In-process monitoring of weld penetration depth is possible with optical coherence tomography (OCT). The weld depth can be identified with OCT by statistical signal processing of the raw OCT signal and keyhole mapping. This approach is only applicable to stable welding processes and requires a time-consuming keyhole mapping to identify the optimal placement of a singular OCT measuring beam. In this work, we use an OCT measurement line for the identification of the weld depth. This approach shows the advantage that the calibration effort can be reduced as the measurement line requires only calibration in one dimension. As current literature focuses on weld depth measurement with a singular measurement point in the keyhole, no optimal algorithm exists for weld depth measurement with an OCT measurement line. We developed seven different weld depth processing pipelines and tested these algorithms under different weld conditions, such as stable deep penetration welding, unstable deep penetration welding, and heat conduction welding. We analyzed the accuracy of the weld depth processing algorithms by comparing the measured weld depth with metallographic weld depths. The intensity accumulation approach is identified as the most accurate algorithm for successful weld depth measurement with a scanning OCT measurement line. The laser welding of copper materials gains relevance in an increasing number of applications in electronic products such as battery tab connectors  and statThe successful determination of the weld penetration depth with OCT depends on the accurate positioning of the measurement beam in the keyhole, and the choice of a suitable OCT data processing approach. The keyhole mapping approach was suggested to determine optimal measurement beam placement . This apThe goal of the data processing approach is to identify the maximum intensity within the resulting measurement signal at each measurement point, as the signal intensity of the OCT is maximized for a direct reflection from the keyhole bottom. However, different artifacts and noise may occur that make it difficult to identify the keyhole depth position. The noise in the OCT signal originates either from the setup or the process. The setup noise includes artifacts from the DC-term  and the th percentile filter from the statistical density function [th percentile filter to the statistical density function.Alternative data processing approaches are necessary to enable a more deterministic approach, while acquiring insights into the keyhole dynamics. A kernel density estimation (KDE) with Gaussian kernel applied to a moving window, with regards to the signal time-series, allows to calculate a nonparametric estimate of the statistical density function. First, the weld penetration depth can be determined with an 80function . Second,function . The staFinally, the direct measurement of the weld penetration depth can be acquired by finally subtracting the result of the distance measurement from one measurement beam in the keyhole, and another on the surface plane of the workpiece .Nevertheless, all the before mentioned approaches to determine the weld penetration depth using only one measurement point in the keyhole, require time-consuming keyhole mapping in two dimensions. Our work proposes the measurement of the weld penetration depth with a measurement beam that is scanned laterally to the processing laser. In consequence, we expect to reduce the effort in keyhole mapping. Furthermore, no optimal data processing approach exists for two-dimensional measurement information from laterally scanned OCT measurement beams in the case of weld penetration depth measurement.In consequence, we investigate the applicability of seven different data processing approaches for weld depth determination for laser welding of copper with a laterally scanned OCT measurement beam at four different distances towards the TCP. The applicability of each data processing approach is determined for different weld conditions with stable and unstable keyhole, as well as welding parameters in the heat conduction regime with measurements in the melt pool denudation. The accuracy of each data processing approach is determined by a comparison of the measurement error towards metallographic weld depth measurements.First, the experimental setup is introduced. Second, the experimental procedure is described. Lastly, the data processing approaches are described for the weld depth measurement from laterally scanned OCT measurements, and methods for the comparison of the weld depth measurement with metallographic evaluations.The experimental setup consists of a programmable focusing optic with cross-jet, fiber-coupled processing laser, and OCT . The lasA weld seam length of 60 mm is welded bead-on-plate on pure copper workpieces  in the focus position. During the welding process, the OCT measurement beam is scanned perpendicular to the weld seam with the welding speed in the x-direction. The OCT measurement beam has a length of 2 mm in the y-direction with 200 measurement points and resulted in a frame rate of 87 Hz . In consParameters are varied for laser process parameters, and OCT parameters . The lasWeld depth measurements are performed by OCT data and the metallographic evaluation of probes. Scanned OCT measurements follow the same data processing procedure: OCT sensor signal transformation to depth information, noise reduction, segmentation in y-direction, segmentation in z-direction, and feature extraction for the measurement of weld penetration depth . The OCTNoise reduction is performed by background subtraction with averaged background information. Remaining noise artifacts from lenses in the optical setup are identified by Hough transform and set to zero.Afterwards, a segmentation in the y-direction is required to distinguish between the workpiece surface and the keyhole opening. First, the workpiece surface is detected by identification of the image row with maximum accumulated signal intensity as the signal intensity on the workpiece surface is higher than the signal intensity in the keyhole. The workpiece surface determines the reference position for weld depth measurement in the keyhole. Second, the keyhole is determined by identification of columns in the y-direction with reduced signal intensity by analysis of the falling and rising edge intensity at workpiece surface level.Only the keyhole opening is considered for further processing steps. The segmentation of the keyhole opening in the z-direction aims for the isolation of measurement information from the keyhole bottom by separating the workpiece surface, the keyhole region, and the region below the keyhole. Two different methods are applied for this segmentation process: the moving average method  and the The moving average method calculates the mean signal intensity in a window with a depth of 40 pixels in the z-direction and a width that corresponds to the width of the keyhole opening in the y-direction. Starting at the workpiece surface, the window is moved in the z-direction after each calculation up to a distance of 5 mm. This limit is set according to the thickness of the workpiece. Reflections from the keyhole bottom lead to an increase in signal intensity. In consequence, we expect windows not to contain measurement information from the keyhole bottom if they fall below the 75th percentile of all calculated mean values. A window is chosen as the lower limit of the keyhole if the percentile condition is fulfilled, and no neighboring window above the 75th percentile can be found in the negative z-direction. All windows below this window are discarded.In contrast to the moving average method that is applied to each 2D OCT image frame, the MIP method considers the measurement information from all 2D frames along the measurement time. Here, each coordinate of a 2D OCT image frame in z- and y-direction are compared with the identical coordinate in all other taken 2D OCT image frames regarding the intensity value. Maximum intensity values of a coordinate are considered for the resulting projected image frame. The highest pixel intensity region within the projected image frame defines the keyhole region, while measurement information below this limit is discarded. The maximum width of the keyhole opening defines the outer limit in the y-direction.The measurement of weld penetration depth is tested with three different feature extraction algorithms: intensity accumulation, max-value, and kernel density estimation. These approaches for feature extraction differently consider the dimensionality of the measurement information .The application of KDE is an adaption from the literature . Here, eThe accumulation of intensity differs from the application of KDE as the lateral measurement information is seen as a whole in the y-direction. The accumulation of intensity approach follows two steps: thresholding and the accumulation of intensity. Thresholding is performed by the identification of each maximum intensity value within each measurement point in the y-direction and subsequent application of an 80th percentile. This percentile filter is chosen as measurement points that are directed towards keyhole walls show lower intensity values than a measurement point directed in the keyhole bottom. Afterwards, the weld penetration depth is extracted by an accumulation of intensity. Here, the intensity values for all lateral measurement points in the y-direction are added at each z-coordinate in the keyhole. This process is repeated for the subsequent 2D-OCT image frames. The z-position with the maximum accumulated intensity is considered the weld penetration depth. Finally, the max-value approach is the simplest feature extraction method. Here, the weld penetration depth is determined by extraction of the maximum signal intensity value within the keyhole. In the end, the extracted pixel coordinate in the z-direction is transformed to the weld penetration depth by considering the axial pixel size of 11.7 µm.In this study, a comparison of the different segmentation and feature extraction algorithms is performed. The analysis of the different data processing methods is possible by comparing measured weld penetration depths with weld penetrations depths that are determined via metallographic cross-sections. The comparison between measured weld penetration depth by OCT and metallographic analysis are performed by root mean square error (RMSE).n is equal to the total number of in-process frames, iy is the algorithm’s calculated weld seam depth, and The RMSE can be calculated as follows:The goal of our work is to identify the best data processing approach for laterally scanned OCT measurement beams for weld penetration depth measurements, and to show the applicability of laterally scanned OCT measurement beams for weld depth determination. In consequence, we analyze the applicability of our different data processing approaches