基于无人机与数字图像法的混凝土结构表面裂缝检测应用研究

对于经常受到振动、疲劳、地基沉降、冻融循环等因素影响的高耸结构、桥梁高墩、高层建筑等,结构裂缝等混凝土表面缺陷非常常见并需要长期观测。通过无人机对结构表面拍照并应用图像算法识别裂缝等缺陷特征可以很好的解决人工检测困难的问题。本文以标准裂缝宽度卡作为参照,使用相机从各方位对模拟裂缝进行拍照,模拟实际工程中无人机拍摄的状态。实现对裂缝的长度、宽度、方向等几何参数进行测量,并应用数字图像法对带裂缝混凝土结构图像进行识别,证明无人机拍摄图像分析相较传统人工检测的优越性。     

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。     

土木结构物裂缝识别检测的自动化研究

土木结构裂缝是结构损伤发生的先兆,结构裂缝从其孕育阶段裸眼难辨的微缝开始,逐渐发展,引起构件损伤、结构变形、体系失稳,最后发展到工程坍塌。因此为了结构物的工程安全,要求对裂缝进行行之有效的监测和分析。而传统的检测裂缝的方法费时,费力,经济性差。本研究利用近年正在高速发展的数字图像技术,对土木结构物裂缝识别检测进行自动化研究。即使用数码相机对土木结构物进行摄影观测,并开发相应的程序对所获取的检测数字图像进行处理。研究结果表明,该方法能自动识别结构物裂缝,并能很好地解决传统方法上的不足,且检测结果准确,操作方便,可应用于实际工程。     

优化分形维数计算方法

应用分形几何研究钢筋混凝土结构裂缝扩展形式。研究表明,结构表面裂缝的分形维数与结构力学性能之间存在良好的线性相关性;为了准确测定分形维数,结合工程实例,对比分析了盒计数网格法和数字图像盒维数法测得的分维值,并讨论了各自的优缺点及各自测定的条件,结论对分维值的准确测定能起到一定的指导作用。     

基于数字图像相关法的桥梁模型自振频率测试

本文利用数字图像相关法测试了两座模型桥梁的位移时程曲线,同时根据测试的位移时程曲线计算了结构的频域曲线。在环境激励和人工激励下,利用传统接触式速度传感器对测试结果进行了对比分析。环境激励下,基于数字图像技术的位移时程曲线测试精确度不足,无法识别结构的自振频率。人工激励下,基于数字图像技术的位移时程曲线测试准确,识别的结构自振频率与传统接触式速度传感器测试结果一致。基于数字图像相关法的结构自振频率识别,在较大幅值振动测试中优势明显,实际柔性结构的适用性测试还需要进一步研究。     

基于改进Canny算法的墙面裂缝自动识别及量测

墙体结构的表面裂缝对建筑结构的健康和美观都有一定程度的影响,因此墙面裂缝的监测和检测工作较为重要。基于数字图像的检测方法人工参与度低,自动化程度高,能快速识别裂缝。本文对比了传统微分算子识别图像裂缝的效果,选取Canny算法为基本算法,在算法中引入导向滤波和同态滤波进行改进,达到消除噪声、保留和增强边缘细节信息的目的。在识别裂缝的基础上,通过边界像素的计算测量裂缝的像素长度和宽度,通过像素解析度求得实际长度。通过实验对比手工测量值和图像测量值,在普通相机标定情况下,图像测量的裂缝几何特征值精度较好,与手工测量值的误差在3%左右。因此该方法能够为后续裂缝的常态监测和检测的智能化提供一种思路。     

试论PC箱梁桥裂缝及修补措施

介绍了PC箱梁桥常见的裂缝形态,针对裂缝产生的原因,从表面封闭法、压力注浆法、充填法等方面,阐述了PC箱梁桥裂缝的处理措施,从而保障桥梁施工的质量及桥梁结构的安全性。     

某重力式桥台裂缝检测及成因分析

1前言 建筑物的破坏经常从发生裂缝开始,从而降低了结构的安全性,应当严格防止这类裂缝的出现,一旦出现也必须花费巨资进行加固补强.然而,也有许多裂缝并不象前述的那样严重,这类裂缝对结构安全没有影响,甚至对耐久性的影响也可以忽略,对这类裂缝只需要进行表面处理或不做处理.本文作者对某重力式桥台所出现裂缝进行了现场测试,对裂缝成因进行了定性和定量的分析,并提出了防止大体积混凝土裂缝的措施.     

