A Novel Bridge Information Modeling (BrIM) Based Framework for Bridge Inspections

Bridges are critical components of civil infrastructure. According to the National Bridge Inventory (NBI), there are more than ten thousand structurally deficient bridges in the United States. It becomes critical for the authorities to maintain their serviceability and reliability in order to keep the transportation system operational. Current bridge condition assessment practices are mainly based on visual inspections carried out by technical experts, which is subjective as observations and opinions may vary from one individual to another, and is expensive and prone to human errors. The main focus of this study is to help improve current inspection practices by implementing image processing algorithms to detect concrete surface cracks and integrate the results into a bridge information modeling (BrIM) based framework. Integrating crack detection algorithm results with BrIM will allow users to view and explore cracks and their properties linked to a three dimensional (3D) model of the inspected bridge component. The proposed methodology processes 2D images by adjusting pixel parameters of gray scale images and detects cracks with their dimensional aspects. It implements existing crack detection algorithms, a scaling tool to automatically measure crack dimensions, and includes a framework to integrate crack detection results with BrIM for inspecting bridges in a more efficient manner. This will enable more effective repairs and maintenance operations, saving a considerable amount of effort, time, and money

Image-based Automated Width Measurement of Surface Cracking

The detection of cracks is an important monitoring task in civil engineering infrastructure devoted to ensuring durability, structural safety, and integrity. It has been traditionally performed by visual inspection, and the measurement of crack width has been manually obtained with a crack-width comparator gauge (CWCG). Unfortunately, this technique is time-consuming, suffers from subjective judgement, and is error-prone due to the difficulty of ensuring a correct spatial measurement as the CWCG may not be correctly positioned in accordance with the crack orientation. Although algorithms for automatic crack detection have been developed, most of them have specifically focused on solving the segmentation problem through Deep Learning techniques failing to address the underlying problem: crack width evaluation, which is critical for the assessment of civil structures. This paper proposes a novel automated method for surface cracking width measurement based on digital image processing techniques. Our proposal consists of three stages: anisotropic smoothing, segmentation, and stabilized central points by k-means adjustment and allows the characterization of both crack width and curvature-related orientation. The method is validated by assessing the surface cracking of fiber-reinforced earthen construction materials. The preliminary results show that the proposal is robust, efficient, and highly accurate at estimating crack width in digital images. The method effectively discards false cracks and detects real ones as small as 0.15 mm width regardless of the lighting conditions

CONCRETE CRACK EVALUATION FOR CIVIL INFRASTRUCTURE USING COMPUTER VISION AND DEEP LEARNING

Department of Urban and Environmental Engineering (Urban Infrastructure Engineering)Surface cracks of civil infrastructure are one of the important indicators for structural durability and integrity. Concrete cracks are typically investigated by manual visual observation on the surface, which is intrinsically subjective because it highly depends on the experience of inspectors. Furthermore, manual visual inspection is time-consuming, expensive, and often unsafe when inaccessible structural components need to be assessed. Computer vision-based approach is recognized as a promising alternative that can automatically extract crack information from images captured by the digital camera. As texts and cracks are similar in terms of consisting distinguishable lines and curves, image binarization developed for text detection can be appropriate for crack identification purposes. However, although image binarization is useful to separate cracks and backgrounds, the crack assessment is difficult to standardize owing to the high dependence of binarization parameters determined by users. Another critical challenge in digital image processing for crack detection is to automatically distinguish cracks from an image containing actual cracks and crack-like noise patterns (e.g., stains, holes, dark shadows, and lumps), which are often seen on the surface of concrete structures. In addition, a tailored camera system and the corresponding strategy are necessary to effectively address the practical issues in terms of the skewed angle and the process of the sequential crack images for efficient measurement. This research develops a computer vision-based approach in conjunction with deep learning for accurate crack evaluation of for civil infrastructure. The main contribution of the proposed approach can be summarized as follows: (1) a deep learning-based approach for crack detection, (2) a hybrid image processing for crack quantification, and (3) camera systems for the practical issues on civil infrastructure in terms of a skewed angle problem and an efficient measurement with the sequential crack images. The proposed research allows accurate crack evaluation to provide a proper maintenance strategy for civil infrastructure in practice.clos

