Automatic pavement crack detection plays an important role in ensuring road safety.In images of cracks,information about the cracks can be conveyed through high-frequency and low-fre-quency signals that focus on fine ...Automatic pavement crack detection plays an important role in ensuring road safety.In images of cracks,information about the cracks can be conveyed through high-frequency and low-fre-quency signals that focus on fine details and global structures,respectively.The output features obtained from different convolutional layers can be combined to represent information about both high-frequency and low-frequency signals.In this paper,we propose an encoder-decoder framework called octave hierarchical network(Octave-H),which is based on the U-Network(U-Net)architec-ture and utilizes an octave convolutional neural network and a hierarchical feature learning module for performing crack detection.The proposed octave convolution is capable of extracting multi-fre-quency feature maps,capturing both fine details and global cracks.We propose a hierarchical feature learning module that merges multi-frequency-scale feature maps with different levels(high and low)of octave convolutional layers.To verify the superiority of the proposed Octave-H,we employed the CrackForest dataset(CFD)and AigleRN databases to evaluate this method.The experimental results demonstrate that Octave-H outperforms other algorithms with satisfactory performance.展开更多
Automatic crack detection of cement pavement chiefly benefits from the rapid development of deep learning,with convolutional neural networks(CNN)playing an important role in this field.However,as the performance of cr...Automatic crack detection of cement pavement chiefly benefits from the rapid development of deep learning,with convolutional neural networks(CNN)playing an important role in this field.However,as the performance of crack detection in cement pavement improves,the depth and width of the network structure are significantly increased,which necessitates more computing power and storage space.This limitation hampers the practical implementation of crack detection models on various platforms,particularly portable devices like small mobile devices.To solve these problems,we propose a dual-encoder-based network architecture that focuses on extracting more comprehensive fracture feature information and combines cross-fusion modules and coordinated attention mechanisms formore efficient feature fusion.Firstly,we use small channel convolution to construct shallow feature extractionmodule(SFEM)to extract low-level feature information of cracks in cement pavement images,in order to obtainmore information about cracks in the shallowfeatures of images.In addition,we construct large kernel atrous convolution(LKAC)to enhance crack information,which incorporates coordination attention mechanism for non-crack information filtering,and large kernel atrous convolution with different cores,using different receptive fields to extract more detailed edge and context information.Finally,the three-stage feature map outputs from the shallow feature extraction module is cross-fused with the two-stage feature map outputs from the large kernel atrous convolution module,and the shallow feature and detailed edge feature are fully fused to obtain the final crack prediction map.We evaluate our method on three public crack datasets:DeepCrack,CFD,and Crack500.Experimental results on theDeepCrack dataset demonstrate the effectiveness of our proposed method compared to state-of-the-art crack detection methods,which achieves Precision(P)87.2%,Recall(R)87.7%,and F-score(F1)87.4%.Thanks to our lightweight crack detectionmodel,the parameter count of the model in real-world detection scenarios has been significantly reduced to less than 2M.This advancement also facilitates technical support for portable scene detection.展开更多
Rapid and accurate segmentation of structural cracks is essential for ensuring the quality and safety of engineering projects.In practice,however,this task faces the challenge of finding a balance between detection ac...Rapid and accurate segmentation of structural cracks is essential for ensuring the quality and safety of engineering projects.In practice,however,this task faces the challenge of finding a balance between detection accuracy and efficiency.To alleviate this problem,a lightweight and efficient real-time crack segmentation framework was developed.Specifically,in the network model system based on an encoding-decoding structure,the encoding network is equipped with packet convolution and attention mechanisms to capture features of different visual scales in layers,and in the decoding process,we also introduce a fusion module based on spatial attention to effectively aggregate these hierarchical features.Codecs are connected by pyramid pooling model(PPM)filtering.The results show that the crack segmentation accuracy and real-time operation capability larger than 76%and 15 fps,respectively,are validated by three publicly available datasets.These wide-ranging results highlight the potential of the model for the intelligent O&M for cross-sea bridge.展开更多
With the digital image technology,a crack detection method of reinforced concrete bridge was studied for the performance assessment.The effects including the image gray level,pixel rate,noise filter,and edge detection...With the digital image technology,a crack detection method of reinforced concrete bridge was studied for the performance assessment.The effects including the image gray level,pixel rate,noise filter,and edge detection were analyzed considering cracks qualities.A computer program was developed by visual C++6.0 programming language to detect the cracks,which was tested by 15cases of bridge video images.The results indicate that the relative error is within 6%for cracks larger than 0.3 mm cracks and it is less than 10%for crack width between 0.2 mm and 0.3 mm.In addition,for the crack below 0.1 mm,the relative error is more than30%because the bridge is in safe stage and it is very difficult to detect the actual width of crack.展开更多
Structural cracks can change the frequency response function (FRF) of an offshore platform. Thus, FRF shifts can be used to detect cracks. When a crack at a specific location and magnitude occurs in an offshore struct...Structural cracks can change the frequency response function (FRF) of an offshore platform. Thus, FRF shifts can be used to detect cracks. When a crack at a specific location and magnitude occurs in an offshore structure, changes in the FRF can be measured. In this way, shifts in FRF can be used to detect cracks. An experimental model was constructed to verify the FRF method. The relationship between FRF and cracks was found to be non-linear. The effect of multiple cracks on FRF was analyzed, and the shift due to multiple cracks was found to be much more than the summation of FRF shifts due to each of the cracks. Then the effects of noise and changes in the mass of the jacket on FRF were evaluated. The results show that significant damage to a beam can be detected by dramatic changes in the FRF, even when 10% random noise exists. FRF can also be used to approximately locate the breakage, but it can neither be efficiently used to predict the location of breakage nor the existence of small hairline cracks. The FRF shift caused by a 7% mass change is much less than the FRF shift caused by the breakage of any beam, but is larger than that caused by any early cracks.展开更多