for stable deep penetration welding, unstable deep penetration welding, and heat conduction welding.OCT measurement information in the case of stable deep penetration welding processes shows characteristically increased noise in the keyhole region, due to the influence of the vapor plume and the high reflectivity of the molten material, with a maximum signal intensity within the ROI at the keyhole bottom a. We appOverall, the influence of the OCT measurement beam position behind the TCP within the range of 0.06 mm and 0.12 mm does not show a significant impact on the capability of the different data processing approaches in the case of stable deep penetration welding. The combination of the laser power and the welding speed shows a higher impact on the overall measurement success. Welding at higher laser powers  shows generally more successful weld depth measurements in comparison to welding at 4000 W. This can be explained by the change in the keyhole morphology, where possibly a different measurement position is required to identify the keyhole bottom. As all data processing methods show a trend towards lower RMSE value for an increase in the OCT measurement position behind the TCP, at a laser power of 4000 W and a welding speed of 15 m/min, we expect a more accurate weld depth measurement for these parameters for a further increase in OCT measurement position behind the TCP. This is in accordance with findings by Beck et al.  who coulAs no accurate measurement of the weld depth is possible for welding at 4000 W and 15 m/min within our range of OCT measurement positions, we focus on welding at 5000 W and 6000 W at 20 m/min and 40 m/min for the comparison of the different data processing approaches. Generally, a standalone application of KDE without z-direction segmentation does not lead to accurate weld depth measurements . This isSimilar results can be achieved with max intensity extraction. As the extraction of maximum intensity requires lower computational effort than the calculation of an 80th percentile, the maximum intensity weld depth extraction can be considered as a good alternative to the established KDE weld depth extraction. However, the max intensity weld depth extraction is sensitive to measurement positions at the keyhole side walls, where also maximum intensity information can be found due to the specific keyhole morphology in the case of the laterally scanned OCT measurement beam.Intensity accumulation as a weld depth extraction method overcomes this issue. The keyhole bottom has a certain width that leads to higher neighboring pixel intensities. As the feature extraction with intensity accumulation weighs this aspect of measurement information, improved RMSE values can be found for IA regardless of the segmentation method. In consequence, IA can be recommended as a weld depth extraction approach for laterally scanned OCT measurement beams.OCT measurement information in the case of unstable deep penetration welding processes shows similar noise in the keyhole region like stable deep penetration welding. Additionally, the beginning of melt ejections in the form of spatters can be recognized within OCT measurements a. All daThe application of KDE without z-direction segmentation is the only data processing pipeline that leads to an overestimation of the weld depth c becauseThe application of segmentation methods, such as MIP and MA, improves the accuracy of the weld depth extraction method and tends to the identification of underestimated weld depths . The undMax intensity extraction shows the lowest resulting RMSE values in comparison to the other weld depth extraction methods. Here, we expect that this weld depth extraction method results in the lowest RMSE values as this approach does not show a directional dependence towards the measurement information. Here, the maximum intensity value allows concluding on direct reflections from the keyhole morphology. Measurement errors may result from direct reflection at keyhole positions closer to the workpiece surface.The choice of the segmentation method does not show a significant impact on the RMSE value regardless of the feature extraction method. This is the case as the relevant measurement information is extracted closer to the workpiece surface due to high signal intensity components that originate in reflections from the keyhole morphology at elevated positions instead of the keyhole bottom. In consequence, the relevance of the ROI size in the z-direction and the resulting influence by artifacts at positions below the keyhole bottom are negligible.In conclusion, the unstable keyhole does not allow for accurate measurements of the weld depth. Weld depth extraction methods enable to identify the keyhole morphology but presumably fail to identify the keyhole bottom due to the dynamic melt flow. Best results are achieved with the max intensity extraction as this approach does not show a directionality in its detection of relevant measurement information in the keyhole.OCT measurement information in the case of heat conduction welding processes shows slightly less noise in the interaction region as no vapor plume is generated as in stable deep penetration welding a. HoweveLike previously mentioned results, experimental results for heat conduction welding by the weld penetration depth extraction approach with KDE show the highest RMSE values without previous segmentation in the z-direction . As soonIn conclusion, similar findings with regards to the data processing approaches can be identified for heat conduction welding, as for stable deep penetration welding. The application of intensity accumulation results in the lowest RMSE values due to the measurement information from the denudation bottom at different lateral positions.In this report, experimental work was performed to assess the influence of different data processing approaches for weld depth measurements from laterally scanned OCT data in the case of laser welding copper. It was shown that the segmentation of the OCT data in the z-direction is crucial for successful weld depth extraction. The MIP segmentation approach showed better RMSE values than the MA segmentation approach with all further weld depth extraction methods. A successful weld depth extraction was possible by intensity accumulation within the ROI of the laterally scanned OCT measurement beam for heat conduction welding and deep penetration welding. Overall, the extraction of the maximum intensity shows a good alternative to KDE. The disadvantage of KDE and maximum intensity as weld depth extraction methods, in comparison to the intensity accumulation method, are based on the higher sensitivity to measurement information extraction from measurement points that are not directed to the keyhole bottom. Measurements with unstable deep penetration welds resulted in non-successful weld depth extractions due to the high melt pool dynamics. We expect these findings to be beneficial for quality assessment in laser welding. The shown applicability of different data processing approaches for weld depth extraction of a laterally scanned OCT measurement beam will reduce the calibration effort for weld depth measurements with OCT, and shows alternative data pipeline in comparison to the state-of-the-art.


There are numerous bio-inspired synaptic and structural plasticity mechanisms available, especially for spiking neural networks, which can drive the network towards the criticality. However, there is no solid connection between these self-adaption mechanisms and the task performance, and it is not clear how and why particular self-adaptation mechanisms contribute to the solution of the task, although their relation to criticality is understood. Here we propose an evolutionary approach for the structural plasticity that relies solely on the task performance and does not contain any task-independent adaptation mechanisms, which usually contribute towards the criticality of the network. As a driver for the structural plasticity, we use a direct binary search guided by the performance of the classification task that can be interpreted as an interaction of the network with the environment. Remarkably, such interaction with the environment brings the network to criticality, although this property was not a part of the objectives of the employed structural plasticity mechanism. This observation confirms a duality of criticality and task performance, and legitimizes internal activity-dependent plasticity mechanisms from the viewpoint of evolution as mechanisms contributing to the task performance, but following the dual route. Finally, we analyze the trained network against task-independent information-theoretic measures and identify the interconnection graph’s entropy to be an essential ingredient for the classification task performance and network’s criticality.Oscillator networks rapidly become one of the promising vehicles for energy-efficient computing due to their intrinsic parallelism of execution. The  More recently, criticality has found its application in the theory of neuromorphic computing and neural networks, both artificial and biological. In particular, it has been shown that a network at the critical state exhibits a high computational performance during classification tasks5, possesses a wide dynamical range6, and maximal information transmission and storage capacity8. Since the discovery of criticality in neocortical circuits9, the research focus is centered around self-organized motifs of criticality13. Typical self-organization mechanisms which may lead to criticality in biological neural networks and their artificial counterparts are the synaptic and structural plasticity. These activity-dependent adaptation mechanisms allow for the adjustments of the signal propagation rate from neuron to neuron and the time-varying interconnection topology of the network, respectively14. It has been shown that some plasticity rules  for spiking neural networks) can tune the network towards criticality and, thus, appeared to be beneficial for certain tasks (like classification)15. However, these rules are rather decoupled from the task and a direct relation between the plasticity mechanism and the task performance is missing. Very recently, it has been shown that the plasticity mechanisms responsible for spatio-temporal learning also can tune a network to criticality16. However, it is again not clear if the plasticity mechanisms used therein  simply steer the considered class of recurrent neural networks to criticality, and, therefore, contribute towards a successful realization of tasks.Criticality as a property marking the transition between ordered and disordered states has been a central focus of statistical physics for decades18 and surveys20) whereas the bio-inspired plasticity rules are used to precondition the structural and dynamical properties of the reservoir, and the supervised training procedure for