Structural Local Damage Detection Methods For Bridges Based On Computer Vision Techniques

Bridges are of significance to countries and regions as economic lifelines.Bridges inevitably suffer from environmental erosion,dynamic loads and sudden disasters(e.g.earthquakes)in the whole service period,resulting in the gradual initiation,development and accumulation of structural damages,causing the continuous performance deterioration and even catastrophic accidents.At present,structural damage detection,model correction and safety asessment are commonly performed by dynamic inversion methods based on modal identification,which only process incomplete acceleration monitoring information on limited measuring positions.In addition,these methods rely on the overall property of structural frequency and are not sensitive to slight damages,especially when coupled with complicated non-homogeneous interferences.Conventional identification methods are always not universal in the practical applications of real scenes.In application,manual inspections are frequently adopted to visually inspect structures and evaluate damage degrees according to previous experiences,which heavily depend on subjective consciousness and are often descriptive,inaccurate and unreliable.Meanwhile,they always consume expensive labor,time and financial costs.To solve these issues,this study focuses on structural damage detection by optical images,which are obtained by ordinary consumer cameras from real-world structures and contain complex disturbances.In consideration with the unique characteristic of the individual task for different structural components,this study proposes autonomous monitoring and smart detection frameworks t hrough local pixel threshold processing,unsupervised gaussian clustering,establishing stacked Restricted Boltzmann Machine,fusion directed acyclic graph convolutional neural networks and region proposal networks to accomplish goals of primary image processing,image statistical feature modeling,multi-level feature fusion and attention-based object detection.Specifically,this study investigates the corrosion fatigue degradation of high strength steel wires in stay-cables,tiny fatigue crack identification in steel box girders and multi-type seismic damage classification and localization of reinforced concrete pier columns.The main contents and results are as follows:A novel non-destructive assessment method for in-service cable corrosion status identification and fatigue life degradation is proposed based on the corrosion random process of high-strength steel wires and image statistical characteristics,breaking the destructive limits of traditional cable replacement and inspection engineering.By time-dependent statistical modeling of corrosion process and unsupervised clustering of Gaussian mxiture model,the mapping relation model from corrosion feature space to key parameters of fatigue life is established and successfully applied on the corrosion degradation assessment of cable bridges in China.Prediction errors of corrosion fatigue life keep within 1 6% under a variety of stress amplitude codnitions.A refined identification method is proposed for tiny fatigue cracks in real-world steel box girders accompanied with complicated background disturbances,which solves the problems that conventional modal identification methods based on dynamic inversion are lack of sensitivity on early damages and can only identify late severe damages.By constructing deep Restricted Bolzmann Machines and fu sion directed acyclic graph convolutional neural networks,multi-level feature extraction and fusion of primary detailed and advanced abstract features are achieved.Transfer tests on several large-span bridges with steel box girder in China show that identification accuracies on all kinds of test samples exceed 93%,which varifies its robustness and portability.A two-stage identification and localization framework for multi-type seismic damages of reinforced concrete structures is established based on the regional proposal and attention-wise mechanism,conquering the bottleneck of multi-scale damage detection coupled with inhomogeneous background.Multi-scale candidate regions are generated with different sizes and aspect ratio.A multi-task loss function is constructed based on the combination of cross entropy classification and smooth L1 regression of rectangular damage regions' coordinates.Rectangular boxes are regressed for localization as well as the corresponding category labels and guarantee probabilities.The average precision of identifications for concrete cracking,spalling,steel bar exposure and buckling exceeds 80%,and the average coverage of damaged regions exceeds 88%.This study investigates structurl damage detection methods for civil infrastructures based on computer vision techniques,overcomes the deficiencies of traditional methods and greatly improves the results' accuracy and stability of structural condition assessment and highly enhances the automation and intelligence of state evaluation for civil engineering.It makes great contributions as a foundation for the popularization of intelligent detection,automatic damage identification and state assessment of civil infrastructures.