Automatic pavement crack detection is a critical task for maintaining the pavement stability and driving safety.The task is challenging because the shadows on the pavement may have similar intensity with the crack,whi...Automatic pavement crack detection is a critical task for maintaining the pavement stability and driving safety.The task is challenging because the shadows on the pavement may have similar intensity with the crack,which interfere with the crack detection performance.Till to the present,there still lacks efficient algorithm models and training datasets to deal with the interference brought by the shadows.To fill in the gap,we made several contributions as follows.First,we proposed a new pavement shadow and crack dataset,which contains a variety of shadow and pavement pixel size combinations.It also covers all common cracks(linear cracks and network cracks),placing higher demands on crack detection methods.Second,we designed a two-step shadow-removal-oriented crack detection approach:SROCD,which improves the performance of the algorithm by first removing the shadow and then detecting it.In addition to shadows,the method can cope with other noise disturbances.Third,we explored the mechanism of how shadows affect crack detection.Based on this mechanism,we propose a data augmentation method based on the difference in brightness values,which can adapt to brightness changes caused by seasonal and weather changes.Finally,we introduced a residual feature augmentation algorithm to detect small cracks that can predict sudden disasters,and the algorithm improves the performance of the model overall.We compare our method with the state-of-the-art methods on existing pavement crack datasets and the shadow-crack dataset,and the experimental results demonstrate the superiority of our method.展开更多
Feasibility of a wave propagation-based active crack detection technique for nondestructive evaluations （NDE） of concrete structures with surface bonded and embedded piezoelectric-ceramic （PZT） patches was studied...Feasibility of a wave propagation-based active crack detection technique for nondestructive evaluations （NDE） of concrete structures with surface bonded and embedded piezoelectric-ceramic （PZT） patches was studied. At first, the wave propagation mechanisms in concrete were analyzed. Then, an active sensing system with integrated actuators/sensors was constructed. One PZT patch was used as an actuator to generate high frequency waves, and the other PZT patches were used as sensors to detect the propagating wave. Scattered wave signals from the damage can be obtained by subtracting the baseline signal of the intact structure from the recorded signal of the damaged structure. In the experimental study, progressive cracked damage inflicted artificially on the plain concrete beam is assessed by using both lateral and thickness modes of the PZT patches. The results indicate that with the increasing number and severity of cracks, the magnitude of the sensor output decreases for the surface bonded PZT patches, and increases for the embedded PZT patches.展开更多
Pavement crack detection plays a crucial role in ensuring road safety and reducing maintenance expenses.Recent advancements in deep learning(DL)techniques have shown promising results in detecting pavement cracks;howe...Pavement crack detection plays a crucial role in ensuring road safety and reducing maintenance expenses.Recent advancements in deep learning(DL)techniques have shown promising results in detecting pavement cracks;however,the selection of relevant features for classification remains challenging.In this study,we propose a new approach for pavement crack detection that integrates deep learning for feature extraction,the whale optimization algorithm(WOA)for feature selection,and random forest(RF)for classification.The performance of the models was evaluated using accuracy,recall,precision,F1 score,and area under the receiver operating characteristic curve(AUC).Our findings reveal that Model 2,which incorporates RF into the ResNet-18 architecture,outperforms baseline Model 1 across all evaluation metrics.Nevertheless,our proposed model,which combines ResNet-18 with both WOA and RF,achieves significantly higher accuracy,recall,precision,and F1 score compared to the other two models.These results underscore the effectiveness of integrating RF and WOA into ResNet-18 for pavement crack detection applications.We applied the proposed approach to a dataset of pavement images,achieving an accuracy of 97.16%and an AUC of 0.984.Our results demonstrate that the proposed approach surpasses existing methods for pavement crack detection,offering a promising solution for the automatic identification of pavement cracks.By leveraging this approach,potential safety hazards can be identified more effectively,enabling timely repairs and maintenance measures.Lastly,the findings of this study also emphasize the potential of integrating RF and WOA with deep learning for pavement crack detection,providing road authorities with the necessary tools to make informed decisions regarding road infrastructure maintenance.展开更多
Crack detection procedures by different modal parameters are analyzed for identifying a crack and its location and magnitude in a jacket platform. The first ten natural frequencies and modal shapes of the jacket model...Crack detection procedures by different modal parameters are analyzed for identifying a crack and its location and magnitude in a jacket platform. The first ten natural frequencies and modal shapes of the jacket models are obtained by numerical experiments based on NASTRAN Code. A crack at different locations and of different magnitudes is imposed in the model at the underwater beams. Then, the modal evaluation parameters are calculated numerically, to illustrate the evaluation of modal parameter criteria used in jacket crack detection. The sensitivities of different modal parameters to different cracks are analyzed. A new technique is presented for predicting the approximate location of a breakage in the absence of the data of an intact model. This method can be used to detect a crack in underwater members by use of incomplete mode shapes of the top members of the jacket.展开更多
Fatigue cracks that develop in civil infrastructure such as steel bridges due to repetitive loads pose a major threat to structural integrity.Despite being the most common practice for fatigue crack detection,human vi...Fatigue cracks that develop in civil infrastructure such as steel bridges due to repetitive loads pose a major threat to structural integrity.Despite being the most common practice for fatigue crack detection,human visual inspection is known to be labor intensive,time-consuming,and prone to error.In this study,a computer vision-based fatigue crack detection approach using a short video recorded under live loads by a moving consumer-grade camera is presented.The method detects fatigue crack by tracking surface motion and identifies the differential motion pattern caused by opening and closing of the fatigue crack.However,the global motion introduced by a moving camera in the recorded video is typically far greater than the actual motion associated with fatigue crack opening/closing,leading to false detection results.To overcome the challenge,global motion compensation(GMC)techniques are introduced to compensate for camera-induced movement.In particular,hierarchical model-based motion estimation is adopted for 2D videos with simple geometry and a new method is developed by extending the bundled camera paths approach for 3D videos with complex geometry.The proposed methodology is validated using two laboratory test setups for both in-plane and out-of-plane fatigue cracks.The results confirm the importance of motion compensation for both 2D and 3D videos and demonstrate the effectiveness of the proposed GMC methods as well as the subsequent crack detection algorithm.展开更多
An alternative technique for crack detection in a Timoshenko beam based on the first anti-resonant frequency is presented in this paper. Unlike the natural frequency, the anti-resonant frequency is a local parameter r...An alternative technique for crack detection in a Timoshenko beam based on the first anti-resonant frequency is presented in this paper. Unlike the natural frequency, the anti-resonant frequency is a local parameter rather than a global parameter of structures, thus the proposed technique can be used to locate the structural defects. An impedance analysis of a cracked beam stimulated by a harmonic force based on the Timoshenko beam formulation is investigated. In order to characterize the local discontinuity due to cracks, a rotational spring model based on fracture mechanics is proposed to model the crack. Subsequently, the proposed method is verified by a numerical example of a simply-supported beam with a crack. The effect of the crack size on the anti-resonant frequency is investigated. The position of the crack of the simply-supported beam is also determined by the anti-resonance technique. The proposed technique is further applied to the ＂contaminated＂ anti-resonant frequency to detect crack damage, which is obtained by adding 1-3% noise to the calculated data. It is found that the proposed technique is effective and free from the environment noise. Finally, an experimental study is performed, which further verifies the validity of the proposed crack identification technique.展开更多