the readout is performed to realize certain functionality . The proposed training procedure 16. Our result is, however, the first one in which criticality signatures have been obtained without any activity-dependent plasticity rules, but following the task performance solely. Additionally, in contrast to the existing results, we show the persistence of criticality signatures in the STO-network under structured periodic input, whereas such input breaks criticality and a sufficient noise is necessary for the occurrence of the criticality signatures for a class of self-organized spiking recurrent neural networks16. At the end of the paper, we analyze the trained network against task-independent information-theoretic measures and provide a qualitative characterization of the interconnection graph evolution during training.In numerical simulations, we confirm that the best task performance is indeed achieved at the criticality. This is an indicator of a certain duality between the task performance and the criticality observed in many previous results34L is the nonlinear frequency coefficient, Q is the nonlinear damping coefficient, I is the current density applied to the system, and parameter The magnetization dynamics of the STO can be modeled bySolutions to  will osci-th oscillator, and the complex-valued coupling F represent the coupling strength and the coupling phase, respectively. A typical behavior of solutions to . The critical regime (orange dot) manifests itself in a power-law probability distribution of cluster sizes. Additionally, the presence of different dynamical regimes on either side of the critical point indicates that the power law is related to a phase transition, and serve another signature of criticality in the network according to Beggs and Timme36.Synchronization properties of the oscillators’ dynamical behavior heavily depend on the intensity of interaction between oscillatorsval . The network is initialized in the super-critical state with high values of coupling parameters and the all-to-all interconnection topology. Even in the absence of any external input such network exhibits a high coherence of oscillators’ behavior since every node influence its neighbours too much.z as its input and returning a probability of the input to belong to one of 3 classes of digits . With this setup, the readout captures the temporal evolution of the reservoir. The weights of the readout are trained using classical supervised learning algorithms (see section Methods).To reconstruct the external input (MNIST digit), we augment the STO-network with a readout, which is a two-layer ANN taking the discretized (in time) evolution of In the considered supercritical regime, the external inputs do not qualitatively change the collective behavior and the network shows a high coherence of behavior and, therefore, it is difficult to decide on the unknown external input by looking into the networks’ evolution. This is a typical shortcoming of the super-critical behavior.37 and its scheme is depicted in Fig. As a driver for the structural plasticity, we use a direct binary search guided by the loss of supervised learning procedure for the readout weights. On each iteration, (i) we change one random entry of the adjacency matrix , 2(d), and 3(d) for the entropy evolution during the performance-based training under other input types). Neither assortativity nor clustering shows significant changes over the training period.To uncover the reasons for criticality, we examine basic task-independent information-theoretic properties28 and Parkinson disease assessment using Parkinson’s Disease Classification Data Set40. The latter one contains various acoustic characteristics of phonation of the vowel ’a’ recorded from Parkinson’s disease patients to extract clinically useful information for the disease diagnosis. The results are summarized in Table The proposed structural plasticity mechanism for the STO-network leads to the qualitatively same results in different tasks. To showcase this, we compare the interconnection topology characteristics and criticality signatures of the trained networks under the performance feedback for two additional tasks: handwritten digits (’0’–’9’) classification from the MNIST datasetIt should be noted that the interconnection topologies of the trained networks are different see Fig. . Finally41.Although the macroscopic characteristics  of the interconnection graph for the trained network of harmonic oscillators are similar to the corresponding characteristics of the trained STOs, the number of links and the power law distributions are different. Reasons for these differences are as follows: (i) The initial all-to-all networks are initialized at different distances to criticality and, therefore, the network of harmonic oscillators looses more links in the course of training compared to the network of STOs. (ii) There are much wider deviations of the power law fitting under the external inputs for the network of harmonic oscillators compared to the power law fitting deviations for the STOs. This is due to the same scaling of the input signal used for both STOs and harmonic oscillators, whose dynamical properties are different. As a result, the applied input has a stronger influence on the overall behavior of the network of harmonic oscillators than on the behavior of the STOs. This influence can be balanced, for example, by embedding internal adaptation mechanisms into the input nodes that self-adjust signal intensity depending on the internal dynamical characteristics of the nodes. Although the input scaling analysis and mechanisms are not in the scope of the current paper, they are definitely important ingredients for the neuromorphic reservoirs’ design42, although this property was not a part of the objectives of the employed structural plasticity mechanism. We also identified the interconnection graph’s entropy  as an essential ingredient for the classification task performance and network’s criticality.In this paper, we proposed an evolutionary approach for the structural plasticity that relies solely on the task performance and does not contain any task-independent adaptation mechanisms, which usually contribute towards the criticality of the network. As a driver for the structural plasticity, we used a direct binary search guided by the performance of the classification task that can be interpreted as an interaction of the network with the environment. Remarkably, such interaction with the environment brings the network to criticality16. It has been shown therein that the biologically inspired plasticity and homeostasis mechanisms responsible for the learning abilities can give rise to criticality signatures when driven by random input, but these break down under the structured input of short repeating sequences. Moreover, the necessity of sufficient noise for the occurrence of the criticality signatures degrades the model’s performance in simple learning tasks. In contrast, the emergence of the criticality signatures and their persistence under the noise-free periodic structured inputs have been shown for the trained STO-network considered in our paper. Our findings refute the generality of the hypothesis that the structured input breaks down criticality signatures16 and challenge the conjecture that criticality is beneficial for complex tasks only12.Signatures of criticality have also been found in a class spiking recurrent neural networks used for spatiotemporal pattern learning through a combination of neural plasticity mechanismsBeyond those results, here we have shown for the first time the criticality signatures arising in a network model designed for learning under the direct binary search rather than any combination of activity-dependent plasticity mechanisms. The paper also showcases criticality signatures in networks of spin-torque oscillators for the first time.Despite all the current progress, the relationship between criticality and learning in bio-inspired neural networks is far from completely understood. The main challenge we see is in understanding mechanisms which translate external stimuli generated by the task performance into the language understandable for a network. Our paper employs the rather inefficient mechanism of binary search that can be possibly substituted by more sophisticated ones, e.g., genetic learning algorithms, or moved even further down to the network’s self-organization level. At this stage, it is however not clear whether the bio-inspired synaptic and structural activity-dependent plasticity are sufficient mechanisms to realize adequate reactions of artificial neuromorphic networks to the environmental stimuli, or any kinds of mutations and evolutionary adaptations are necessary for this.z(t), Python scipy library. For the purpose of further analysis all trajectories are discretized in time with the time step 34. As a measure for the synchrony in network (z(t), and dt. Figure Numerical solution  to  is obtaiFor either real or imaginary part of the trajectory segment of length entclass1pt{minimaAs a signature of criticality we use probability distribution of cluster sizes. To approximate this distribution, we run a simulation and split the discretized time series of oscillators states into bins of length ure Fig. b.28 and Parkinson’s Disease Classification Data Set40. Every MNIST-digit is the i-th row of the corresponding MNIST digitl, in which the value of every pixel of the i-th row is plugged sequentially for the equal amount of time  or Gated Recurrent Units (GRU) can be suitable for this role, however, this extension is out of the scope of the current paper. Another interesting direction is the usage of readouts implemented in CMOS circuits for the synchronization detection in networks of coupled oscillators45.The readout consists of two fully connected feed-forward layers of artificial neurons: the input layer that receives the signal from the reservoir, and the output layer, with the softmax activation function, containing two, three, or ten nodes with the classification probabilities depending on the task. The input layer of the readout consists of Entropyk. Then, the entropy can be calculated according toAssortativityr that measures the tendencies of nodes to be connected to other nodes that have similar in- and out- degrees as themselves. Following46, four types of assortativity can be introduced: i, and i. The average values of the mentioned terms over all edges of the network are denoted by Clustering coefficient is the average of cluster coefficients T(u) is the number of directed triangles through node u, u, and u, i.e., the ratio of the number of edges in both directions to the total number of edges attached to node u47.To evaluate changes of the interconnection topology in course of the training process the following task-independent graph- information-theoretic measures are used: (i) Supplementary Information.


