Damage detection method for large structures using static and dynamic strain data from distributed fiber optic sensor

A method for damage detection applicable to large slender steel structures such as towers of large-scale wind turbines, long-span bridges, and high-rise buildings is presented. This method is based on continuous strain data obtained by distributed fiber optic sensor (FOS) and neural network (NN) analysis. An analytical model for cracked beam based on an energy balance approach was used to train a NN. The continuous static strains and the natural frequencies obtained from the distributed FOSs were used as the input to the trained NN to estimate the crack depths and locations. An experimental study was carried out on a cracked cantilever beam to verify the present method for damage identification. The cracks were inflicted on the beam, and static and free vibration tests were performed for the intact case and the damage cases. The distributed FOSs were used to measure the continuous strains. The damage estimation was carried out for the 5 damage cases using the NN technique. It has been found that the identified crack depths and locations agree reasonably well with the inflicted cracks on the structure.     

Position-Invariant Neural Network for Digital Pavement Crack Analysis

Abstract:   This article presents an integrated neural network-based crack imaging system to classify crack types of digital pavement images. This system includes three neural networks: (1) image-based neural network, (2) histogram-based neural network, and (3) proximity-based neural network. These three neural networks were developed to classify various crack types based on the subimages (crack tiles) rather than crack pixels in digital pavement images. These spatial neural networks were trained using artificially generated data following the Federal Highway Administration (FHWA) guidelines. The optimal architecture of each neural network was determined based on the testing results from different sets of the number of hidden units, learning coefficients, and the number of training epochs. To validate the system, actual pavement pictures taken from pavements as well as the computer-generated data were used. The proximity value is determined by computing relative distribution of crack tiles within the image. The proximity-based neural network effectively searches the patterns of various crack types in both horizontal and vertical directions while maintaining its position invariance. The final result indicates that the proximity-based neural network produced the best result with the accuracy of 95.2% despite its simplest neural network structure with the least computing requirement.     

A grid-based classification and box-based detection fusion model for asphalt pavement crack

Crack identification is essential for the preventive maintenance of asphalt pavement. Through periodic inspection, the characteristics of existing and emerging cracks can be obtained, and the pavement condition index can be calculated to assess pavement health. The most common method to estimate the area of cracks in an image is to count the number of grid cells or boxes that cover the cracks in an image. Accurate and efficient crack identification is the premise of crack assessment. However, the current patch-based classification method is limited by the receptive field and cannot be used to directly classify cracks. Furthermore, the postprocessing algorithm in anchor-based detection is not explicitly optimized for crack topology. In this paper, a new model, which is the fusion of grid-based classification and box-based detection based on You Only Look Once version 5 (YOLO v5) is developed and described for the first time. The accuracy and efficiency of the model are high partly due to the implementation of a shared backbone network, multi-task learning, and joint training. The non-maximum suppression (NMS)–area-reduction suppression (ARS) algorithm is presented to filter redundant, overlapping prediction boxes in the postprocessing stage for the crack topology, and the mapping and matching algorithm is proposed to combine the advantages of both the grid-based and box-based models. A double-labeled dataset containing tens of thousands of asphalt pavement images is assembled, and the proposed method is verified on the test set. The fusion model has superior performance over the individual classification and detection models, and the proposed NMS-ARS algorithm further improves the detection accuracy. Experimental results demonstrate that the presented method effectively realizes automatic crack identification for asphalt pavement.     

