Automated crack evaluation of a high‐rise bridge pier using a ring‐type climbing robot

This article proposes a deep learning‐based automated crack evaluation technique for a high‐rise bridge pier using a ring‐type climbing robot. First, a ring‐type climbing robot system composed of multiple vision cameras, climbing robot, and control computer is developed. By spatially moving the climbing robot system along a target bridge pier with close‐up scanning condition, high‐quality raw vision images are continuously obtained. The raw vision images are then processed through feature control‐based image stitching, deep learning‐based semantic segmentation, and Euclidean distance transform–based crack quantification algorithms. Finally, a digital crack map on the region of interest (ROI) of the target bridge pier can be automatically established. The proposed technique is experimentally validated using in situ test data obtained from Jang–Duck bridge in South Korea. The test results reveal that the proposed technique successfully evaluates cracks on the entire ROI of the bridge pier with precision of 90.92% and recall of 97.47%.     

结构表面裂缝数字图像法识别研究综述与前景展望

裂缝反映结构受力状态与安全性、耐久性,是结构现场安全性检测监测以及结构模型试验研究的重要指标之一.现有的人工裂缝识别技术难以满足工程现场与实验室需求,操作中存在测不准、高空多、效率低、记不全等缺点.相比之下,采用数字图像法进行结构表面裂缝识别,具有便捷、自动、定量、准确等优势.文章对结构表面裂缝数字图像法识别研究进行系...     

Structure‐aware 3D reconstruction for cable‐stayed bridges: A learning‐based method

A powerful deep learning‐based three‐dimensional (3D) reconstruction method for reconstructing structure‐aware semantic 3D models of cable‐stayed bridges is proposed herein. Typically, conventional bridge semantic 3D model reconstruction methods are not robust when low‐quality point clouds are used. Furthermore, they are suited particularly for their respective fields and less generalized for cable‐stayed bridges. Hence, a structure‐aware learning‐based cable‐stayed bridge 3D reconstruction framework is proposed. The encoder part of the network uses both multiview images and a photogrammetric point cloud as input, whereas the decoder part uses a recursive binary tree network to model a high‐level structural relation graph and low‐level 3D geometric shapes. Two actual cable‐stayed bridges are employed as examples to evaluate the proposed method. Test results demonstrate that the proposed method successfully reconstructs the bridge model with structural components and their relations. Quantitative results indicate that the predicted models achieved an average F₁ score of 99.01%, a Chamfer distance of 0.0259, and a mesh‐to‐cloud distance of 1.78 m. The achieved result is similar to that obtained using the manual reconstruction approach in terms of component‐wise accuracy, and it is considerably better than that obtained using the manual approach in terms of spatial accuracy. In addition, the proposed recursive binary tree network is robust to noise and partial scans. The potential applications of the obtained 3D bridge models are discussed.     

基于多旋翼无人机的桥梁外观检测技术及工程应用研究

针对传统桥梁外观检测手段在实践中存在的诸多不足,提出基于多旋翼无人机的桥梁外观检测技术。该技术利用无人机搭载摄像头近距离提取桥梁表面图像,采用图像处理技术的方法检出表面缺陷。结合厦门海沧大桥工程,应用结果表明该技术能够高效、全面和准确地完成桥梁外观检测作业。     

Real‐time crack assessment using deep neural networks with wall‐climbing unmanned aerial system

Crack information provides important evidence of structural degradation and safety in civil structures. Existing inspection methods are inefficient and difficult to rapidly deploy. A real‐time crack inspection method is proposed in this study to address this difficulty. Within this method, a wall‐climbing unmanned aerial system (UAS) is developed to acquire detailed crack images without distortion, then a wireless data transmission method is applied to fulfill real‐time detection requirements, allowing smartphones to receive real‐time video taken from the UAS. Next, an image data set including 1,330 crack images taken by the wall‐climbing UAS is established and used for training a deep‐learning model. For increasing detection speed, state‐of‐the‐art convolutional neural networks (CNNs) are compared and employed to train the crack detector; the selected model is transplanted into an android application so that the detection of cracks can be undertaken on a smartphone in real time. Following this, images with cracks are separated and crack width is calculated using an image processing method. The proposed method is then applied to a building where crack information is acquired and calculated accurately with high efficiency, thus verifying the practicability of the proposed method and system.     