The surface condition of roadways has direct consequences on a wide range of processes related to the transportation technology, quality of road facilities, road safety, and traffic noise emissions. Methods developed for detection of road surface condition are crucial for maintenance and rehabilitation plans, also relevant for driving environment detection for autonomous transportation systems and e-mobility solutions. In this paper, the clustering of the tire-road noise emission features is proposed to detect the condition of the wheel tracks regions during naturalistic driving events. This acoustic-based methodology was applied in urban areas under nonstop real-life traffic conditions. Using the proposed method, it was possible to identify at least two groups of surface status on the inspected routes over the wheel-path interaction zone. The detection rate on urban zone reaches 75% for renewed lanes and 72% for distressed lanes. Due to continuous improvements in technologies applied to transportation systems, from autonomous driving to e-mobility, it is more common to find sensors embedded in vehicles that allow continuous scanning of the road environment. One of the crucial elements of this environment is the superficial pavement condition which is closely related to traffic safety and rolling noise emissions ,2.The Tire-Pavement Interaction Noise (TPIN) is a complex phenomenon which depends on a number of parameters, such as tire characteristics , driver influence , environmental conditions, surface contamination , and the parameter on which the present work will focus, the superficial asphalt condition ,4,5,6.Even though noise reduction properties of low-noise pavements decrease over time, resulting in heterogeneous tire-road noise generation along the route , the roaTraditionally, the identification and evaluation of asphalt defects has been performed by labour-consuming visual inspections. These activities have some subjective assessments due to experience and the assessor’s judgment of the catalogued defects. Other approaches, such as coring tests, are also available as mechanical techniques . These mConversely, new remote sensing methodologies can address some of these shortcomings; for example, analysing large road sections is possible while handling large amounts of data. These methods include tools such as ground penetration radar, infrared thermography , laser sIn particular, the acoustic-based methodology exploits the information from the rolling noise measurements,  , which ivehicles .The objective of this paper is to present an acoustic-based approach for unattended discrimination of the changes of the road surface conditions over the wheel-track path through the analysis of the tire-road noise and unsupervised machine learning (UL). Thus, the resulting clusters are presented accordingly the road sections of similar asphalt status. Although the obtained noise measurement data would also gather information about other aspects present during rolling, such as acoustical comfort inside the car, tire-pavement adhesion mechanism, vibrations, and aerodynamic phenomena, their identification is out of the scope of the present work. This study addresses the influences of driving conditions into the analysis, such as speed and acceleration of the vehicle, and several types and degrees of damage could be found on a typical route. However, the design of a dataset with all distress types for supervised classification could be a difficult and time-consuming task. Hence, UL algorithms will be included in superficial pavement monitoring of three road circuits. Although some standards for rolling noise measurement in near field have been implemented, such as CPX and OBSI, the data analysis from several sensors needed for their configuration can be time and computationally consuming tasks for other purposes added to policy noise level reports. Behind-The-Tire (BTT) measurement setup is a practical option, which allows to analyse the relative influence of pavement texture on tire-road noise through a simpler electroacoustic setup configuration ,23. The Most of the literature in this field, exploiting the advantages of supervised machine learning in surface-asphalt detection using the frequency features of TPIN measurements, reports high accuracy on classification tasks, for instance the surface-road type, materiality class or wet-dry condition. The supervised method has been effective for these tasks, although it requires a labelled database with enough examples of all possible deterioration classes to identify them on the actual route. However, the road upper layer to be inspected might have many forms and degrees of deterioration, and several of them can even be unknown; especially, when the inspection is over long distances. In addition, the difficulty of collecting this complete database is the reason for the decision to use the unsupervised approach for monitoring the superficial asphalt condition along the wheel-path interaction zone.This paper is organized as follows: The implemented method consists of four main steps, as depicted in Firstly, the data are collected from three different types of input, such as the recordings of the TPIN signal, speed, and acceleration from the vehicle’s electronic control unit via the On-Board Diagnostics (OBD) port, and coordinates of the surveyed routes from a smartphone GPS. Afterwards, the dataset construction is performed through sound signal processing by frequency domain transformations, feature extraction, and driving condition dependences. The conformed dataset can be sliced by geographic areas or specific streets to deepen distress road identification.Next, the similarities between the acoustic footprint data are clustered using UL algorithms. Finally, the available data from GPS were used to visually represent the detected differences in road conditions. Since the GPS of the mobile phone has an accuracy of 4.9 m, the geolocation data is not intended to determine the location of a specific type of deterioration, but rather a generalized inspection of the road surface.As far as possible during the data recording rounds, some constraints of the experiment were conserved to reduce variability factors and facilitate the subsequent interpretation of results. Only one class of tire tread pattern was installed on the experimental vehicle, which is depicted in The TPIN data acquisition campaign was carried out during fall season under stable weather conditions, i.e., average air temperature maintained between 22–25 °C and humidity of 40–50%. Only dry asphalt was considered in the experiments.Sound pressure signals were collected by using a unique instrumented diesel passenger car, equipped with two 1/2” type-1 microphones and BSWA-MA231 preamplifiers, according to the BTT technique. Although there is not an international standard for this simple instrumentation technique, it offers certain advantages for inspections. For applications in opportunistic scenarios, the sensor configuration is performed with the minimum disturbance to the vehicle structure, no additional trailer is required, and the microphones remain hidden during inspections.The microphones were connected to an NI-9234 Data Acquisition (DAQ) device with sample rate of 51,200 Hz. The DAQ software is controlled by a portable PC on board. Although the measurement tasks were performed with two microphones, one on each rear wheel, the data captured by each microphone were processed independently. This means that the instrumentation setup simulates two vehicles of the same type making the same run with only one BTT microphone.Simultaneously, an ELM327 interface connected at the car OBD port sends the driving parameter data to a smartphone via Bluetooth at 1 Hz. Because the PC was connected to the mobile network through the Wi-Fi smartphone portable hotspot, their clocks were matched to enable recordings using a single clock.The method seeks to recognize the differences between the sounds coming from different road conditions when the tire rolls over them. In this context, the information that makes the sound comparison between the signals is extracted from the TPIN registers and then constitutes a data set. The selected features are then expected to contain the relevant information of the input data.The first step is to pass the audio data through a cut-off high-pass filter from 20 Hz, which removes the unrelated signal to rolling phenomenon at lower frequencies, such as sources of mechanical vibration and nonaudible acoustical data. Then, the audio data were split into 1-s nonoverlapped frames. This frame size allows matching between the OBD and GPS data sampled at 1 Hz with the corresponding TPIN frame trough date-time data. A Hamming window was applied to reduce the discontinuity effects at the boundaries of each chunk before the transformation in the frequency domain . Each auIn general, literature reports that the sound radiation of TPIN mechanisms is prominent below 4000 Hz, and it is closed related with parameters of both the pavement  and the wheel  ,40. MoreAlthough TPIN noise characterisation is most commonly performed in the frequency domain through the 1/n-octave bands ,26,29,35Indeed, the triangular filter bank was typically selected for audio processing in tasks related to the non-linear perception of sound by humans ,43. The In accordance with the frequency behaviour of the TPIN described above, the triangular filter bank was designed to extract the data at frequencies below 4000 Hz.On the whole, each signal frame passes through a set of triangular filters  on the frequency extracted sound features, a linear multivariate model was implemented. The data for the model were recorded under a wide speed range (35 to 60 km/h) on urban roads with the same vehicle, set of tires, and driver in order to minimize the data variability see . The dat2] of the vehicle registered during the trips are considered for corrections of noise level on each frequency band  =70 km/h . Althougoughness  other vaoughness .(1)L′T of the data. These are the peak level Once completed, the dataset can be filtered by geographic regions for further local and specific analysis. The available GPS data tracking makes it possible to select the amount of data for the next tasks by a geospatial query. Each zone has a very specific pavement structural capacity and corresponds to the same administration. The similarity in the initial asphalt mixture type within each geographical section has been assumed. This consideration would facilitate further analysis since the variability of the noise signal can reach 10 dB due to differences in pavement texture . TherefoAfter data segmentation, the available data were pre-processed to ensure that each feature iation 1 .(2)xstdNext, the transformation of high-dimensional data into a meaningful representation of reduced dimensionality is included through dimensionality reduction, also called the feature reduction task. As a result, feature reduction facilitates and improves the cluster discrimination, classification, visualization and compression of high-dimensional data .In this regard, unsupervised nonlinear dimensionality reduction was applied by the t-Distributed Stochastic Neighbor Embedding algorithm (t-SNE) introduced by Maaten and Hinton . t-SNE aThe above algorithm allows us to preserve the local structure of the data through pairwise similarity based on the Euclidean distance, while preserving much of the global structure of the data ,50,51. TBecause there are no a priori labelled classes about the current condition of the inspected streets, UL may be able to explore the similarity and separability criteria among the observations in the data set. Then, two types of clustering techniques with different approaches were applied, but similar results were obtained: hierarchical clustering and probabilistic clustering. In many cases, the first attempt at grouping data set results in a clustering that may not be the most effective, so multiple clustering configurations must be studied .The hierarchical approach does not apply a random initialization. Particularly, the data pooling algorithm starts with each observation as a cluster itself. These smaller clusters merge into larger ones by a series of successive fusions of the observations by minimizing the distance between clusters criteria . The resed level .Alternatively, the probabilistic Gaussian Mixture Models (GMMs) technique form ellipsoidal-shaped clusters based on the iterative expectation-maximization algorithm. It provides the basis for the Bayesian Gaussian Mixture Model (BGMM) ,55,56.The approximation of the minimum number of clusters could be established by the “elbow diagram”. The elbow method helps to determine the optimal number of clusters mization . The infAn example of the selection of the number of clusters The representation of all the passes over the inspected route is carried out by a basic geo-processing stage. In this process, the cluster type assigned to each element of the dataset was plotted on a map by GPS tracking data. However, after the cluster assignment task, the elements per cluster class might appear during normal driving with a certain level of randomness because the area of the asphalt on which the wheel contacts is not always the same. Moreover, defects may not be reached by the wheel in a single pass, especially in deteriorated areas with very local defects; in that case, the deteriorated area of the road upper layer is not registered just because the defect has not been passed over. Therefore, the possibility of detecting defects increases with the number of records belonging to the same street segment.To address this drawback, the travelled road is first segmented, and then each element of the clustered dataset is related to the nearest piece of the road line. Road segments were set every 20 m for urban roads. This segment length allows us to include enough cluster elements in each segment when velocities are not constant. Finally, the segment label is assigned by the mode ous work .