Automatic Surface Crack Detection in Concrete Structures Using OTSU Thresholding and Morphological Operations

Concrete cracking is a ubiquitous phenomenon, present in all types of concrete structures. Identifying and tracking the amount and severity of cracking is paramount to evaluating the current condition and predicting the future service life of a concrete asset. Concrete cracks can indicate reinforcement corrosion, the development of spalls or changing support conditions. Therefore, monitoring cracks during the life span of concrete structures has been an effective technique to evaluate the level of safety and preparing plans for future appropriate rehabilitation. One growing technique are unmanned inspections using Unmanned Aerial Vehicles (UAV). UAVs are drones equipped with cameras, sensors, GPS, etc. RGB images (color images in Red, Green and Blue color space) are obtained from a camera mounted on a UAV flying around the structure, to detect cracks and other defects. Each image captured by UAV needs to be evaluated to track the crack formations. To save time, this task can be done by applying image processing techniques to automatically detect and report cracks rather than using a human to identify them. In addition, processing RGB images with sufficient information, such as the distance of camera to surface for each picture, will provide the dimension of the cracks (length and width). The report consists of the following sections: A literature review of image processing techniques used in structural health monitoring and other fields of interest is provided in chapter 2. The Proposed method to identify cracks is demonstrated in Chapter 3. Experimental results, conclusion and future work are presented in Chapter 4. Appendix A includes the processed images using the proposed method and Appendix B includes the comparison between Talab’s method and the proposed method. In Appendix C, a “readme” file is given to run the program, and finally Appendix D shows the Matlab Code

3D Characterisation of microcracks in concrete

The nature of microcracks that developed in concrete is not well understood. One reason for this is the lack of suitable techniques to detect and characterise the microcracks. Conventional methods include imaging polished cross sections with scanning electron microscopy and optical microscopy. However, these techniques only provide a two-dimensional representation of a three-dimensional structure, which significantly reduces the insights from such analysis. Another reason is that the development of microcracks may be associated with various complex forms of concrete deterioration during service life, e.g. due to mechanical loading, drying, thermal effects and chemical reactions. This complicates laboratory scale experiments and inducing “realistic” microcracks in concrete samples becomes very difficult. The aim of this study is to develop new techniques for three-dimensional quantitative characterisation of microcracks and to apply these to understand the properties of microcracks in concrete. A thorough literature review was conducted to identify the causes of microcracking in concrete, mechanisms of microcrack initiation and propagation, transport properties of micro-cracked concrete and methods to characterise microcracks in two dimensions (2D) and three dimensions (3D). Materials and experimental procedures for inducing different types of microcracks, sample preparation for imaging and image analysis of microcracks are discussed. The feasibility of three-dimensional techniques such as focused ion beam nanotomography (FIB-nt), broad ion beam combines with serial sectioning (BIB), X-ray microtomography (μ-CT) and laser scanning confocal microscopy (LSCM) for imaging microcracks were investigated. A new approach that combines LSCM with serial sectioning was proposed to enhance the capability of LSCM for imaging microcracks in 3D. A major focus of this thesis was dedicated to microcracks induced by autogenous shrinkage because this has been previously neglected due to the dominant role of drying shrinkage. Nonetheless, the increasing use of high strength concretes containing low water/binder ratio, complex binder systems and multiple chemical admixtures in recent years has highlighted the problem of autogenous shrinkage in these concretes. This study presents a first attempt on direct characterisation and understanding of the microcracks caused by autogenous shrinkage in 3D. Various concrete samples were produced and sealed cured to induce autogenous shrinkage. The water/binder ratio, cement type and content, and aggregate particle size distribution were varied to vary the magnitude of autogenous shrinkage and degree of microcracking. Linear deformation measurement was performed to correlate autogenous shrinkage with degree of microcracking. Samples were imaged in 2D using laser scanning confocal microscope (LSCM) and in 3D with X-ray microtomography (μ-CT). Subsequently, 2D and 3D image analysis was employed to quantify microcracks > 1 μm in width. A major challenge was to isolate the microcracks that are inherently connected to pores and air voids. Therefore, an algorithm was developed to separate microcracks from pores, and to extract quantitative data such as crack density, orientation degree, distribution of width and length, as well as connectivity and tortuosity. The results show that use of supplementary cementitious materials and low water/binder ratio can increase linear deformation and the amount of the microcracks. The thesis discusses the effect of autogenous shrinkage on the characteristics of the induced microcracking, which is critical to understanding the transport properties and long-term durability of concretes containing supplementary cementitious materials.Open Acces