基于无人飞机成像的桥梁裂缝宽度识别可行性研究

基于传统桥梁检测车裂缝宽度检测存在阻碍交通、受到桥型限制、使用维护费用高等问题。该文以旋翼无人飞机为工作平台,为满足0.2mm以上桥梁裂缝宽度识别要求,采用IMETRUM非接触式测量仪验证无人机悬停状态下机载成像质量具有可靠性;通过加装机载三点激光测距仪,测量物距并推导成像平面与被测平面夹角,计算并修正裂缝图像像素解析度;设计适于无人机成像的图像预处理程序、构建基于支持向量机（SVM）裂缝形态智能提取训练模型、裂缝法向实际宽度计算方法。以湘潭市湘江二大桥为研究对象,通过对实桥进行无人机裂缝宽度识别,并与传统人工测试进行比较,表明机载成像识别裂缝宽度满足工程精度要求,以无人飞机为工作平台替代桥检车或支架工作平台,实现结构表面裂缝形状与宽度识别具有可行性和广泛应用前景。     

Autonomous UAVs for Structural Health Monitoring Using Deep Learning and an Ultrasonic Beacon System with Geo‐Tagging

Visual inspection has traditionally been used for structural health monitoring. However, assessments conducted by trained inspectors or using contact sensors on structures for monitoring are costly and inefficient because of the number of inspectors and sensors required. To date, data acquisition using unmanned aerial vehicles (UAVs) equipped with cameras has become popular, but UAVs require skilled pilots or a global positioning system (GPS) for autonomous flight. Unfortunately, GPS cannot be used by a UAV for autonomous flight near some parts of certain structures (e.g., beneath a bridge), but these are the critical locations that should be inspected to monitor and maintain structural health. To address this difficulty, this article proposes an autonomous UAV method using ultrasonic beacons to replace the role of GPS, a deep convolutional neural network (CNN) for damage detection, and a geo‐tagging method for the localization of damage. Concrete cracks, as an example of structural damage, were successfully detected with 97.7% specificity and 91.9% sensitivity, by processing video data collected from an autonomous UAV.     

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.     

Concrete bridge surface damage detection using a single‐stage detector

Early and timely detection of surface damages is important for maintaining the functionality, reliability, and safety of concrete bridges. Recent advancement in convolution neural network has enabled the development of deep learning‐based visual inspection techniques for detecting multiple structural damages. However, most deep learning‐based techniques are built on two‐stage, proposal‐driven detectors using less complex image data, which could be restricted for practical applications and possible integration within intelligent autonomous inspection systems. In this study, a faster, simpler single‐stage detector is proposed based on a real‐time object detection technique, You Only Look Once (YOLOv3), for detecting multiple concrete bridge damages. A field inspection images dataset labeled with four types of concrete damages (crack, pop‐out, spalling, and exposed rebar) is used for training and testing of YOLOv3. To enhance the detection accuracy, the original YOLOv3 is further improved by introducing a novel transfer learning method with fully pretrained weights from a geometrically similar dataset. Batch renormalization and focal loss are also incorporated to increase the accuracy. Testing results show that the improved YOLOv3 has a detection accuracy of up to 80% and 47% at the Intersection‐over‐Union (IoU) metrics of 0.5 and 0.75, respectively. It outperforms the original YOLOv3 and the two‐stage detector Faster Region‐based Convolutional Neural Network (Faster R‐CNN) with ResNet‐101, especially for the IoU metric of 0.75.     

Computer vision‐based recognition of 3D relationship between construction entities for monitoring struck‐by accidents

Struck‐by accidents often cause serious injuries in construction. Monitoring of the struck‐by hazards in terms of spatial relationship between a worker and a heavy vehicle is crucial to prevent such accidents. The computer vision‐based technique has been put forward for monitoring the struck‐by hazards but there exists shortages such as spatial relationship distortion due to two‐dimensional (2D) image pixels‐based estimation and self‐occlusion of heavy vehicles. This study is aimed to address these problems, including the detection of workers and heavy vehicles, three‐dimensional (3D) bounding box reconstruction for the detected objects, depth and range estimation in the monocular 2D vision, and 3D spatial relationship recognition. A series of experiments were conducted to evaluate the proposed method. The proposed method is expected to estimate 3D spatial relationship between construction worker and heavy vehicle in a real‐time and view‐invariant manner, thus enhancing struck‐by hazards monitoring at the construction site.     