(3)Labenot assigned will be present in the mapping report as a “n/a”. In the same way, the segments without available data (due to stop-car or low-speed events) will be present as “n/d”, from legend no data.When a segment of route is classified as multimodal, the label legend The performance of the introduced acoustic-based methodology for surveying road condition was tested on urban roads see .The first experiment was carried out in the Reference-route. The benefit of applying the proposed methodology on this route is to evaluate the clustering performance on a controlled and known road environment. The road has two different types of superficial conditions  which inFor the feature reduction task, the t-SNE algorithm was fitted to respond closer to the known condition of the reference surface road, i.e., t-SNE components (n = 2) and (perplexity = 30). This last one is a parameter that means (loosely) how to balance attention between local and global aspects of the data . Figure Frequency features are showed in The cluster Subsequently, the geographic processing of the instances assigned to each cluster was carried out through the stage described in The second group of data refers to a single avenue in urban environment. It was known that maintenance work had been done previously, and a short section of this road had been completely repaved. Thus, the present experiment was motivated by the evaluation of the model’s ability to identify both distressed and renewed areas. For this experiment, the resulting number of clusters was Subsequently, the instances assigned to each cluster could be displayed in their original domain, i.e., according to noise levels by frequency bands and by overall noise levels. Although the clustered noise spectrum does not show more than 3 dB difference between the average levels of each cluster  and the The reason of this last experiment was to test the identification of the condition of the wheel-path interaction zone among several neighbouring streets. These streets, with similar characteristics, such as vehicle density flow and travel speed, do not necessarily have the same asphalt condition, but in general, the initial installed materiality could. Besides, cluster Mapping report depicted in After, a further visual inspection of the studied street group was carried out. The area assigned to A straightforward interpretation of the cluster correspondence of a road-surface condition is possible if the road surfaces along each route have similar materiality at the time of installation. This condition is assumed for the roads considered separately for each case study. From this point of view, the cluster with higher amplitude of acoustic footprint can be linked to the old areas, while new areas would belong to a lower amplitude cluster. Details of the visual inspection and the comparison with cluster estimation are presented at the end of the section in A comparison is made between interpretation of the resulting clusters and visual inspection. The term “estimation” is used since there are no previously assigned class labels to compare the detections. Nonetheless, it was possible to obtain the length of the road sections of each circuit The dominant clusters on each route section agree with the discriminated wheel-path condition obtained from the cluster interpretation and with the assigned condition by means of visual inspection. Then, the correct identification of each route section is estimated by the ratio between the dominant cluster length The results show that the surface discrimination for two pavement condition categories was obtained from the clusters within the reduced feature space. In the roadway of the University campus, the detection of both renewed and distressed sections shows more separable resulting clusters in the feature space , and the a priori knowledge of the superficial roadway status allowed a high estimation of the real condition of the road. In particular, the renewed section length was detected up to 96% and the distressed section length up to 76%.On the other hand, the system is less effective in road environments with greater variability. These are the cases of the urban avenue and the urban circuit, where the presence of a third cluster is related with external urban factors instead rolling noise. Therefore, this cluster was not considered for the estimation of the road surface condition. In addition, the classes proposed by the number of clusters are closer to each other into the feature space . As a result, the discrimination rate of distressed areas between 60% and 72% was achieved for urban roads environment.The results of the automatic discrimination of the asphalt surface condition over the wheel-path interaction zone have been compared with the conventional visual inspection technique, which is performed on reference points of the road. In addition, local road evaluations were generalized to make them comparable with the results of the large length inspection attained from the proposed method. As a result, the discrimination rate presented in For all urban-roads study case, the corresponding Due to the location of the microphones, BTT method depends on the wheel path, which is the contact area between the tire and the road during normal traffic. Therefore, the assigned group is not necessarily the same for all passes neither for both tires at the same time. However, with increasing passes, the detections show homogeneity across road segments, and the subsequent geoprocessing step allows smoothing the cluster assignment by a majority vote summary.This acquiring system proposed allows to process signals from two microphone positions independently. This suggests that two cars with similar engine and tire characteristics and with only one instrumented tire, would provide similar results. However, the influence of driver and vehicle variability must be considered in the clustering interpretation. This can lead to collecting information collaboratively in the horizon.This method, based on the clustering of the acoustic features of rolling noise, provides an unsupervised alternative for the discrimination of the asphalt surface status along the trajectory followed by a vehicle wheel. Notwithstanding, certain possible improvements, such as the selection of the vehicle and the placement of the microphone for data acquisition, should be considered in future works.The placement of the microphone in the wheel housing produces signals that are not “purely” from the interaction between the tire and the road. In fact, there could be other types of sounds, both from the outside (when driving on busy roads) and from the vehicle itself (noise from the exhaust pipe or from the ventilation of the wheels themselves). The presence of these other sounds could negatively affect the classification process by increasing the background noise and masking the signal containing the contact surface information. However, most of these components are not sensitive to the type of road surface the vehicle is driving on. Therefore, the filtering, feature extraction, and dimensionality reduction processes have allowed to minimize their influence, as their acoustic fingerprints are separable. These non-overlapping classes were also observed in experiments with supervised classifiers with similar microphone placement ,25,32,58The selection of the speed of reference in the linear model could be improved in future works. Although the literature reports the linear relationship of the TPIN levels in dB and d by ISO  and CNOSThe asphalt discrimination rate in urban scenarios could be improved with the application of this acoustics-based method using electric vehicles, due to the lower crossing speed (<35 km/h) as reported in .The detection of road surface quality by unsupervised learning has been evaluated by comparisons with applications of supervised classification metrics  . On the Comparison of the signals acquired by two or more microphones  could be included in future research steps. This would improve the detection of wear of the pavement, such as potholes, cracks, and bumps. A shorter-time window could also be included for impulsive noise events processing.The main contribution of this paper is to present the performance of asphalt surface characterization based on the TPIN signal produced along the wheel path. This unsupervised technique allows the automatic discrimination of a small number of clusters related to predominant asphalt superficial condition.Moreover, this method takes advantage of some improvement opportunities detected in previous related works, such as the inclusion of driving condition influences and the detection of the differences over the asphalt length without a catalogue of damages.The superficial condition of the studied roads is closely related to the rolling sound footprint and TPIN amplitudes in the frequency and time domains. These relations allow the interpretation of the clustering results.An advantage of the application of UL over supervised techniques is the possibility of detecting areas with homogeneous rolling noise footprint without knowledge of the current road status. These localized zones are related to the homogeneous condition of the road status (deteriorated or not). The results were compared throughout further conventional visual inspections.The implemented methodology has allowed the automatic and continuous discrimination of the state of the asphalt surface along the wheel trajectory. From these results, the surface discrimination of the wheel path on single lane roads can reach 92 % . Multiple observations allow to evaluate better the TPIN from a narrower wheel track area.Whereas in the case of the urban scenarios of roads with more than one lane, the discrimination rate decreases up to 57%. This because of the discrimination system must deal with different variables such as the speed limit, traffic flow, and a wider inspected area. Especially, when the vehicle changes lanes during each trip as it could happened during naturalistic driving behaviour.The present acoustic-based method allows the inspection of road facilities with nonstop traffic inspections, non-destructive approach, and opportunistic scenario.The mapping report contributes to pavement management through visual information. The surveyed areas producing different TPIN footprints assist in road maintenance planning, traffic noise mitigation activities, road condition warning reports.In the present research phase, only corrections due to driving characteristics (speed and acceleration) were included. Future developments are also expected to incorporate corrections due to the variability of other conditions during driving, such as vehicle load, driver behaviour, tire inflation pressure, tire tread pattern, temperature, humidity, vehicle engine, pavement materiality, etc.The technique could be improved for the detection of punctual defects such as potholes or manholes through refined time windowing in the signal processing and spatial resolution in the geoprocessing.However, the consideration of these new conditions will surely imply complexity in the clustering interpretation.Three kinds of scenarios were considered to check the performance of the methodology such as a reference route, one urban avenue, and one urban street circuit. All these pavements possessed same characteristics of regular aggregate compositions, i.e., no one of them were neither open-graded pavements, rubber asphalts or poroelastic surfaces. These measurements were limited to weather conditions without rain and dry asphalt. The conclusions of the study are as follows:


