Based on a digital image correlation（DIC）method with the measurements of a high speed crack＇s displacement and strain fields,a technique to accurately and automatically locate its crack tip has been developed.The c...Based on a digital image correlation（DIC）method with the measurements of a high speed crack＇s displacement and strain fields,a technique to accurately and automatically locate its crack tip has been developed.The crack tip is identified by finding the abrupt jump on the opening（or dislocation）curve of a point on the trace of the crack propagation,while the opening is measured through a virtual extensometer technique and the abrupt jump is identified by finding the peak on the curve.The method was verified using a specially designed experiment and applied to measure the critical crack tip opening angle of a rock sample.Because the involvement of analytical models has been avoided and then the good performance could be ensured for low resolution speckle images,this technique is expected to be very useful in the quantitative study of high speed cracks in experiments using high speed cameras.展开更多
The crack is a common pavement failure problem.A lack of periodic maintenance will result in extending the cracks and damage the pavement,which will affect the normal use of the road.Therefore,it is significant to est...The crack is a common pavement failure problem.A lack of periodic maintenance will result in extending the cracks and damage the pavement,which will affect the normal use of the road.Therefore,it is significant to establish an efficient intelligent identification model for pavement cracks.The neural network is a method of simulating animal nervous systems using gradient descent to predict results by learning a weight matrix.It has been widely used in geotechnical engineering,computer vision,medicine,and other fields.However,there are three major problems in the application of neural networks to crack identification.There are too few layers,extracted crack features are not complete,and the method lacks the efficiency to calculate the whole picture.In this study,a fully convolutional neural network based on ResNet-101 is used to establish an intelligent identification model of pavement crack regions.This method,using a convolutional layer instead of a fully connected layer,realizes full convolution and accelerates calculation.The region proposals come from the feature map at the end of the base network,which avoids multiple computations of the same picture.Online hard example mining and data-augmentation techniques are adopted to improve the model’s recognition accuracy.We trained and tested Concrete Crack Images for Classification(CCIC),which is a public dataset collected using smartphones,and the Crack Image Database(CIDB),which was automatically collected using vehicle-mounted charge-coupled device cameras,with identification accuracy reaching 91.4%and 86.4%,respectively.The proposed model has a higher recognition accuracy and recall rate than Faster RCNN and different depth models,and can extract more complete and accurate crack features in CIDB.We also analyzed translation processing,fuzzy,scaling,and distorted images.The proposed model shows a strong robustness and stability,and can automatically identify image cracks of different forms.It has broad application prospects in practical engineering problems.展开更多
Detection of cracks in concrete structures is critical for their safety and the sustainability of maintenance processes.Traditional inspection techniques are costly,time-consuming,and inefficient regarding human resou...Detection of cracks in concrete structures is critical for their safety and the sustainability of maintenance processes.Traditional inspection techniques are costly,time-consuming,and inefficient regarding human resources.Deep learning architectures have become more widespread in recent years by accelerating these processes and increasing their efficiency.Deep learning models(DLMs)stand out as an effective solution in crack detection due to their features such as end-to-end learning capability,model adaptation,and automatic learning processes.However,providing an optimal balance between model performance and computational efficiency of DLMs is a vital research topic.In this article,three different methods are proposed for detecting cracks in concrete structures.In the first method,a Separable Convolutional with Attention and Multi-layer Enhanced Fusion Network(SCAMEFNet)deep neural network,which has a deep architecture and can provide a balance between the depth of DLMs and model parameters,has been developed.This model was designed using a convolutional neural network,multi-head attention,and various fusion techniques.The second method proposes a modified vision transformer(ViT)model.A two-stage ensemble learning model,deep featurebased two-stage ensemble model(DFTSEM),is proposed in the third method.In this method,deep features and machine learning methods are used.The proposed approaches are evaluated using the Concrete Cracks Image Data set,which the authors collected and contains concrete cracks on building surfaces.The results show that the SCAMEFNet model achieved an accuracy rate of 98.83%,the ViT model 97.33%,and the DFTSEM model 99.00%.These findings show that the proposed techniques successfully detect surface cracks and deformations and can provide practical solutions to realworld problems.In addition,the developed methods can contribute as a tool for BIM platforms in smart cities for building health.展开更多