基于图象子块分布特性的路面破损图象特征提取

由于路面破损形式的多种多样,造成路面破损分类[1]成为一大难题,这极大的限制了路面破损自动检测的普及和发展,使得路面破损自动检测即使在发达国家也普及得不够理想。本文在前文提出的破损密度因子的基础上,进一步设计了出方向密度因子,得到一种基于图象子块分布特性的路面破损识别算法。通过仿真,验证了其对常见的5种路面破损类型进行分类的可行性。为了进一步验证我们提出的识别算法,论文选择了另外一种路面破损分类算法,即PROXIMITY算法进行神经网络仿真对比。神经网络的训练样本是两组,测试样本也是两组,进行了四次仿真对比。四次仿真结果都显示方向密度因子算法优于PROXIMITY算法。     

Instantaneous pavement condition evaluation using non-destructive neuro-evolutionary approach

In this paper, a hybrid neural network (NN)-genetic algorithm (GA) based non-destructive pavement auscultation method for instantaneous airfield infrastructure condition assessment is discussed. NNs are employed for finite element aided forward prediction of pavement surface deflections resulting from non-destructive test impulse loading and the GAs are used for global optimisation of the pavement structural parameters by matching the NN predicted deflections with the measured pavement response. This hybrid approach takes advantage of the non-linear estimation capability provided by neural networks trained using finite element (FE) solutions in modelling the stress-dependent behaviour of unbound pavement geo-materials while improving the robustness to measurement uncertainty through the application of genetic algorithms. The performance of the developed hybrid pavement auscultation technique is evaluated through extensive field studies conducted at a state-of-the-art full-scale airfield pavement test facility. The results show that this approach is promising for real-time condition evaluation of airfield pavement infrastructure systems.     

A Pavement Crack Detection Method Combining 2D with 3D Information Based on Dempster‐Shafer Theory

Pavement cracking is one of the main distresses presented in the road surface. Objective and accurate detection or evaluation for these cracks is an important task in the pavement maintenance and management. In this work, a new pavement crack detection method is proposed by combining two‐dimensional (2D) gray‐scale images and three‐dimensional (3D) laser scanning data based on Dempster‐Shafer (D‐S) theory. In this proposed method, 2D gray‐scale image and 3D laser scanning data are modeled as a mass function in evidence theory, and 2D and 3D detection results for pavement cracks are fused at decision‐making level. The experimental results show that the proposed method takes advantage of the respective merits of 2D images and 3D laser scanning data and therefore improves the pavement crack detection accuracy and reduces recognition error rate compared to 2D image intensity‐based methods.     

沥青混凝土路面非荷载性裂缝形成机理研究

裂缝是沥青混凝土路面的主要缺陷之一。早期裂缝对路面使用性能不会产生明显影响。然而，裂缝的扩展最终会导致沥青路面结构性破坏。引起沥青混凝土路面裂缝的原因错综复杂，不过可以将这些复杂原因引起的裂缝归结为荷载性裂缝和非荷载性裂缝两大类。温度裂缝和反射裂缝是非荷载裂缝的两种主要形式。从非荷载性裂缝的角度出发，详细分析了温度收缩裂缝、温度梯度裂缝和反射裂缝形成的机理，指出温度梯度是引起路面横向裂缝的主要原因，而反射裂缝是半刚性基层裂缝和温度裂缝综合作用的结果。     

橡胶沥青在路面养护中的应用与研究

研究橡胶沥青在路面养护中的3种应用形式与功能:橡胶沥青应力吸收层延缓裂缝反射;橡胶沥青同步碎石封层作为预防性养护可以弥合面层裂缝;橡胶沥青混合料面层提高路用性能。实践表明,橡胶沥青不仅在路面养护中大有作为,而且符合"资源节约型、环境友好型"社会的建设理念。     

Quantitative road crack evaluation by a U-Net architecture using smartphone images and Lidar data

Road cracks are a major concern for administrators. Visual inspection is labor-intensive. The accuracy of previous algorithms for detecting cracks in images requires improvement. Further, the length and thickness of cracks must be estimated. Light detection and ranging (Lidar), a standard smartphone feature is used to develop a method for the completely automatic, accurate, and quantitative evaluation of road cracks. The two contributions of this study are as follows. To achieve the highest segmentation accuracy, U-Net is combined with data augmentation and morphology transform. To calculate the crack length and thickness, crack images are registered into Lidar color data. The proposed algorithm was validated using a public database of road cracks and those measured by the authors. The algorithm was 95% accurate in determining crack length. The coefficient of determination for thickness estimation accuracy was 0.98 addressing various crack shapes and asphalt pavement patterns.     