Given the complexity of international construction projects (ICP), risk management difficulties commonly cause cost overruns. This paper analyzes the problems of risk interdependence and subjective ratings in the application of the traditional risk assessment model in ICP. To solve the above problems, this paper proposes a risk assessment model for ICP that considers risk interdependence and obtains references from similar projects. The model applies the Decision-Making Trial and Evaluation Laboratory (DEMATEL) to determine the risk interdependence and its contribution to the overall project risk. Moreover, this model recalls the risks, probabilities, impacts, and risk events records of similar historical projects as the necessary inputs, thereby addressing the issue of subjectivity. An integrated framework is provided to identify, analyze, and prioritize ICP risks to incorporate risk interdependence into the risk management process. Finally, this paper demonstrates and validates the proposed model through a real project. The proposed model is useful for international construction companies to support project selection and bidding decisions in the early stage of ICP. International construction projects (ICP) are more complex than domestic construction projects because of the transnational participants, diverse currencies, different cultures, unfamiliar standards, and unpredictable nature of disputes in different language versions of the contract. The complexity of ICP leads to risk management difficulties  that mayPrevious studies have proposed a comprehensive checklist of risk factors, quantified the probability and impact of the risks using fuzzy set theory, weighed the importance of each risk by the analytic hierarchy process (AHP), and finally completed the prioritization of individual project risks or rating of overall project risk. However, these methods or techniques focus on assigning subjective ratings to individual project risks, resulting in two challenges.The first challenge is the ignorance of risk interdependence. Previous studies mostly modeled project risk as a group of parallel risk factors –7 under The second challenge is subjectiveness. In practice, decision-makers inevitably rely on personal experience, intuitive judgment, and risk preference to judge the risk probability and impact. It is practicable in domestic construction projects because of the similar project environment. However, the international construction market is diverse and changeable, which increases the difficulty of identifying comparable projects based on personal experience. Occasionally, this leads to vagueness and inconsistencies during risk assessments for ICP. If the decision-maker can recall historical projects in an extensive organizational repository and conduct risk assessments based on this documented information, such a problem can be avoided.Therefore, this paper proposes a risk assessment model for ICP that considers risk interdependence and obtains references from similar projects. This model applies the Decision-Making Trial and Evaluation Laboratory (DEMATEL) to determine the risk interdependence and its contribution to the overall project risk. Among a great variety of multiple-criteria decision-making (MCDM) methods, DEMATEL is able to visualize the complex interdependence among criteria . Hence, The remainder of this paper is organized as follows. The “Literature review” section reviews the literature on ICP risk management, risk interdependence, and DEMATEL. The “Risk assessment model” section describes the proposed model considering risk interdependence. The “Case study” section presents a case study to demonstrate the model application and validate the effectiveness of the proposed model. Finally, the “Conclusions” section concludes the paper by summarizing the contributions and limitations of this research and providing suggestions for future research.The application of MCDM methods for risk assessment in construction projects has abounded in literature, as shown in On the other hand, recent studies have indicated that risks in ICP are interdependent and such interdependence has an influence on project outcomes. They identified the major risk interdependence  by statistical techniques, such as structural equation modeling –60, factOnly a few studies have made efforts to analyze the influence of risk interdependence on project outcomes. Several authors have applied the Bayesian belief network (BBN) to assess project risk –66. For Given the limitations of previous studies, this paper adopts the technique of DEMATEL to address the challenges associated with risk interdependence. DEMATEL, first developed by Gabus and Fontela , is a syFurthermore, this paper adopts the idea of case-based reasoning (CBR) to avoid subjectivity. CBR solves new problems by referring to the proven outcomes of similar situations rather than explicit formulas or predefined rules. Several researchers , 75–77 hIn summary, this paper tries to propose a risk assessment model for ICP that considers risk interdependence by DEMATEL and uses references from similar historical projects, thereby filling the previously mentioned research gap.The risk assessment model for ICP that considers risk interdependence is shown in the solid line in As defined by PMBoK , each prThis paper classifies individual project risks into four levels: nation/region, trans-nation, organization, and construction, as shown in E similar projects that are sorted as {SP1, SP2,⋯,SPE} according to the Global Similarity Score (GSS). Suppose that SPi(IR) refers to the set of risks of the ith similar project; then, CP(IR), which refers to the set of risks of the current project, can be identified as shown in Eq ..3). In pPij and Iij refer to the probability and impact of the jth risk in the ith similar project, respectively; then, P(IRj) and I(IRj) that refer to the probability and impact of the jth risk in the current project can be defined as shown in Eqs  and andPij an in Eqs .EL) of the jth risk can be defined as the product of occurrence probability and direct impact, as shown in Eq . Suppose that there are T-1 identified risks. If we consider the overall project risk as another separate element in the system, we can build a matrix of risk dependence with T rows and T columns. The element rmn in the mth row and the nth column is determined by counting the occurrence of nth risk caused by mth risk in similar projects, as shown in Eq (The first step is to establish the direct risk dependence matrix (wn in Eq . The lasN). Suppose Rmn is the normalized value of rmn; then, it can be calculated by Eq , as shown in Eq , which is defined as the impact of a risk on the cost overrun through its risk interdependence, and it can be understood as the added impact of a risk on cost overrun through risk interdependence under the same expected loss. Moreover, DEMATEL further calculates the depending degree, the depended degree, the centrality degree, and the cause degree. The depending degree (D) indicates the impact of corresponding individual project risk on other individual project risks, and it corresponds to the sum of each row except the last column. The depended degree  by Eq  by Eq . SupposeRE is an indicator of the relative risk magnitude of a project when compared with others.The overall project risk can be described as the variation range of some project performance, such as cost and duration. As it increases, the probability of achieving the overall project objectives decreases. For each OCP, this paper adds the value of the significance index to quantify the overall project risk exposure (RE), as shown in Eq . RE is aRisk response refers to implementing a set of response measures (or actions) to reduce the adverse impact of the identified risks on project objectives. This paper attempts to formulate the response measures for the current project by citing and improving the response measures from similar historical projects.Consistent with risk identification, the response measures are also identified as the combination of response measures of similar historical projects. These measures would usually be practical and effective since they have been previously implemented before. In this way, decision-makers need not develop response measures from scratch. Moreover, if necessary, decision-makers should improve the identified measures to better adapt to any unique situation of the current project according to their knowledge and experience.Finally, decision-makers should monitor the occurrence of risks during the life cycle of the OCP and report all the information on the risk management activities and project outcomes for future use.This section conducts a case study to demonstrate the application of the proposed model and validate its effectiveness in considering risk interdependence.The Kumasi-Bechem railway is a railway construction project located in the Republic of Ghana that has been contracted by a leading Chinese construction company. The contract amount of the project is 500 million dollars, the construction period is 24 months, the payment type is a lump sum contract, and the delivery system is engineering procurement construction (EPC). Although the company has not implemented construction projects in Ghana before, it has extensive construction experience in other countries and regions. Therefore, this company has constructed a case base including 102 international railway construction projects.Based on the above conditions, this paper retrieves similar historical projects in the case base by selecting input variables and collecting relevant data, as described in a previous study . Four hiEqs –6) are  are 6) aThen, this paper counts the occurrence of risk dependence in the four similar projects. For each project, the case base retains the occurred risk events and the risk network that reflects the causal relations among these risk events. Such data was collected through post-project reviews with project managers. The risk networks of the four similar projects can be seen in R). and then calculated the normalized matrix (N) and the risk interdependence matrix (D) according to Eqs R). and tAfter analyzing the individual project risks and their interdependence, this paper calculates the significance index of each risk according to Eq . Table 7As shown in RE) for the target project is 0.03907 according to Eq  and interdependence contribution (indirect impact). Hence, it can prioritize the individual project risks and evaluate the overall project risks with consideration to risk interdependence. This step is very important for ICP because these projects tend to have more complex risk interdependence. Although some risks events did not cause cost loss directly, they may lead to the occurrence of other risk events and cause cost loss indirectly. In summary, the proposed model can support decisions related to risks in the early proposal stage of ICP.Despite the advantages of the proposed method, there are still limitations to the conclusions. First, the proposed model relies on the support of an extensive case base that contains a wide variety of historical projects with risk events. The case base used in this paper is collected from a Chinese construction company, hence should not be directly generalized to other contractors. Instead, the contractors should construct different case bases individually following the proposed process. If there lacks such a case base, the subjective ratings from experts are required as the input to the model. Second, this paper applies the causal relations among the occurred risk events in similar projects as the input of DEMATEL. Given that such data were collected from post-project reviews with project managers, certain data deviations remained due to various reasons, such as the loss of documents, unwillingness to disclose mistakes, and risk preference of different managers. Finally, this paper emphasizes the identification and analysis of risks specific to international construction. Hence, the proposed model is useful for international construction companies to support project selection and bidding decisions in the early stage of ICP. However, it should not be directly generalized to the middle and late stages due to the differences in key points of risk management. The future work of this paper is to develop a computer-based tool to realize the proposed models. In this way, this paper could provide better decision support for international construction companies.S1 Fig(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 Fig(TIF)Click here for additional data file.S4 Fig(TIF)Click here for additional data file.S1 File(XLSX)Click here for additional data file.


