laws which appear in the form of PDEs. These equations are changed into the set of highly coupled nonlinear ODEs. Since the transformed equations are highly nonlinear and exact solution of these coupled equations is not available. To overcome this issue, in our work, we applied a famous numerical scheme (RKF) combined with shooting iteration technique to compute the solutions of the transformed fluid flow problem. Solutions for temperature, velocity and the entropy generation fields are observed graphically, and the impacts of several influential parameters are examined.This section is divided into three parts to study the variation in temperature, velocity, and entropy generation of fluid flow for both converging/diverging channels of shrinking/stretching walls under the effects of different parameters like sweep angle The impressions of pertinent parameters on velocity profile are plotted in set of Figs. The change in the temperature field due to the impacts of different parameters like opening angle mentclasspt{minimamentclasspt{minimaThe impressions of Eckert number The variation of temperature profile due to stretching and shrinking parameter The Figs. The Impact of volume fraction by nanoparticles on temperature field is designed in the set of Figs. The opposite impact of Reynolds number on temperature field is observed for diverging and converging channels as displayed in Figs. The impacts of different pertinent parameter like opening angle The boost in entropy generation profile due to increasing values of Eckert numbers for divergent channel is observed for both stretching/shrinking walls. This alteration in the entropy generation is more prominent near center of channel for stretching/divergent as shown in Fig. The increase in radiation parameter boosts the entropy generation of the system for all the case involved as shown in the set of Figs. The change in entropy generation due to mounting values of Reynolds number is plotted in the set of Figs. Table The augmenting values of angle of elevation reduces the velocity profile for stretching/shrinking walls of diverging channel, while a reversed performance is detected for the case of converging channel of stretching/shrinking walls.The stretching parameter boosts the velocity profile, whereas the shrinking parameter diminishes the velocity field for converging as well as diverging channel.The nanoparticles volume friction and Reynolds number declines the velocity profile By mounting the values of sweep angle The growing values of The temperature profile rises for both converging and diverging channels under the effects of stretching parameter, while shrinking parameter diminishes the temperature profile for both converging and diverging channels.For the growing values of radiation parameter and nanoparticles volume fraction, the temperature profile reduces for both converging and diverging channels of stretching/shrinking walls.The entropy generation declines for all the values of opening angle The increasing values of Eckert number There is direct variation between radiation parameter and the entropy generation profile i.e., by mounting the values of radiation parameter, irreversibility of the system increases.Reynolds number declines the entropy profile for stretching/shrinking diverging channel and upsurges it for stretching/shrinking convergent channel.In the current exploration, the heat transfer and thermal conductivity of steady, incompressible aggregated fluid flow under the effects of magnetic force is considered. The flow is considered between two non-parallel stretching/shrinking walls. The impacts of radiation parameter (Nr), Reynolds number (Re), volume fraction of aggregated nanoparticles (