Harsh working environments and wear between blades and other unit components can easily lead to cracks and damage on wind turbine blades.The cracks on the blades can endanger the shafting of the generator set,the towe...Harsh working environments and wear between blades and other unit components can easily lead to cracks and damage on wind turbine blades.The cracks on the blades can endanger the shafting of the generator set,the tower and other components,and even cause the tower to collapse.To achieve high-precision wind blade crack detection,this paper proposes a crack fault-detection strategy that integratesGated ResidualNetwork(GRN),a fusionmodule and Transformer.Firstly,GRNcan reduce unnecessary noisy inputs that could negatively impact performancewhile preserving the integrity of feature information.In addition,to gain in-depth information about the characteristics of wind turbine blades,a fusionmodule is suggested to implement the information fusion of wind turbine features.Specifically,each fan feature ismapped to a one-dimensional vector with the same length,and all one-dimensional vectors are concatenated to obtain a two-dimensional vector.And then,in the fusion module,the information fusion of the same characteristic variables in the different channels is realized through the Channel-mixing MLP,and the information fusion of different characteristic variables in the same channel is realized through the ColumnmixingMLP.Finally,the fused feature vector is input into the Transformer for feature learning,which enhances the influence of important feature information and improves the model’s anti-noise ability and classification accuracy.Extensive experimentswere conducted on the wind turbine supervisory control and data acquisition(SCADA)data froma domesticwind field.The results show that compared with other state-of-the-artmodels,including XGBoost,LightGBM,TabNet,etc.,the F1-score of proposed gated fusion based Transformer model can reach 0.9907,which is 0.4%-2.09% higher than the comparedmodels.Thismethod provides amore reliable approach for the condition detection and maintenance of fan blades in wind farms.展开更多
A high-precision identification method for steam turbine rotor crack is presented. By providing me nrst three measured natural frequencies, contours for the specified natural frequency are plotted in the same coordi- ...A high-precision identification method for steam turbine rotor crack is presented. By providing me nrst three measured natural frequencies, contours for the specified natural frequency are plotted in the same coordi- nate, and the intersection of the three curves predicts the crack location and size. The cracked rotor system is mod- eled using B-spline wavelet on the interval （BSWI） finite element method, and a method based on empirical mode decomposition （EMD） and Laplace wavelet is implemented to improve the identification precision of the first three measured natural frequencies. Compared with the classical nondestructive testing, the presented method shows its effectiveness and reliability. It is feasible to apply this method to the online health monitoring for rotor structure.展开更多
This study aimed to propose road crack detection method based on infrared image fusion technology.By analyzing the characteristics of road crack images,this method uses a variety of infrared image fusion methods to pr...This study aimed to propose road crack detection method based on infrared image fusion technology.By analyzing the characteristics of road crack images,this method uses a variety of infrared image fusion methods to process different types of images.The use of this method allows the detection of road cracks,which not only reduces the professional requirements for inspectors,but also improves the accuracy of road crack detection.Based on infrared image processing technology,on the basis of in-depth analysis of infrared image features,a road crack detection method is proposed,which can accurately identify the road crack location,direction,length,and other characteristic information.Experiments showed that this method has a good effect,and can meet the requirement of road crack detection.展开更多
The traditional You Only Look Once(YOLO)series network models often fail to extract satisfactory features for road detection,due to the limited number of defect images in the dataset.Additionally,most open-source road...The traditional You Only Look Once(YOLO)series network models often fail to extract satisfactory features for road detection,due to the limited number of defect images in the dataset.Additionally,most open-source road crack datasets contain idealized cracks that are not suitable for detecting early-stage pavement cracks with fine widths and subtle features.To address these issues,this study collected a large number of original road surface images using road detection vehicles.A large-capacity crack dataset was then constructed,with various shapes of cracks categorized as either cracks or fractures.To improve the training performance of the YOLOv5 algorithm,which showed unsatisfactory results on the original dataset,this study used median filtering to preprocess the crack images.The preprocessed images were combined to form the training set.Moreover,the Coordinate Attention(CA)attention module was integrated to further enhance the model’s training performance.The final detection model achieved a recognition accuracy of 88.9%and a recall rate of 86.1%for detecting cracks.These findings demonstrate that the use of image preprocessing technology and the introduction of the CA attention mechanism can effectively detect early-stage pavement cracks that have low contrast with the background.展开更多
In response to the challenges in highway pavement distress detection,such as multiple defect categories,difficulties in feature extraction for different damage types,and slow identification speeds,this paper proposes ...In response to the challenges in highway pavement distress detection,such as multiple defect categories,difficulties in feature extraction for different damage types,and slow identification speeds,this paper proposes an enhanced pavement crack detection model named Star-YOLO11.This improved algorithm modifies the YOLO11 architecture by substituting the original C3k2 backbone network with a Star-s50 feature extraction network.The enhanced structure adjusts the number of stacked layers in the StarBlock module to optimize detection accuracy and improve model efficiency.To enhance the accuracy of pavement crack detection and improve model efficiency,three key modifications to the YOLO11 architecture are proposed.Firstly,the original C3k2 backbone is replaced with a StarBlock-based structure,forming the Star-s50 feature extraction backbone network.This lightweight redesign reduces computational complexity while maintaining detection precision.Secondly,to address the inefficiency of the original Partial Self-attention(PSA)mechanism in capturing localized crack features,the convolutional prior-aware Channel Prior Convolutional Attention(CPCA)mechanism is integrated into the channel dimension,creating a hybrid CPC-C2PSA attention structure.Thirdly,the original neck structure is upgraded to a Star Multi-Branch Auxiliary Feature Pyramid Network(SMAFPN)based on the Multi-Branch Auxiliary Feature Pyramid Network architecture,which adaptively fuses high-level semantic and low-level spatial information through Star-s50 connections and C3k2 extraction blocks.Additionally,a composite dataset augmentation strategy combining traditional and advanced augmentation techniques is developed.This strategy is validated on a specialized pavement dataset containing five distinct crack categories for comprehensive training and evaluation.Experimental results indicate that the proposed Star-YOLO11 achieves an accuracy of 89.9%(3.5%higher than the baseline),a mean average precision(mAP)of 90.3%(+2.6%),and an F1-score of 85.8%(+0.5%),while reducing the model size by 18.8%and reaching a frame rate of 225.73 frames per second(FPS)for real-time detection.It shows potential for lightweight deployment in pavement crack detection tasks.展开更多