Nondestructive Testing Methods and New Applications

Nondestructive testing enables scientists and engineers to evaluate the integrity of their structures and the properties of their materials or components non-intrusively, and in some instances in real-time fashion. Applying the Nondestructive techniques and modalities offers valuable savings and guarantees the quality of engineered systems and products. This technology can be employed through different modalities that include contact methods such as ultrasonic, eddy current, magnetic particles, and liquid penetrant, in addition to contact-less methods such as in thermography, radiography, and shearography. This book seeks to introduce some of the Nondestructive testing methods from its theoretical fundamentals to its specific applications. Additionally, the text contains several novel implementations of such techniques in different fields, including the assessment of civil structures (concrete) to its application in medicine

Automated classification of civil structures defects based on Convolutional Neural Network

Today, the most used method for civil infrastructure inspection is based on visual assessment performed by certified inspectors following prescribed protocols. However, the increase in aggressive environmental and load conditions, coupled with the achievement for many structures of the end life-cycle, highlighted the need to automate damage identification to satisfy the number of structures that need to be inspected. To overcome this challenge, the current paper presents a method to automate the concrete damage classification using a deep Convolutional Neural Network (CNN). The CNN is designed after an experimental investigation among a wide number of pretrained networks, all applying the transfer learning technique. Training and Validation are performed using a built database with 1352 images balanced between “undamaged”, “cracked”, and “delaminated” concrete surface. To increase the network robustness compared to images with real-world situations, different configurations of images has been collected from Internet and on-field bridge inspections. The GoogLeNet model is selected as the most suitable network for the concrete damage classification, having the highest validation accuracy of about 94%. The results confirm that the proposed model can correctly classify images from real concrete surface of bridges, tunnel and pavement, resulting an effective alternative to the current visual inspection

Identification of Top-down, Bottom-up, and Cement-Treated Reflective Cracks Using Convolutional Neural Network and Artificial Neural Network

The objective of this study was to formulate a Convolutional Neural Networks (CNN) model and to develop a decision-making tool using Artificial Neural Networks (ANN) to identify top-down, bottom-up, and cement treated (CT) reflective cracking in in-service flexible pavements. The CNN’s architecture consisted of five convolutional layers with three max-pooling layers and three fully connected layers. Input variables for the ANN model were pavement age, asphalt concrete (AC) thickness, annual average daily traffic (AADT), type of base, crack orientation, and crack location. The ANN network architecture consisted of an input layer of six neurons, a hidden layer of ten neurons, and a target layer of three neurons. The developed CNN model was found to achieve an accuracy of 93.8% and 91.0% in the testing and validation phases, respectively. The ANN based decision-making tool achieved an overall accuracy of 92% indicating its effectiveness in crack identification and classification. In the second phase of the study, the flexible pavement responses under a dual tire assembly were analyzed to identify the critical stress mechanisms for bottom-up and top-down cracking. Higher tensile strains were observed to occur underneath the tire ribs than away from them supporting the argument that both surface initiated and bottom-up fatigue cracking develop in or near the wheel paths. The incorporation of surface transverse tangential stresses increased the surface tensile strains near the tire ribs by approximately 68%, 63%, and 53% respectively for low, medium, and high volume flexible pavements indicating an increased potential for the initiation and development of top-down cracking when tangential stresses are considered. In contrast, this effect was observed to be minimal for the tensile strains at the bottom of the asphalt layer, which are the main pavement responses used in the prediction of fatigue cracking. Shrinkage cracking in cement treated base (CTB) was also modeled in finite element using displacement boundary conditions. The tensile stresses due to shrinkage strains in the cement treated base were observed to be comparable to the tensile strength of CTB at 7 days and higher at 56 days indicating the potential development of shrinkage cracks