Cracks are a major sign of aging transportation infrastructure.The detection and repair of cracks is the key to ensuring the overall safety of the transportation infrastructure.In recent years,due to the remarkable su...Cracks are a major sign of aging transportation infrastructure.The detection and repair of cracks is the key to ensuring the overall safety of the transportation infrastructure.In recent years,due to the remarkable success of deep learning(DL)in the field of crack detection,many researches have been devoted to developing pixel-level crack image segmentation(CIS)models based on DL to improve crack detection accuracy,but as far as we know there is no review of DL-based CIS methods yet.To address this gap,we present a comprehensive thematic survey of DL-based CIS techniques.Our review offers several contributions to the CIS area.First,more than 40 papers of journal or top conference most published in the last three years are identified and collected based on the systematic literature review method.Second,according to the backbone network architecture of the models proposed in them,they are grouped into 10 topics:FCN,U-Net,encoder-decoder model,multi-scale,attention mechanism,transformer,two-stage detection,multi-modal fusion,unsupervised learning and weakly supervised learning,to be reviewed.Meanwhile,our survey focuses on discussing strengths and limitations of the models in each topic so as to reveal the latest research progress in the CIS field.Third,publicly accessible data sets,evaluation metrics,and loss functions that can be used for pixel-level crack detection are systematically introduced and summarized to facilitate researchers to select suitable components according to their own research tasks.Finally,we discuss six common problems and existing solutions to them in the field of DL-based CIS,and then suggest eight possible future research directions in this field.展开更多
基金Supported by the National Natural Science Foundation of China(11172039,11402023)the Fundamental Research Funding of BIT(20120142021)the State Key Laboratory of Earthquake Dynamics(LED2011B03)
文摘Based on a digital image correlation（DIC）method with the measurements of a high speed crack＇s displacement and strain fields,a technique to accurately and automatically locate its crack tip has been developed.The crack tip is identified by finding the abrupt jump on the opening（or dislocation）curve of a point on the trace of the crack propagation,while the opening is measured through a virtual extensometer technique and the abrupt jump is identified by finding the peak on the curve.The method was verified using a specially designed experiment and applied to measure the critical crack tip opening angle of a rock sample.Because the involvement of analytical models has been avoided and then the good performance could be ensured for low resolution speckle images,this technique is expected to be very useful in the quantitative study of high speed cracks in experiments using high speed cameras.
基金funded by the National Key Research and Development Program of China(No.2017YFC1501200)the National Natural Science Foundation of China(Nos.51678536,41404096)+2 种基金supported by Department of education’s Production-Study-Research combined innovation Funding-“Blue fire plan(Huizhou)”(CXZJHZ01742)the Program for Science and Technology Innovation Talents in Universities of Henan Province(Grant No.19HASTIT043)the Outstanding Young Talent Research Fund of Zhengzhou University(1621323001).
文摘The crack is a common pavement failure problem.A lack of periodic maintenance will result in extending the cracks and damage the pavement,which will affect the normal use of the road.Therefore,it is significant to establish an efficient intelligent identification model for pavement cracks.The neural network is a method of simulating animal nervous systems using gradient descent to predict results by learning a weight matrix.It has been widely used in geotechnical engineering,computer vision,medicine,and other fields.However,there are three major problems in the application of neural networks to crack identification.There are too few layers,extracted crack features are not complete,and the method lacks the efficiency to calculate the whole picture.In this study,a fully convolutional neural network based on ResNet-101 is used to establish an intelligent identification model of pavement crack regions.This method,using a convolutional layer instead of a fully connected layer,realizes full convolution and accelerates calculation.The region proposals come from the feature map at the end of the base network,which avoids multiple computations of the same picture.Online hard example mining and data-augmentation techniques are adopted to improve the model’s recognition accuracy.We trained and tested Concrete Crack Images for Classification(CCIC),which is a public dataset collected using smartphones,and the Crack Image Database(CIDB),which was automatically collected using vehicle-mounted charge-coupled device cameras,with identification accuracy reaching 91.4%and 86.4%,respectively.The proposed model has a higher recognition accuracy and recall rate than Faster RCNN and different depth models,and can extract more complete and accurate crack features in CIDB.We also analyzed translation processing,fuzzy,scaling,and distorted images.The proposed model shows a strong robustness and stability,and can automatically identify image cracks of different forms.It has broad application prospects in practical engineering problems.