The operational and regional conditions to which the prestressed concrete sleeper(PCS)is subjected in a railway track significantly contribute to its performance and durability.Maintaining the health of PCS poses chal...The operational and regional conditions to which the prestressed concrete sleeper(PCS)is subjected in a railway track significantly contribute to its performance and durability.Maintaining the health of PCS poses challenges,and one of these issues involves the potential occurrence of longitudinal cracks in reinforcing bars,which can be caused by various constructional,functional,and environmental factors.Longitudinal cracks in PCS compromise the structural performance,resulting in a reduced capacity to withstand the loads exerted by moving vehicles.The current evaluations not only fail to yield a precise parameter for estimating the behavior and response of the PCS,but they also overlook the specific conditions of the PCS,such as prestressing,and only provide limited information regarding existing damage.Balancing the need for accurate evaluation with consideration of costs and resources,and making informed decisions about maintenance and track performance enhancement,has become a multifaceted challenge in ensuring a robust PCS assessment.This research introduces a novel methodology to improve the evaluation of mechanical and geometrical parameters of PCS over their operational lifespan.The objective is to enhance the accuracy of PCS performance estimation by concentrating on detecting longitudinal cracks.The suggested approach seamlessly integrates model updating methods and the finite element(FE)approach to achieve an accurate and timely assessment of PCS conditions.This comprehensive examination scrutinizes the methodology by applying artificial cracks to the PCS.In addition to introducing this assessment approach,a detailed examination is conducted on a laboratory-simulated PCS featuring various combinations of longitudinal cracks measuring 40,80,and 120 cm in length.This systematic and rigorous approach ensures the reliability and robustness of the methodology.Ultimately,the parameters of cross-sectional area,moment of inertia,and modulus of elasticity,which significantly impact the performance of this sleeper,are explored and demonstrated through functional methodologies.The findings suggest that assessing and addressing damage should be conducted through a comprehensive and integrated procedure,taking into account the actual conditions of the PCS.Longitudinal cracks lead to a substantial decrease in the performance of these components in railway tracks.By applying the proposed methods,it is anticipated that the evaluation error for these components will be reduced by approximately 30%compared to visual inspections,particularly in predicting the extent of damage for cracks measuring up to 120 cm.This research has the potential to significantly enhance the evaluation of PCS performance and mitigate the impact of longitudinal cracks on the safety and longevity of ballasted railway tracks in desert areas.展开更多
Disaster-resilient dams require accurate crack detection,but machine learning methods cannot capture dam structural reaction temporal patterns and dependencies.This research uses deep learning,convolutional neural net...Disaster-resilient dams require accurate crack detection,but machine learning methods cannot capture dam structural reaction temporal patterns and dependencies.This research uses deep learning,convolutional neural networks,and transfer learning to improve dam crack detection.Twelve deep-learning models are trained on 192 crack images.This research aims to provide up-to-date detecting techniques to solve dam crack problems.The finding shows that the EfficientNetB0 model performed better than others in classifying borehole concrete crack surface tiles and normal(undamaged)surface tiles with 91%accuracy.The study’s pre-trained designs help to identify and to determine the specific locations of cracks.展开更多
This paper introduces a crack detection example of the prestressed box girder in a certain project. The morphology of the box girder cracks was surveyed and mapped. The length, width, and depth of the cracks were insp...This paper introduces a crack detection example of the prestressed box girder in a certain project. The morphology of the box girder cracks was surveyed and mapped. The length, width, and depth of the cracks were inspected, and the strength and reinforcement configuration of the components were tested. The test results indicate that the strength and reinforcement configuration of the inspected components meet the design requirements. The crack at the end of the top plate of the box girder is a local compressive crack at the anchorage end. The width and length of the crack on the bottom surface of the top plate are not significant, and the depth is relatively shallow. Judging from the crack morphology, this crack is identified as a temperature crack. Additionally, based on the treatment measures for cracks of different widths, the treatment measures for the cracks of the components in this project are derived, providing a reference basis for similar projects in the future.展开更多
Computer-vision and deep-learning techniques are widely applied to detect,monitor,and assess pavement conditions including road crack detection.Traditional methods fail to achieve satisfactory accuracy and generalizat...Computer-vision and deep-learning techniques are widely applied to detect,monitor,and assess pavement conditions including road crack detection.Traditional methods fail to achieve satisfactory accuracy and generalization performance in for crack detection.Complex network model can generate redundant feature maps and computational complexity.Therefore,this paper proposes a novel model compression framework based on deep learning to detect road cracks,which can improve the detection efficiency and accuracy.A distillation loss function is proposed to compress the teacher model,followed by channel pruning.Meanwhile,a multi-dilation model is proposed to improve the accuracy of the model pruned.The proposed method is tested on the public database CrackForest dataset(CFD).The experimental results show that the proposed method is more efficient and accurate than other state-of-art methods.展开更多
基金supported in part by the National Natural Foundation of China(No.62176147)。
文摘Automatic pavement crack detection plays an important role in ensuring road safety.In images of cracks,information about the cracks can be conveyed through high-frequency and low-fre-quency signals that focus on fine details and global structures,respectively.The output features obtained from different convolutional layers can be combined to represent information about both high-frequency and low-frequency signals.In this paper,we propose an encoder-decoder framework called octave hierarchical network(Octave-H),which is based on the U-Network(U-Net)architec-ture and utilizes an octave convolutional neural network and a hierarchical feature learning module for performing crack detection.The proposed octave convolution is capable of extracting multi-fre-quency feature maps,capturing both fine details and global cracks.We propose a hierarchical feature learning module that merges multi-frequency-scale feature maps with different levels(high and low)of octave convolutional layers.To verify the superiority of the proposed Octave-H,we employed the CrackForest dataset(CFD)and AigleRN databases to evaluate this method.The experimental results demonstrate that Octave-H outperforms other algorithms with satisfactory performance.