浅谈混凝土裂缝的产生及预防

本文作者结合多年从事现场工作经验,主要从结构混凝土裂缝影响结构安全性、耐久性方面谈起,对裂缝的现象、产生的原因、预防的措施以及维护、修复的处理方法等进行系列的分析讨论.并有针对性的提出应采取的相对应预防措施,以及出现裂缝时的几种处理方法.     

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。     

基于数字图像处理的沥青路面破损检测技术

本文针对路面破损的早期形式裂缝进行分析,探讨了基于数字图像处理的裂缝目标检测技术及方法。该方法主要包括以下几个步骤：处理路面裂缝图像噪音;增强图像特征;检测裂缝边缘并进行图像分割。本文采用不同的算法实现上述过程,并对结果进行比较分析。     

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.     

台北捷運系統隧道檢測作業簡介

介绍目前台北捷運系統採用之DER結構檢查評估方法之應用情形,並針對結構安全評估與隧道影像掃描資料建立之作法加以說明。DER檢查方法目前已廣泛應用於橋梁結構檢查,台北捷運公司目前將其擴大應用至隧道結構例行檢查工作上,包括平時及定期之目視結構檢查作業,並建立專用之結構管理资訊系統,將結構之各項基本资料、執行檢测結果及維修紀錄等資訊加以管理應用。另外,針對隧道結構安全性之評估作業,則透過專業之工程顧問公司協助進行,針對隧道結構潛在性及隱藏性之瑕疵加以檢測評估,同時利用數位影像掃描技術,將隧道内之完整影像掃描儲存為數位檔案,作為影像紀錄及未來追蹤比對之依據。     

Image analysis method for crack distribution and width estimation for reinforced concrete structures

Crack observation is important for evaluating the structural performance and safety of reinforced concrete (RC) structures. Most of the existing image-based crack detection methods are based on edge detection algorithms, which detect cracks that are wide enough to present dark areas in the obtained images. Cracks initiate as thin cracks, generally having width less than the width of a pixel in images; such cracks are generally undetectable by edge detection-based methods.An image analysis method is proposed to observe the development and distribution of thin cracks on RC surfaces; it also allows estimation of crack widths. Image matching based on optical flow and subpixel is employed to analyze slight concrete surface displacements. Camera calibration is included to eliminate perspective effects and lens distortion. Geometric transformation is adopted so that cameras do not need to be perpendicular to the observed surface or specified positions. Formulas are proposed to estimate the width of shear crack opening. The proposed method was then applied to a cyclic test of an RC structure. The crack widths and their development analyzed by the image analysis were verified with human inspection in the test. In addition, concrete surface cracks that appeared at a very early stage of the test could be observed by the proposed method before they could be detected by the naked eye. The results thus demonstrate that the proposed image analysis method offers an efficient way applicable not only for structural tests but also for crack-based structural-health-monitoring applications.     

工程结构运维多场景的敏感服役指标智能识别

随着我国土木工程行业由建造向运维逐渐转型,工程结构服役安全保障需求陡增,提质增效的结构智能诊断方法成为研究热点。结构服役性态指标是表征工程结构安全水平的要素,是工程结构诊断养护技术体系以及结构健康监测研究的基础,判断结构服役性态的敏感指标并进一步实现指标的智能识别是工程结构诊断智能化的首要任务。为此,围绕工程结构运维公共建筑、地铁隧道、公路桥梁、公路路面等多个场景中的敏感服役指标的智能识别开展综述研究;梳理关键敏感指标,进一步对指标的智能化识别方法进行归纳总结。结果表明,以深度学习为代表的新一代人工智能技术有效推动了结构服役敏感指标的感知识别研究与应用,其中数字图像方法与深度学习算法在工程结构变形、表面病害智能识别中取得了良好的效果,展现了全面的应用优势。     