Zernike‐moment measurement of thin‐crack width in images enabled by dual‐scale deep learning

Although crack inspection is a routine practice in civil infrastructure management (especially for highway bridge structures), it is time‐consuming and safety‐concerning to trained engineers and costly to the stakeholders. To automate this in the near future, the algorithmic challenge at the onset is to detect and localize cracks in imagery data with complex scenes. The rise of deep learning (DL) sheds light on overcoming this challenge through learning from imagery big data. However, how to exploit DL techniques is yet to be fully explored. One primary component of practical crack inspection is that it is not merely detection via visual recognition. To evaluate the potential risk of structural failure, it entails quantitative characterization, which usually includes crack width measurement. To further facilitate the automation of machine‐vision‐based concrete crack inspection, this article proposes a DL‐enabled quantitative crack width measurement method. In the detection and mapping phase, dual‐scale convolutional neural networks are designed to detect cracks in complex scene images with validated high accuracy. Subsequently, a novel crack width estimation method based on the use of Zernike moment operator is further developed for thin cracks. The experimental results based on a laboratory loading test agree well with the direct measurements, which substantiates the effectiveness of the proposed method for quantitative crack detection.     

High-resolution model reconstruction and bridge damage detection based on data fusion of unmanned aerial vehicles light detection and ranging data imagery

Damage detection is essential for the maintenance of transportation infrastructure that experiences high daily traffic levels in potentially extreme environments and changes in use patterns. However, traditional physical inspection is always labor-intensive, subjective, and biased, lacking the objective perspective required for a comprehensive and reliable assessment. Recently, unmanned aerial vehicles (UAVs) combined with emerging high-performance sensor(s) have been intensively researched. Here, we present an aerial bridge surface survey method that can be used to assess damage. Existing damage detection methods focus on single types of damage and are limited in locating global damage, whereas our method detects two types of damage on the surface and marks them in a panoramic image. The workflow involves three steps: data acquisition using a meticulous UAV flight strategy that covers the entire surface, data processing using image-based and point-cloud models after polynomial rectification, and data output (i.e., damage detected by the combined models). To verify the method, a field test detected damage to two real bridges. A UAV equipped with a camera and light detection and ranging (LiDAR) equipment was employed. Experiments demonstrate the effectiveness of the proposed method, which is capable of producing accurate outputs and detecting damage with an average position error of 13.37 mm and a relative size error of 25.25%. Owing to the data fusion model taking advantage of two-dimensional (2D) images and 3D LiDAR data, it outputs a high-resolution 3D model and avoids environmental disturbances. After decision-making-level damage fusion, all position and size properties of damage information are computed into a panoramic damage image. This panoramic image showing all detecting damage helps technicians perform maintenance; the image can be zoomed to focus on any issue individually.     

A simple quantitative approach for post earthquake damage assessment of flexure dominant reinforced concrete bridges

Conventional methods for displacement based condition assessment of bridges solely rely on the maximum level of displacements experienced by the piers, and do not take into account the accumulated damage that result from cyclic loading. More advanced approaches take this into account by considering the structural damage as a linear combination of the normalized maximum displacements and hysteretic energy. Computation of the dissipated hysteretic energy requires monitoring of the lateral forces during the seismic events, which are not as practical as monitoring bridge pier deformations. This article reports on the development of a simple damage assessment method that considers the effect of cyclic loading on the state of damage and it is merely based on monitoring the bridge pier deformations. A fiber optic displacement serial array was designed for measuring the crack opening displacement reversals at the plastic hinge areas.     

大跨预应力混凝土连续刚构桥高墩稳定性分析

以野三河大桥高墩为例,分析了高墩欧拉及极值稳定性。计算结果表明,结构的计算模型和几何特征对其稳定性的影响较大,桥墩的坡率及桥墩混凝土的标号也是影响高墩极值稳定性的主要因素。     

基底摇摆隔震桥墩振动台试验与数值模拟研究

为了研究不同构造形式的基底摇摆隔震桥墩在不同水准地震作用下的响应规律和抗震性能，提出了设置高阻尼橡胶垫块和线性弹簧的两种基底摇摆隔震桥墩模型。通过振动台试验和数值模拟，对两种摇摆隔震桥墩的地震响应规律和抗震性能进行了对比研究。结果表明：两种基底摇摆隔震桥墩在不同水准地震作用后，桥墩均没有出现严重的破坏，其破坏特征主要表现为限位钢板的弯曲变形和提离约束部件的移位，桥墩呈现出良好的抗震性能；两种基底摇摆隔震桥墩相对于传统桥墩能够显著降低墩顶加速度和墩底应变响应，但墩顶水平位移响应也会相应增加，在摇摆隔震桥墩的设计上，应将墩顶水平位移作为主要设计参数；提出了两种基底摇摆隔震桥墩改进的Winkler两弹簧数值计算模型，通过与振动台试验结果对比，证明了该模型能够较好地模拟两种基底摇摆隔震桥墩的摇摆行为及其地震响应规律。     