基金supported by the National Natural Science Foundation of China(No.62176034)the Science and Technology Research Program of Chongqing Municipal Education Commission(No.KJZD-M202300604)the Natural Science Foundation of Chongqing(Nos.cstc2021jcyj-msxmX0518,2023NSCQ-MSX1781).
文摘Automatic crack detection of cement pavement chiefly benefits from the rapid development of deep learning,with convolutional neural networks(CNN)playing an important role in this field.However,as the performance of crack detection in cement pavement improves,the depth and width of the network structure are significantly increased,which necessitates more computing power and storage space.This limitation hampers the practical implementation of crack detection models on various platforms,particularly portable devices like small mobile devices.To solve these problems,we propose a dual-encoder-based network architecture that focuses on extracting more comprehensive fracture feature information and combines cross-fusion modules and coordinated attention mechanisms formore efficient feature fusion.Firstly,we use small channel convolution to construct shallow feature extractionmodule(SFEM)to extract low-level feature information of cracks in cement pavement images,in order to obtainmore information about cracks in the shallowfeatures of images.In addition,we construct large kernel atrous convolution(LKAC)to enhance crack information,which incorporates coordination attention mechanism for non-crack information filtering,and large kernel atrous convolution with different cores,using different receptive fields to extract more detailed edge and context information.Finally,the three-stage feature map outputs from the shallow feature extraction module is cross-fused with the two-stage feature map outputs from the large kernel atrous convolution module,and the shallow feature and detailed edge feature are fully fused to obtain the final crack prediction map.We evaluate our method on three public crack datasets:DeepCrack,CFD,and Crack500.Experimental results on theDeepCrack dataset demonstrate the effectiveness of our proposed method compared to state-of-the-art crack detection methods,which achieves Precision(P)87.2%,Recall(R)87.7%,and F-score(F1)87.4%.Thanks to our lightweight crack detectionmodel,the parameter count of the model in real-world detection scenarios has been significantly reduced to less than 2M.This advancement also facilitates technical support for portable scene detection.
基金supported by the National Key Research and Development Program of China(Grant Nos.2019YFB1600700 and 2019YFB1600701)the Wuhan Maritime Communication Research Institute(Grant No.2020MG001/050-22-CF).
文摘Rapid and accurate segmentation of structural cracks is essential for ensuring the quality and safety of engineering projects.In practice,however,this task faces the challenge of finding a balance between detection accuracy and efficiency.To alleviate this problem,a lightweight and efficient real-time crack segmentation framework was developed.Specifically,in the network model system based on an encoding-decoding structure,the encoding network is equipped with packet convolution and attention mechanisms to capture features of different visual scales in layers,and in the decoding process,we also introduce a fusion module based on spatial attention to effectively aggregate these hierarchical features.Codecs are connected by pyramid pooling model(PPM)filtering.The results show that the crack segmentation accuracy and real-time operation capability larger than 76%and 15 fps,respectively,are validated by three publicly available datasets.These wide-ranging results highlight the potential of the model for the intelligent O&M for cross-sea bridge.
基金Project(51178193)supported by the National Natural Science Foundation of ChinaProject(2009 353-344-570)supported by the Ministry of Transport of ChinaProject(2010-02-051)supported by the Transportation Department of Guangdong Province,China
文摘With the digital image technology,a crack detection method of reinforced concrete bridge was studied for the performance assessment.The effects including the image gray level,pixel rate,noise filter,and edge detection were analyzed considering cracks qualities.A computer program was developed by visual C++6.0 programming language to detect the cracks,which was tested by 15cases of bridge video images.The results indicate that the relative error is within 6%for cracks larger than 0.3 mm cracks and it is less than 10%for crack width between 0.2 mm and 0.3 mm.In addition,for the crack below 0.1 mm,the relative error is more than30%because the bridge is in safe stage and it is very difficult to detect the actual width of crack.
基金Supported by National Natural Science Foundation of China under Grant No.50379025.
文摘Structural cracks can change the frequency response function (FRF) of an offshore platform. Thus, FRF shifts can be used to detect cracks. When a crack at a specific location and magnitude occurs in an offshore structure, changes in the FRF can be measured. In this way, shifts in FRF can be used to detect cracks. An experimental model was constructed to verify the FRF method. The relationship between FRF and cracks was found to be non-linear. The effect of multiple cracks on FRF was analyzed, and the shift due to multiple cracks was found to be much more than the summation of FRF shifts due to each of the cracks. Then the effects of noise and changes in the mass of the jacket on FRF were evaluated. The results show that significant damage to a beam can be detected by dramatic changes in the FRF, even when 10% random noise exists. FRF can also be used to approximately locate the breakage, but it can neither be efficiently used to predict the location of breakage nor the existence of small hairline cracks. The FRF shift caused by a 7% mass change is much less than the FRF shift caused by the breakage of any beam, but is larger than that caused by any early cracks.