利用混凝土损伤塑性模型评价CRCP+AC复合式路面的损伤演变

在连续配筋混凝土复合式路面(CRCP+AC)的研究中,CRCP面板的裂缝形态和分布模式是影响今后路面使用性能和生命周期的重要因素。研究采用ABAQUS有限元程序中的混凝土损伤塑性模型(CDP)表征CRCP面板的力学特性,结合三维瞬态热传导分析的温度场数据,分析了变温条件下的结构损伤情况;并进一步分析复合式路面在温度变化和交通荷载耦合作用下,CRCP面板损伤的演化规律以及裂缝张开闭合的行为特征。研究认为沥青面层的作用不仅仅局限在功能性方面,它可以明显改善CRCP面板的受力状况;复合式路面设计中应根据CRCP面板的温缩性能和损伤性能综合考虑沥青面层的合理厚度。     

对高速公路沥青路面裂缝成因的探析

沥青路面的裂缝是一项比较突出的病害问题,主要受到沥青的力学特性和施工不当的影响。本文对沥青路面裂缝产生的类型进行了分类,并对其成因进行了探析,对于我国高速公路沥青路面设计及施工具有一定的参考意义。     

面层与基层层间摩擦系数对应力强度因子影响的研究

开裂为沥青混凝土路面中存在的主要问题。基于线弹性断裂力学理论,采用ABAQUS软件中的平面应变单元法,分别分析了变温作用下裂缝的长度、面层与基层层间摩擦系数态对含表面裂缝和含反射沥青混凝土路面的应力强度因子分布规律的影响。得出了含表面裂缝和反射裂缝路面体的应力强度因子均随着裂缝长度的增加逐渐增大。表面裂缝裂尖位于面层中时,应力强度因子随着摩擦系数的增加而逐渐减小;裂尖位于基层中时,应力强度因子随着摩擦系数的增加而逐渐增大。反射裂缝裂尖位于基层中时,应力强度因子随着摩擦系数的增加而减小;反射裂缝裂尖位于面层中且长度小于某一值时,应力强度因子随着摩擦系数的增加而增大;反射裂缝裂尖位于面层中且裂缝长度大于某一值时,应力强度因子随着摩擦系数的增加而减小。     

新建沥青路面裂缝控制综述

分别对施工裂缝、收缩裂缝、沉陷性裂缝等新建沥青路面裂缝的原因进行了分析，并介绍了各种裂缝的特征，提出了相应的预防措施，以确保沥青路面质量，保证道路行车畅通，从而充分发挥沥青路面的经济效益。     

基于计算机视觉技术的结构表面裂缝检测方法研究

计算机视觉技术用于混凝土结构表面裂缝检测,具有现场检测方便、效率高、客观性强的特点,但图像数据分析是该技术的核心,其中裂缝提取与定量测量较为复杂。为提高裂缝图像处理效率和准确率,将深度学习和数字图像处理技术相结合,提出一种裂缝检测方法。建立基于深度卷积神经网络的裂缝识别模型,在图像上自动定位裂缝并结合图像局域阈值分割方法提取裂缝。在裂缝宽度定量测量方面,采用双边滤波算法和三段线性变换对裂缝图像进行预处理,提高了裂缝边缘识别的精确度。通过改进边缘梯度法,实现裂缝最大宽度的定位和裂缝最大宽度的自动获取。该研究为全自动识别裂缝图像及高精度测量裂缝宽度提供了一种解决方法。     

Beamlet Transform‐Based Technique for Pavement Crack Detection and Classification

Abstract: This article presents a Beamlet transform‐based approach to automatically detect and classify pavement cracks in digital images. The proposed method uses a pavement distress image enhancement algorithm to correct the nonuniform background illumination by calculating the multiplicative factors that eliminate the background lighting variation. To extract linear features such as surface cracks from the pavement images, the image is partitioned into small windows and a Beamlet transform‐based algorithm is applied. The crack segments are then linked together and classified into four types: vertical, horizontal, transversal, and block. Simulation results show that the method is effective and robust in the extraction of cracks on a variety of pavement images.     