Cancer drug discovery is currently dominated by clinical trials or clinical research. Several potential drug candidates have been brought into the pipeline of drug discovery after showing very promising results at the pre-clinical level and are waiting to be tested in human clinical trials. Interestingly, among the potential drug candidates, a few of them have targeted transcription factors highlighting the fundamental undruggable nature of these molecules. However, using advanced technologies, researchers were recently successful in partly unlocking this undruggable nature, which was considered as a ‘grey area’ in the early days of drug discovery, and as a result, several potential candidates have emerged recently. The purpose of the review is to highlight some of the recently reported studies of targeting transcription factors in cancer and their promising outcomes. Transcription factors (TFs) are a group of proteins required for gene expression and more precisely in the regulation of gene transcription. In metazoan, they are localized in the nuclear microenvironment in infinitesimally small quantity and participate in the regulation of gene expression in the presence or absence of appropriate signals. DNA binding and transactivation domains are found to be the two most exclusive parts of any TF. Besides that, TFs often possess domains responsible for interacting with proteins to form homo and heterodimers. Most of the mammalian TFs are involved in the regulation of multiple genes and these diverse functions are determined by their interactions with cofactors associated with the pathway of gene regulation. Broadly, mammalian TFs are classified based on the signature domains such as zinc finger, basic helix-loop-helix (bHLH), basic leucine zipper (bZIP) domain, and each signature domain plays a key role in determining the three-dimensional structure of the protein associated with the functional activities. The expression and functional activities of TFs are tightly coupled with cell proliferation, differentiation, and apoptosis. The uncontrolled expression or functional abnormalities of TFs leads to the initiation or establishment of diseases like cancer. Almost 300 TFs and associated cofactors are identified, based on the analysis of cancer genes, and considered as a major contributor to the development of various types of cancers .Developing novel therapeutics in cancer is continuously evolving and potential novel targets are emerging routinely with the intention to reduce the mortality and the suffering of cancer patients. The extreme adaptive power of cancer cells, generations of secondary tumours through metastasis and developing drug resistance during or after responding to the primary therapy pose major challenges to researchers in controlling the disease progression and subsequently the saving of lives.Development of cancer is strongly related to genetic abnormalities including TFs . Along wPerhaps the most fundamental part of the identification of a new target is to understand the regulatory mechanisms and the key players that control the proliferation of cancer cells. Over the last few decades, researchers have applied several cutting-edge cells and molecular biology techniques to reveal the network and pathways of the regulatory mechanisms that drive the uncontrolled proliferation of cancer cells. As a result, we have more comprehensive pictures of the several regulatory networks than ever before that control the gene expression, signal transductions, and several other fundamental cellular processes directly or indirectly associated with the proliferation and differentiation of mammalian cells. A detailed fine print of these regulatory networks is a cornerstone for the identification of novel targets in drug discovery. One of the most fundamental questions in any form of drug discovery is the identification and validation of targets. Particularly in the case of cancer, it is more complicated because targets are predominantly endogenous biomolecules engaged with many essential cellular activities. Cancer cell proliferation, invasion, metastasis, angiogenesis, and apoptosis are regulated by multiple hyperactive and/or modulated cellular networks that include extracellular signaling networks, transmembrane receptors, intracellular protein kinases, and TFs . It is cSeveral biomolecules such as enzymes, receptors, membrane transporter, and TFs are considered as potential targets in drug discovery. However, the evidence is in the favour of the fact that some biomolecules are considered as better targets than others though they are potentially very similar. For example, enzymes are considered as a more-preferred target than TFs, mainly because many successes were reported in manipulating the activities of an enzyme by designing an inhibitor rather modulating the activities of a TF. For example, phospholipase C (PLC) is one of the enzymes that work in coordination with other signal transduction pathways such as phosphatidyl inositol-3 kinase (PI3 kinase), myosin activated protein kinase (MAP kinase) to control cell proliferation, differentiation, and angiogenesis. Among the six isozymes of PLC, PLC-γ1 is one of the most studied enzymes, which plays a major role in tumorigenesis  and becaIn somatic cells, gene expression is controlled through the modification of histones via acetylation and de-acetylation by the group of enzymes known as histone acetyltransferases (HAT) and histone deacetylases (HDAC). The activities of HATs and HDACs are found to be unbalanced in cancer cells. Depending upon the types of cancer, HDAC inhibitors have shown to induce cell cycle arrest, differentiation, and reduced angiogenesis. HDAC inhibitor vorinostat, romidepsin, and panobinostat are currently approved for the treatment of T-Cell lymphoma and multiple myeloma . On the At the beginning of the era of targeted drug discovery, NF-κB and p53 were considered as the two very promising candidates due to their critical, multifunctional roles in several cellular activities as well as in the development of tumorigenesis. Till today, close to eight hundred inhibitors of NF-κB have been developed capable of inhibiting DNA binding activity, transactivation, and the expression of this TF. However, none of these inhibitors were found to be clinically effective , 8.Since p53 is a tumour suppressor, therefore, the reactivation of this protein in cancer cells were proposed to be effective in controlling the proliferation of cancer cell or tumour. Activation of p53 is coupled with the dissociation of this protein with another protein, mouse double minute 2 homolog (MDM2). Small molecule inhibitors such as nutlins, spirooxindoles, benzodiazepinediones, and piperidinones found to be very effective in blocking p53-MDM2 interaction, but in clinical trials, patients with liposarcoma and leukaemia, these inhibitors generated severe side effects and they were withdrawn from the trials . ObservaMany avenues of targeting TFs have emerged recently  which macis- or trans-acting factors to the regulatory element of the gene that encodes the TF. In other cases, it is activated by a new recombinant transcriptional activator generated through chromosomal aberrations. Such recombinant transcription super activators modify the expression of several genes related to cell proliferation. Significant examples of this kind of transcriptional upregulations or miss regulations [Increased expression of TFs is observed to be associated with the development and maintenance of several malignant cancers. In many cases, this elevated level of expression is due to upregulated transcription activity or due to the binding of ulations  have beec-Myb expression is regulated by estrogen receptors (ERs) [Bcl2. Several in vitro and in vivo studies, carried out by our group, clearly demonstrated that the c-MYB expression is absolutely required for the proliferation of breast cancer. Therefore, downregulation of ER activities with an anti-estrogen drug like tamoxifen is considered to be one of the accepted ways of treating estrogen-positive breast cancer. However, the laboratory of ours and others investigated to develop an alternative therapeutic model in estrogen-positive breast cancer because more than one-third of breast cancer patients develop resistance to anti-estrogen therapy during the course of treatment [in vivo followed by clinical trials.TF c-MYB activates many cell proliferation specific genes. The half-life of the c-MYB mRNA in the somatic cells is very short indicating that the transcription is highly regulated at a particular stage of the cell cycle. In human breast epithelial cells, rs (ERs) . Analysirs (ERs) . This hireatment . Our anareatment , 16 estareatment  but it hc-Myb is regulated by the MLL-fusion protein at the transcriptional level and this persistence high-level expression is essential for the proliferation of the leukemic cells (in vitro and in vivo in MLL-fusion protein leukaemia [c-Myb and inhibition of the proliferation of leukemic cells [The mixed lineage leukemia (MLL)-fusion protein leukaemia is considered one of the most aggressive forms of diseases where the mortality rate is as high as 50%. Mixed lineage leukemia protein-1 or MLL-1 has histone H3 lysine 4 (H3K4) methyltransferase activity and form almost 70-inframe fusion proteins with several oncogenic partners through reciprocal chromosomal translocation. The fusion protein retains the N-terminal DNA domain of MLL protein and the conserved domain to interact with other co-factors such as multiple endocrine neoplasia type 1 (MEN-1). The c-terminal domain is contributed by the transactivation domain of several partner proteins such as AF9, ENL, ELL, AF4, and many others. MLL-fusion proteins form a multiprotein transcription super elongation complex that recruits DOTL1, BRD4, and PTEF-b through interaction with the transactivation domain . Expressic cells . Therefoeukaemia . For exaic cells . The phaic cells . Similaric cells , 23. TheMYC gene and downregulates its expression. The BET inhibitor OTX015/MK-8628 which interferes with its binding with acetylated histones was used to conduct phase-I and II clinical trials, however, it was not successful due to severe side effect. Another BET inhibitor BI 894999 is now in clinical trials in combination with the inhibitor of P-TEFb. This combination treatment demonstrated a synergistic effect in the inhibition of cancer cell proliferation and a reduction of tumour growth [Mammalian genes are regulated at the transcriptional and epigenetic levels. While TFs are associated with the transcriptional control mechanism, epigenetic regulation is associated with the accessibility of the chromosome through the modification of histones required to make a complex 3-D structure of the chromosomes. Genes encoding the TFs’ are also regulated epigenetically and it has been demonstrated that overexpression of oncogenic TFs is a result of permanent epigenetic modification of genes. Therefore, it is expected that drugs targeting the enzymes responsible for epigenetic modification would affect the expression of TF’s. However, the most challenging part here is the identification of a particular enzyme modifying histone because these histone-modifying enzymes are working simultaneously to control the expression of many genes and interruptions would generate severe off-target effects. The epigenetic regulation of the oncogenic TF MYC is one of the well-studied models. In case of MYC, this study had additional significance because this TF lacks a defined targetable domain. Inhibitors of histone-modifying enzymes such as HDAC, DNA methyltransferases, and bromodomain and extra-terminal motif (BET) were used to determine the effect on transcription. One of the BET family members, BRD4 recruits P-TEFb (a heterodimer of Cyclin T1 and CDK9) which is required for its functional activities. Inhibitors of BRD4 showed its reduced binding at acetylated histones located in the promoter and the enhancer region of the r growth , 25.in vitro and in vivo assays. For our better understanding, we would like to mention very briefly about a few recent approaches in this area.Every TF is designed to bind a specific DNA sequence and a defined conformation is required to recognize the stretch of DNA harbouring the specific binding sequence. Therefore, studying the structure of the DNA binding domain and designing a drug that could block this domain is one of the established approaches to the targeted drug discovery. X-ray crystallography plays a very important role in this area because it provides very fine molecular details (at the atomic level) and therefore could predict the shape of a possible inhibitory molecule that would block the DNA binding cavity. Using the computer-assisted drug discovery (CADD), which utilizes the crystallographic structure of the protein or specific domain of the protein, it is possible to run a virtual screening of chemical libraries to make a list of plausible ‘hits’ for carrying out In prostate cancer, human androgen receptors (ARs), a TF, are responsible for the development and progression of the disease. Enzalutamide is a small molecule inhibitor that blocks the ligand-binding domain of this receptor to inactivate the transcriptional activities. However, resistance to this drug was also reported after treating patients for a while. In a CADD approach, Li et al. , performin silico approach and found this novel inhibitor to be effective to block the DNA binding of this protein as well as stops the tumour growth and metastasis in vivo. In both of these studies mentioned here, all initial approaches were to target other novel domains (SH2 for STAT3 and ligand-binding domain of the AR) but not the DNA binding domain due to the undruggable nature of this domain. However, several biophysical methods, X-ray crystallography, CADD, and other in silico approaches are currently working in collaboration to target the DNA binding domain of TF’s which were almost inaccessible in the early days of drug discovery.Constitutive activation of the TF signal transducer and activator of transcription 3 (STAT3) has been shown to be essential for the aggressiveness of the malignant tumours and thus making this TF as an attractive target for drug discovery. The STAT3 protein has an Src homology 2 (SH2) domain at the c-terminal region of this protein and a series of drugs targeted to this domain was found to be unsuccessful in clinical trials. In their study, Huang et al. , targeteBased on the nature of the DNA binding domain, TFs are also classified as zinc finger, bHLH, bZIP group of proteins. The largest family of human TF belongs to the zinc finger group proteins. By rearranging the zinc finger domains, which are also associated with the change in the conformation of the protein, these groups of TFs are capable of binding promoters of multiple genes. This phenomenon of conformational modifications is also applicable for the b-HLH and b-ZIP group of protein due to the flexibility of the DNA binding motif. Targeting the DNA binding domain of these groups of proteins is more challenging because of the high risk of generating off-target effects. For example, currently, our knowledge is very much limited about targeting a specific zinc finger a protein with multiple zinc finger motif. Since zinc fingers are capable to bind other divalent metal ions, therefore, attempts to add other metal ions such as Co (II), Ni (II), and Cu (II) in excess found to be effective to reduce the DNA binding activity of the zinc finger proteins . HoweverTFs maintain a dynamic equilibrium between their DNA bound and unbound form. This equilibrium is necessary for the genes that are expressed constitutively at a very low level. However, for the inducible gene expression, the binding of a set of specific TFs along with cofactors is upregulated in the presence of appropriate signals. Therefore, the direct or indirect association of TF and its cofactors has been found to be essential to trigger the active transcription. On the other hand, interference of this association, if possible, would downregulate the transcription and expression of genes even in the presence of appropriate signals. Over the last few decades, several such interactions have been documented and the importance of these interactions was found to be very critical in controlling gene transcription. However, successful attempts of targeting these interactions in drug discovery are not at all very impressive which is perhaps due to the very complex nature of these interactions that prevent in designing or identifying appropriate molecule(s) capable of inhibiting such interactions.TF c-MYB plays a key role in haematopoiesis and in the development and maintenance of leukaemia. For its functional activity, c-MYB associates and physically interacts with many proteins and among the several proteins, the p300-c-MYB interaction is one of the most studied interactions because it is implicated as a major driver for the leukemic transformation . The intSeveral examples of active interaction, like above, are already known to be functionally important for the disease development, however, very few of them were moved forward in the pathway of drug discovery. For example, oncogenic TF MYC interacts with WDR5-a protein with druggable pockets, for the activation of genes. This interaction is structurally very well defined based on the X-ray crystallography data and is responsible for leukemogenesis. However, no attempts were reported so far as to target this interaction . TherefoBesides interacting with its co-factors, TF’s often from homodimers such as STAT3, hypoxia-inducible factor 1 (HIF-1) and this homodimerization are pivotal for the functional activity of this protein. As it was mentioned previously, STAT3 drew major attention recently because it is constitutively expressed in several cancers. The dimerization occurs through the reciprocal interaction of the conserved tyrosine residue when phosphorylated at the SH2 domain . Over thHIF-1 is a bHLH TF considered as another promising target. It plays a very crucial role in the expression of genes related to the angiogenesis and controls the expression of vascular endothelial growth factor which is upregulated to several folds during tumour growth. HIF1α is required to form a heterodimer with HIF1β for the activation of its target genes. Since HIF1 is an appealing target, several inhibitors were identified targeting this TF such as DNA binding activity, degradations, and heterodimer formation. A high throughput genetic screening was conducted by Miranda et al. , to idende novo degradation of these proteins. Mammalian cells have endogenous protein degradation pathways where proteins are degraded followed by ubiquitination and sumoylation. The anti-infective drug mebendazole exhibits anti-tumor activities by reducing cell proliferation and enhancing apoptosis mediated through the activation of the protein degradation pathway. In one of such recent efforts, Walf-Vorderwülbecke et al. [Most of the TF in somatic cells express at a very low level and this low basal level of expression is required to bind the high-affinity DNA binding site. However, under the condition of elevated level of expression, TFs bind to the low-affinity binding site of the DNA and can activate several genes . Often te et al. , showed e et al. .The purpose of this review is to discuss several strategies to target TFs as a part of targeted drug discovery. However, promising results were reported where activities of TFs were modulated by potential drug candidates and the mechanism of action is not clearly understood. For example, several natural bioactive molecules have been found to be modulating the activity of the signaling pathway connected to the TF AP-1 . SimilarThe effect TF on gene expression and the development of diseases are a very well-established phenomenon. It is unconditionally accepted that functional inactivation or downregulation of the overexpressed TF would generate an immediate effect on cellular and metabolic processes. However, in the field of drug discovery, TFs were sitting at the ‘back seat’ as a target for a long period of time because of the undruggable nature of these molecules. Over the last few decades, several new technologies were applied to explore this undruggable nature and as a result, some promising new candidates targeting cofactors, or the TFs itself were identified, and they are currently in the different phases of human clinical trials. It is also well understood that targeting TF is still a very high-risk area of targeted drug discovery. Therefore, to increase the rate of success, an extensive search is necessary to identify a panel of potential drug candidates in the early stage of drug discovery to obtain a clinically potential molecule(s) with modest adverse effects. If we are successful, then drugs targeting TF would be a new armamentarium for cancer treatment.