基金supported in part by the 14th Five-Year Project of Ministry of Science and Technology of China(2021YFD2000304)Fundamental Research Funds for the Central Universities(531118010509)Natural Science Foundation of Hunan Province,China(2021JJ40114)。
文摘Automatic pavement crack detection is a critical task for maintaining the pavement stability and driving safety.The task is challenging because the shadows on the pavement may have similar intensity with the crack,which interfere with the crack detection performance.Till to the present,there still lacks efficient algorithm models and training datasets to deal with the interference brought by the shadows.To fill in the gap,we made several contributions as follows.First,we proposed a new pavement shadow and crack dataset,which contains a variety of shadow and pavement pixel size combinations.It also covers all common cracks(linear cracks and network cracks),placing higher demands on crack detection methods.Second,we designed a two-step shadow-removal-oriented crack detection approach:SROCD,which improves the performance of the algorithm by first removing the shadow and then detecting it.In addition to shadows,the method can cope with other noise disturbances.Third,we explored the mechanism of how shadows affect crack detection.Based on this mechanism,we propose a data augmentation method based on the difference in brightness values,which can adapt to brightness changes caused by seasonal and weather changes.Finally,we introduced a residual feature augmentation algorithm to detect small cracks that can predict sudden disasters,and the algorithm improves the performance of the model overall.We compare our method with the state-of-the-art methods on existing pavement crack datasets and the shadow-crack dataset,and the experimental results demonstrate the superiority of our method.
基金Funded by the National Natural Science Foundation of China (51178305)the Key Projects in the Science & Technology Pillar Program of Tianjin (11ZCKFSF00300)
文摘Feasibility of a wave propagation-based active crack detection technique for nondestructive evaluations （NDE） of concrete structures with surface bonded and embedded piezoelectric-ceramic （PZT） patches was studied. At first, the wave propagation mechanisms in concrete were analyzed. Then, an active sensing system with integrated actuators/sensors was constructed. One PZT patch was used as an actuator to generate high frequency waves, and the other PZT patches were used as sensors to detect the propagating wave. Scattered wave signals from the damage can be obtained by subtracting the baseline signal of the intact structure from the recorded signal of the damaged structure. In the experimental study, progressive cracked damage inflicted artificially on the plain concrete beam is assessed by using both lateral and thickness modes of the PZT patches. The results indicate that with the increasing number and severity of cracks, the magnitude of the sensor output decreases for the surface bonded PZT patches, and increases for the embedded PZT patches.
文摘Pavement crack detection plays a crucial role in ensuring road safety and reducing maintenance expenses.Recent advancements in deep learning(DL)techniques have shown promising results in detecting pavement cracks;however,the selection of relevant features for classification remains challenging.In this study,we propose a new approach for pavement crack detection that integrates deep learning for feature extraction,the whale optimization algorithm(WOA)for feature selection,and random forest(RF)for classification.The performance of the models was evaluated using accuracy,recall,precision,F1 score,and area under the receiver operating characteristic curve(AUC).Our findings reveal that Model 2,which incorporates RF into the ResNet-18 architecture,outperforms baseline Model 1 across all evaluation metrics.Nevertheless,our proposed model,which combines ResNet-18 with both WOA and RF,achieves significantly higher accuracy,recall,precision,and F1 score compared to the other two models.These results underscore the effectiveness of integrating RF and WOA into ResNet-18 for pavement crack detection applications.We applied the proposed approach to a dataset of pavement images,achieving an accuracy of 97.16%and an AUC of 0.984.Our results demonstrate that the proposed approach surpasses existing methods for pavement crack detection,offering a promising solution for the automatic identification of pavement cracks.By leveraging this approach,potential safety hazards can be identified more effectively,enabling timely repairs and maintenance measures.Lastly,the findings of this study also emphasize the potential of integrating RF and WOA with deep learning for pavement crack detection,providing road authorities with the necessary tools to make informed decisions regarding road infrastructure maintenance.
文摘Crack detection procedures by different modal parameters are analyzed for identifying a crack and its location and magnitude in a jacket platform. The first ten natural frequencies and modal shapes of the jacket models are obtained by numerical experiments based on NASTRAN Code. A crack at different locations and of different magnitudes is imposed in the model at the underwater beams. Then, the modal evaluation parameters are calculated numerically, to illustrate the evaluation of modal parameter criteria used in jacket crack detection. The sensitivities of different modal parameters to different cracks are analyzed. A new technique is presented for predicting the approximate location of a breakage in the absence of the data of an intact model. This method can be used to detect a crack in underwater members by use of incomplete mode shapes of the top members of the jacket.
基金NCHRP Project,IDEA 223:Fatigue Crack Inspection using Computer Vision and Augmented Reality。
文摘Fatigue cracks that develop in civil infrastructure such as steel bridges due to repetitive loads pose a major threat to structural integrity.Despite being the most common practice for fatigue crack detection,human visual inspection is known to be labor intensive,time-consuming,and prone to error.In this study,a computer vision-based fatigue crack detection approach using a short video recorded under live loads by a moving consumer-grade camera is presented.The method detects fatigue crack by tracking surface motion and identifies the differential motion pattern caused by opening and closing of the fatigue crack.However,the global motion introduced by a moving camera in the recorded video is typically far greater than the actual motion associated with fatigue crack opening/closing,leading to false detection results.To overcome the challenge,global motion compensation(GMC)techniques are introduced to compensate for camera-induced movement.In particular,hierarchical model-based motion estimation is adopted for 2D videos with simple geometry and a new method is developed by extending the bundled camera paths approach for 3D videos with complex geometry.The proposed methodology is validated using two laboratory test setups for both in-plane and out-of-plane fatigue cracks.The results confirm the importance of motion compensation for both 2D and 3D videos and demonstrate the effectiveness of the proposed GMC methods as well as the subsequent crack detection algorithm.
基金Project supported by the National Natural Science Foundation of China(No.50608036)Program for New Century Excellent Talents in Universities.