Nondestructive evaluation of the depth of surface-breaking cracks in concrete pipes

Surface-breaking cracks affect the material and structural properties of concrete pipes. Therefore, the nondestructive evaluation of the crack depth is important to assess the serviceability of these pipes, which are commonly used in underground infrastructure and trenchless installations (micro-tunneling). This paper presents theoretical, numerical and experimental results for the depth evaluation of surface-breaking cracks. The wall of a concrete pipe is represented as a plate in the numerical and the analytical studies. In the experiments, an ultrasonic piezoelectric transmitter is used as a source. The propagation of the ultrasonic pulse is analyzed using the wavelet transform. A newly proposed wavelet transmission coefficient (WTC) is measured using an equal spacing configuration for the crack depth evaluation in concrete pipes and concrete plates. The results from laboratory and in situ tests show good potential for the practical application of the WTC for the depth evaluation of surface-breaking cracks.     

石灰土基层沥青混凝土面层结构路面裂缝防治

归纳了横向裂缝对石灰土基层、沥青混凝土面层结构路面产生的危害,分析了石灰土基层的特性及裂缝形成原因,并提出了施工前的预防措施和施工后的补救措施,从而确保道路行车安全和使用功能。     

沥青路面表面裂缝扩展研究

基于线弹性断裂力学,分析了沥青路面开裂以后,裂缝尖端的应力强度因子与路面温度分布和路面材料特性参数之间的关系,分析结果表明,在沥青路面的抗裂设计中,合理设计沥青面层的厚度和优选温缩性小的路面材料是提高沥青路面抗裂性能的两个重要方面。     

Prediction of the crack condition of highway pavements using machine learning models

Departments of Transportation regularly evaluate the condition of pavements through visual inspections, nondestructive evaluations, image recognition models and learning algorithms. The above methodologies, though efficient, have drawn attention due to their subjective errors, uncertainties, noise effects and overfitting. To improve on the outcomes of the shallow learning models already used in pavement crack prediction, this paper reports on an investigation of the use of recursive partitioning and artificial neural networks (ANN; deep learning frameworks) in predicting the crack rating of pavements. Explanatory variables such as the average daily traffic and truck factor, roadway functional class, asphalt thickness, and pavement condition time series data are employed in the model formulation. Overall, it is observed that the recursive partitioning (regression tree – R² > 0.8 and classification tree – R² > 0.6) and ANN (continuous response – R² > 0.8 and categorical response – R² > 0.6) are compelling machine learning models for the prediction of the crack ratings based on their goodness-of-fit statistics, mean absolute deviation (MAD < 0.4) and the root mean square errors (RMSE between 0.30 and 0.65).     

The development of a machine vision-assisted,teleoperated pavement crack sealer

Crack sealing is a maintenance technique commonly used to prevent water and debris penetration and reduce future degradation in pavement. The conventional crack sealing operations are, however, dangerous, costly, and labor-intensive. Labor turnover and training are also increasing problems related to crack sealing crews. Automating crack sealing will improve productivity and quality, and offer safety benefits by getting workers off the road. The reduction in crew size and the increase in productivity of the automated sealing process will be translated directly into significant potential cost savings. The main objective of this study is to develop an automated system for sealing cracks in pavement, and to validate the developed system through field trials. A machine vision algorithm, which is composed of noise elimination, crack network mapping and modeling, and path planning, was developed to operate the proposed automated system effectively. Extension of the algorithms and tools presented in the study to other applications is also recommended for future studies.