钢筋混凝土空心矩形桥墩振动台试验

通过地震模拟振动台试验研究了钢筋混凝土空心矩形桥墩的抗震性能,以某空心矩形桥墩为原型设计了2个具有不同配箍率的空心矩形桥墩模型试件,并对其进行地震模拟振动台试验,选取El Centro波、Taft波和人工兰州波作为地震动激励,分析了各工况荷载下桥墩顶部的加速度响应、动力放大系数和墩顶位移响应等参数;利用大型通用有限元软件ABAQUS建立了桥墩有限元模型,将试验采集的加速度和位移响应同有限元计算结果进行对比分析。结果表明:2个模型试件均具有良好的抗震性能;不同地震波激励作用下,桥墩顶部加速度和位移响应不同;配箍率越高,墩身裂缝分布越密集,裂缝宽度越小;配箍率对加速度和速度响应无显著影响;验证了利用ABAQUS软件建立的有限元模型的可行性。     

An Unmanned Aerial Vehicle‐Based Imaging System for 3D Measurement of Unpaved Road Surface Distresses1

Abstract: Road condition data are important in transportation management systems. Over the last decades, significant progress has been made and new approaches have been proposed for efficient collection of pavement condition data. However, the assessment of unpaved road conditions has been rarely addressed in transportation research. Unpaved roads constitute approximately 40% of the U.S. road network, and are the lifeline in rural areas. Thus, it is important for timely identification and rectification of deformation on such roads. This article introduces an innovative Unmanned Aerial Vehicle (UAV)‐based digital imaging system focusing on efficient collection of surface condition data over rural roads. In contrast to other approaches, aerial assessment is proposed by exploring aerial imagery acquired from an unpiloted platform to derive a three‐dimensional (3D) surface model over a road distress area for distress measurement. The system consists of a low‐cost model helicopter equipped with a digital camera, a Global Positioning System (GPS) receiver and an Inertial Navigation System (INS), and a geomagnetic sensor. A set of image processing algorithms has been developed for precise orientation of the acquired images, and generation of 3D road surface models and orthoimages, which allows for accurate measurement of the size and the dimension of the road surface distresses. The developed system has been tested over several test sites with roads of various surface distresses. The experiments show that the system is capable for providing 3D information of surface distresses for road condition assessment. Experiment results demonstrate that the system is very promising and provides high accuracy and reliable results. Evaluation of the system using 2D and 3D models with known dimensions shows that subcentimeter measurement accuracy is readily achieved. The comparison of the derived 3D information with the onsite manual measurements of the road distresses reveals differences of 0.50 cm, demonstrating the potential of the presented system for future practice.     

An investigation of water-flow pressure distribution on bridge piers under flood loading

This study simulates the flood effect on piers using a finite volume method in the ANSYS-FLUENT package. The pier is modelled as a non-structural column with a rectangular and a circular cross-section. To simplify the methodology, the pier, as well as the bed and side walls, is assumed to have non-slip boundaries for the fluid domain. A crucial feature of this investigation is the consideration of the effect of water while it is flowing around an object like a bridge pier and the distribution of pressure along the pier height. A numerical model is proposed to explore the influence of variation of velocity on the hydrodynamic force and pressure distribution exerted on piers. A significant finding is that the shape of the pier cross-section has a significant effect on the fluid pressure exerted on bridge piers under flood loading. It is noted that the AS5100 method is appropriate for a conservative estimation of the pressure on rectangular piers, whereas the technique will have a risky safety margin for bridge piers with a circular cross-section and need to be used with caution.     

基于几何成像原理和图传系统的UAV桥检技术

基于影像几何原理,推导了无人机(UAV)进行桥梁检测中物高、物距、焦距和像高之间的关系。提出了适用于桥梁检测的无人机图像传输系统。通过对照试验分别探究相机的测量精度和测量距离之间的关系,测量精度和测量角度之间的关系,以及测量精度和本身裂缝大小之间的关系。经测试,文中提出的无人机图像系统能够满足桥梁裂缝测量的要求。