文摘An alternative technique for crack detection in a Timoshenko beam based on the first anti-resonant frequency is presented in this paper. Unlike the natural frequency, the anti-resonant frequency is a local parameter rather than a global parameter of structures, thus the proposed technique can be used to locate the structural defects. An impedance analysis of a cracked beam stimulated by a harmonic force based on the Timoshenko beam formulation is investigated. In order to characterize the local discontinuity due to cracks, a rotational spring model based on fracture mechanics is proposed to model the crack. Subsequently, the proposed method is verified by a numerical example of a simply-supported beam with a crack. The effect of the crack size on the anti-resonant frequency is investigated. The position of the crack of the simply-supported beam is also determined by the anti-resonance technique. The proposed technique is further applied to the ＂contaminated＂ anti-resonant frequency to detect crack damage, which is obtained by adding 1-3% noise to the calculated data. It is found that the proposed technique is effective and free from the environment noise. Finally, an experimental study is performed, which further verifies the validity of the proposed crack identification technique.
基金supported by the Jiangsu Provincial Key R&D Programme(BE2020034)China Huaneng Group Science and Technology Project(HNKJ20-H72).
文摘Harsh working environments and wear between blades and other unit components can easily lead to cracks and damage on wind turbine blades.The cracks on the blades can endanger the shafting of the generator set,the tower and other components,and even cause the tower to collapse.To achieve high-precision wind blade crack detection,this paper proposes a crack fault-detection strategy that integratesGated ResidualNetwork(GRN),a fusionmodule and Transformer.Firstly,GRNcan reduce unnecessary noisy inputs that could negatively impact performancewhile preserving the integrity of feature information.In addition,to gain in-depth information about the characteristics of wind turbine blades,a fusionmodule is suggested to implement the information fusion of wind turbine features.Specifically,each fan feature ismapped to a one-dimensional vector with the same length,and all one-dimensional vectors are concatenated to obtain a two-dimensional vector.And then,in the fusion module,the information fusion of the same characteristic variables in the different channels is realized through the Channel-mixing MLP,and the information fusion of different characteristic variables in the same channel is realized through the ColumnmixingMLP.Finally,the fused feature vector is input into the Transformer for feature learning,which enhances the influence of important feature information and improves the model’s anti-noise ability and classification accuracy.Extensive experimentswere conducted on the wind turbine supervisory control and data acquisition(SCADA)data froma domesticwind field.The results show that compared with other state-of-the-artmodels,including XGBoost,LightGBM,TabNet,etc.,the F1-score of proposed gated fusion based Transformer model can reach 0.9907,which is 0.4%-2.09% higher than the comparedmodels.Thismethod provides amore reliable approach for the condition detection and maintenance of fan blades in wind farms.
基金National Natural Science Foundation of China(No.51225501No.51035007)Program for Changjiang Scholars and Innovative Research Team in University
文摘A high-precision identification method for steam turbine rotor crack is presented. By providing me nrst three measured natural frequencies, contours for the specified natural frequency are plotted in the same coordi- nate, and the intersection of the three curves predicts the crack location and size. The cracked rotor system is mod- eled using B-spline wavelet on the interval （BSWI） finite element method, and a method based on empirical mode decomposition （EMD） and Laplace wavelet is implemented to improve the identification precision of the first three measured natural frequencies. Compared with the classical nondestructive testing, the presented method shows its effectiveness and reliability. It is feasible to apply this method to the online health monitoring for rotor structure.
文摘This study aimed to propose road crack detection method based on infrared image fusion technology.By analyzing the characteristics of road crack images,this method uses a variety of infrared image fusion methods to process different types of images.The use of this method allows the detection of road cracks,which not only reduces the professional requirements for inspectors,but also improves the accuracy of road crack detection.Based on infrared image processing technology,on the basis of in-depth analysis of infrared image features,a road crack detection method is proposed,which can accurately identify the road crack location,direction,length,and other characteristic information.Experiments showed that this method has a good effect,and can meet the requirement of road crack detection.
基金jointly supported by the National Natural Science Foundation of China(No.52308332)the China Postdoctoral Science Foundation(Grant No.2022M712787).
文摘The traditional You Only Look Once(YOLO)series network models often fail to extract satisfactory features for road detection,due to the limited number of defect images in the dataset.Additionally,most open-source road crack datasets contain idealized cracks that are not suitable for detecting early-stage pavement cracks with fine widths and subtle features.To address these issues,this study collected a large number of original road surface images using road detection vehicles.A large-capacity crack dataset was then constructed,with various shapes of cracks categorized as either cracks or fractures.To improve the training performance of the YOLOv5 algorithm,which showed unsatisfactory results on the original dataset,this study used median filtering to preprocess the crack images.The preprocessed images were combined to form the training set.Moreover,the Coordinate Attention(CA)attention module was integrated to further enhance the model’s training performance.The final detection model achieved a recognition accuracy of 88.9%and a recall rate of 86.1%for detecting cracks.These findings demonstrate that the use of image preprocessing technology and the introduction of the CA attention mechanism can effectively detect early-stage pavement cracks that have low contrast with the background.
基金funded by the Jiangxi SASAC Science and Technology Innovation Special Project and the Key Technology Research and Application Promotion of Highway Overload Digital Solution.