文摘Detection of cracks in concrete structures is critical for their safety and the sustainability of maintenance processes.Traditional inspection techniques are costly,time-consuming,and inefficient regarding human resources.Deep learning architectures have become more widespread in recent years by accelerating these processes and increasing their efficiency.Deep learning models(DLMs)stand out as an effective solution in crack detection due to their features such as end-to-end learning capability,model adaptation,and automatic learning processes.However,providing an optimal balance between model performance and computational efficiency of DLMs is a vital research topic.In this article,three different methods are proposed for detecting cracks in concrete structures.In the first method,a Separable Convolutional with Attention and Multi-layer Enhanced Fusion Network(SCAMEFNet)deep neural network,which has a deep architecture and can provide a balance between the depth of DLMs and model parameters,has been developed.This model was designed using a convolutional neural network,multi-head attention,and various fusion techniques.The second method proposes a modified vision transformer(ViT)model.A two-stage ensemble learning model,deep featurebased two-stage ensemble model(DFTSEM),is proposed in the third method.In this method,deep features and machine learning methods are used.The proposed approaches are evaluated using the Concrete Cracks Image Data set,which the authors collected and contains concrete cracks on building surfaces.The results show that the SCAMEFNet model achieved an accuracy rate of 98.83%,the ViT model 97.33%,and the DFTSEM model 99.00%.These findings show that the proposed techniques successfully detect surface cracks and deformations and can provide practical solutions to realworld problems.In addition,the developed methods can contribute as a tool for BIM platforms in smart cities for building health.
基金the National Natural Science Foundation of China(No.61971005)the Scientific Research Project of Department of Transport of Shaanxi Province in 2020(No.20-24K)+2 种基金the Key Project of Baoji University of Arts and Science(ZK2018013)Research Project of Department of Education of Zhejiang Province(Y202146796)Major Scientific and Technological Innovation Project of Wenzhou City(ZG2021029)。
文摘Cracks are a major sign of aging transportation infrastructure.The detection and repair of cracks is the key to ensuring the overall safety of the transportation infrastructure.In recent years,due to the remarkable success of deep learning(DL)in the field of crack detection,many researches have been devoted to developing pixel-level crack image segmentation(CIS)models based on DL to improve crack detection accuracy,but as far as we know there is no review of DL-based CIS methods yet.To address this gap,we present a comprehensive thematic survey of DL-based CIS techniques.Our review offers several contributions to the CIS area.First,more than 40 papers of journal or top conference most published in the last three years are identified and collected based on the systematic literature review method.Second,according to the backbone network architecture of the models proposed in them,they are grouped into 10 topics:FCN,U-Net,encoder-decoder model,multi-scale,attention mechanism,transformer,two-stage detection,multi-modal fusion,unsupervised learning and weakly supervised learning,to be reviewed.Meanwhile,our survey focuses on discussing strengths and limitations of the models in each topic so as to reveal the latest research progress in the CIS field.Third,publicly accessible data sets,evaluation metrics,and loss functions that can be used for pixel-level crack detection are systematically introduced and summarized to facilitate researchers to select suitable components according to their own research tasks.Finally,we discuss six common problems and existing solutions to them in the field of DL-based CIS,and then suggest eight possible future research directions in this field.