一种改进的用于裂缝图像分割的Otsu方法

裂缝是隧道衬砌最常见的病害之一,基于近几年快速发展的工程检测系统与图像处理算法的研究,提出了一种CCD相机的衬砌裂缝快速检测系统采集裂缝图像。在提取裂缝特征之前,需要将裂缝区域与图像背景分离。采用Otsu法进行分割处理,然而传统的Otsu方法对裂缝区域过小的图像易产生欠分割;对背景不单一或光照度不均匀的裂缝图像易造成过分割的情况。根据Ostu方法分割特点对该方法进行改进,以达到更好的裂缝图像分割效果,从而为实现隧道裂缝的快速检测埋下基础。     

A survey and evaluation of promising approaches for automatic image-based defect detection of bridge structures

Automatic health monitoring and maintenance of civil infrastructure systems is a challenging area of research. Nondestructive evaluation techniques, such as digital image processing, are innovative approaches for structural health monitoring. Current structure inspection standards require an inspector to travel to the structure site and visually assess the structure conditions. A less time consuming and inexpensive alternative to current monitoring methods is to use a robotic system that could inspect structures more frequently. Among several possible techniques is the use of optical instrumentation (e.g. digital cameras) that relies on image processing. The feasibility of using image processing techniques to detect deterioration in structures has been acknowledged by leading experts in the field. A survey and evaluation of relevant studies that appear promising and practical for this purpose is presented in this study. Several image processing techniques, including enhancement, noise removal, registration, edge detection, line detection, morphological functions, colour analysis, texture detection, wavelet transform, segmentation, clustering and pattern recognition, are key pieces that could be merged to solve this problem. Missing or deformed structural members, cracks and corrosion are main deterioration measures that are found in structures, and they are the main examples of structural deterioration considered here. This paper provides a survey and an evaluation of some of the promising vision-based approaches for automatic detection of missing (deformed) structural members, cracks and corrosion in civil infrastructure systems. Several examples (based on laboratory studies by the authors) are presented in the paper to illustrate the utility, as well as the limitations, of the leading approaches.     

Image‐based crack assessment of bridge piers using unmanned aerial vehicles and three‐dimensional scene reconstruction

Crack assessment of bridge piers using unmanned aerial vehicles (UAVs) eliminates unsafe factors of manual inspection and provides a potential way for the maintenance of transportation infrastructures. However, the implementation of UAV‐based crack assessment for real bridge piers is hindered by several key issues, including the following: (a) both perspective distortion and the geometry distortion by nonflat structural surfaces usually appear on crack images taken by the UAV system from the pier surface; however, these two kinds of distortions are difficult to correct at the same time; and (b) the crack image taken by a close‐range inspection flight UAV system is partially imaged, containing only a small part of the entire surface of the pier, and thereby hinders crack localization. In this paper, a new image‐based crack assessment methodology for bridge piers using UAV and three‐dimensional (3D) scene reconstruction is proposed. First, the data acquisition of UAV‐based crack assessment is discussed, and the UAV flight path and photography strategy for bridge pier assessment are proposed. Second, image‐based crack detection and 3D reconstruction are conducted to obtain crack width feature pair sequences and 3D surface models, respectively. Third, a new method of projecting cracks onto a meshed 3D surface triangular model is proposed, which can correct both the perspective distortion and geometry distortion by nonflat structural surfaces, and realize the crack localization. Field test investigations of crack assessment of a real bridge pier using a UAV are carried out for illustration, validation, and error analysis of the proposed methodology.     

单轴压缩下基于全局应变场分析的岩石裂纹扩展及其损伤演化特性研究

 采用自主开发的图像分析软件结合数字图像相关技术对含预制单裂纹的类岩石材料在单轴压缩下的变形破坏特性进行试验研究。基于试件全局应变场角度从细观层次量化分析、总结裂纹起裂、扩展的规律及岩石变形损伤演化特征。并采用断裂分析软件FRANC2D/L对相似模型进行数值模拟,分析在加载全程不同阶段的裂纹扩展路径及其应力场分布特征。结合试验与数值研究结果,细致地探讨裂隙岩石的细观力学机制与宏观力学响应之间的内在联系,该研究有助于提升人们对节理岩体工程灾变机制的认识。