文摘In response to the challenges in highway pavement distress detection,such as multiple defect categories,difficulties in feature extraction for different damage types,and slow identification speeds,this paper proposes an enhanced pavement crack detection model named Star-YOLO11.This improved algorithm modifies the YOLO11 architecture by substituting the original C3k2 backbone network with a Star-s50 feature extraction network.The enhanced structure adjusts the number of stacked layers in the StarBlock module to optimize detection accuracy and improve model efficiency.To enhance the accuracy of pavement crack detection and improve model efficiency,three key modifications to the YOLO11 architecture are proposed.Firstly,the original C3k2 backbone is replaced with a StarBlock-based structure,forming the Star-s50 feature extraction backbone network.This lightweight redesign reduces computational complexity while maintaining detection precision.Secondly,to address the inefficiency of the original Partial Self-attention(PSA)mechanism in capturing localized crack features,the convolutional prior-aware Channel Prior Convolutional Attention(CPCA)mechanism is integrated into the channel dimension,creating a hybrid CPC-C2PSA attention structure.Thirdly,the original neck structure is upgraded to a Star Multi-Branch Auxiliary Feature Pyramid Network(SMAFPN)based on the Multi-Branch Auxiliary Feature Pyramid Network architecture,which adaptively fuses high-level semantic and low-level spatial information through Star-s50 connections and C3k2 extraction blocks.Additionally,a composite dataset augmentation strategy combining traditional and advanced augmentation techniques is developed.This strategy is validated on a specialized pavement dataset containing five distinct crack categories for comprehensive training and evaluation.Experimental results indicate that the proposed Star-YOLO11 achieves an accuracy of 89.9%(3.5%higher than the baseline),a mean average precision(mAP)of 90.3%(+2.6%),and an F1-score of 85.8%(+0.5%),while reducing the model size by 18.8%and reaching a frame rate of 225.73 frames per second(FPS)for real-time detection.It shows potential for lightweight deployment in pavement crack detection tasks.
文摘The operational and regional conditions to which the prestressed concrete sleeper(PCS)is subjected in a railway track significantly contribute to its performance and durability.Maintaining the health of PCS poses challenges,and one of these issues involves the potential occurrence of longitudinal cracks in reinforcing bars,which can be caused by various constructional,functional,and environmental factors.Longitudinal cracks in PCS compromise the structural performance,resulting in a reduced capacity to withstand the loads exerted by moving vehicles.The current evaluations not only fail to yield a precise parameter for estimating the behavior and response of the PCS,but they also overlook the specific conditions of the PCS,such as prestressing,and only provide limited information regarding existing damage.Balancing the need for accurate evaluation with consideration of costs and resources,and making informed decisions about maintenance and track performance enhancement,has become a multifaceted challenge in ensuring a robust PCS assessment.This research introduces a novel methodology to improve the evaluation of mechanical and geometrical parameters of PCS over their operational lifespan.The objective is to enhance the accuracy of PCS performance estimation by concentrating on detecting longitudinal cracks.The suggested approach seamlessly integrates model updating methods and the finite element(FE)approach to achieve an accurate and timely assessment of PCS conditions.This comprehensive examination scrutinizes the methodology by applying artificial cracks to the PCS.In addition to introducing this assessment approach,a detailed examination is conducted on a laboratory-simulated PCS featuring various combinations of longitudinal cracks measuring 40,80,and 120 cm in length.This systematic and rigorous approach ensures the reliability and robustness of the methodology.Ultimately,the parameters of cross-sectional area,moment of inertia,and modulus of elasticity,which significantly impact the performance of this sleeper,are explored and demonstrated through functional methodologies.The findings suggest that assessing and addressing damage should be conducted through a comprehensive and integrated procedure,taking into account the actual conditions of the PCS.Longitudinal cracks lead to a substantial decrease in the performance of these components in railway tracks.By applying the proposed methods,it is anticipated that the evaluation error for these components will be reduced by approximately 30%compared to visual inspections,particularly in predicting the extent of damage for cracks measuring up to 120 cm.This research has the potential to significantly enhance the evaluation of PCS performance and mitigate the impact of longitudinal cracks on the safety and longevity of ballasted railway tracks in desert areas.
基金supported by the National Natural Science Foundation of China(Grant Nos.61972136,41874148,and 42174178)the Natural Science and Foundation of Hubei Province(No.2020CFB497)+4 种基金the Hubei Provincial Department of Education Outstanding Youth Scientific Innovation Team Support Foundation(Nos.T201410 and T2020017)the Natural Science Foundation of Education Department of Hubei Province(No.B2020149)the Science and Technology Research Project of the Education Department of Hubei Province(No.Q20222704)Natural Science Foundation of Xiaogan City(Nos.XGKJ2022010095 and XGKJ2022010094)The funding is a foreign expert project of Henan Province(No.HNGD2023027).
文摘Disaster-resilient dams require accurate crack detection,but machine learning methods cannot capture dam structural reaction temporal patterns and dependencies.This research uses deep learning,convolutional neural networks,and transfer learning to improve dam crack detection.Twelve deep-learning models are trained on 192 crack images.This research aims to provide up-to-date detecting techniques to solve dam crack problems.The finding shows that the EfficientNetB0 model performed better than others in classifying borehole concrete crack surface tiles and normal(undamaged)surface tiles with 91%accuracy.The study’s pre-trained designs help to identify and to determine the specific locations of cracks.
文摘This paper introduces a crack detection example of the prestressed box girder in a certain project. The morphology of the box girder cracks was surveyed and mapped. The length, width, and depth of the cracks were inspected, and the strength and reinforcement configuration of the components were tested. The test results indicate that the strength and reinforcement configuration of the inspected components meet the design requirements. The crack at the end of the top plate of the box girder is a local compressive crack at the anchorage end. The width and length of the crack on the bottom surface of the top plate are not significant, and the depth is relatively shallow. Judging from the crack morphology, this crack is identified as a temperature crack. Additionally, based on the treatment measures for cracks of different widths, the treatment measures for the cracks of the components in this project are derived, providing a reference basis for similar projects in the future.
基金supported in part by the Jiangsu Province Construction System Science and Technology Project(No.2024ZD056)the Research Development Fund of Xi’an Jiaotong-Liverpool University(No.RDF-24-01-097).
文摘Computer-vision and deep-learning techniques are widely applied to detect,monitor,and assess pavement conditions including road crack detection.Traditional methods fail to achieve satisfactory accuracy and generalization performance in for crack detection.Complex network model can generate redundant feature maps and computational complexity.Therefore,this paper proposes a novel model compression framework based on deep learning to detect road cracks,which can improve the detection efficiency and accuracy.A distillation loss function is proposed to compress the teacher model,followed by channel pruning.Meanwhile,a multi-dilation model is proposed to improve the accuracy of the model pruned.The proposed method is tested on the public database CrackForest dataset(CFD).The experimental results show that the proposed method is more efficient and accurate than other state-of-art methods.
