Bridge inspections are relied heavily on visual inspection, and usually conducted within limited time windows, typically at night, to minimize their impact on traffic. This makes it difficult to inspect every meter of the structure, especially for large-scale bridges with hard-to-access areas, which creates a risk of missing serious defects or even safety hazards. This paper presents a new technique for the semi-automated damage detection in tunnel linings and bridges using a hybrid approach based on photogrammetry and deep learning. The first approach involves using photogrammetry to reconstruct a 3D model. It is shown that a model with sub-centimeter accuracy can be obtained after noise removal. However, noise removal also reduces the point cloud density, making the 3D point cloud unsuitable for quantification of small-scale damages such as fine cracks. Therefore, the captured images are also analyzed using deep convolutional neural network (CNN) models to enable crack detection and segmentation. For this aim, in the second approach, the 3D model is generated by the output of CNN models to enable crack localization and quantification on 3D digital model. These two approaches were evaluated in separate case studies, showing that the proposed technique could be a valuable tool to assist human inspectors in detecting, localizing, and quantifying defects on concrete structures.

<p>This study is carried out to assess the applicability of using a digital image correlation (DIC) system in structural inspection, leading to deploy innovative instruments for strain/stress estimation along embedded rebars. A semi-empirical equation is proposed to predict the strain in embedded rebars as a function of surface strain in RC members. The proposed equation is validated by monitoring the surface strain in ten concrete tensile members, which are instrumented by strain gauges along the internal steel rebar. One advantage with this proposed model is the possibility to predict the local strain along the rebar, unlike previous models that only monitored average strain on the rebar. The results show the feasibility of strain prediction in embedded reinforcement using surface strain obtained by DIC.</p>

For the inspection of structures, particularly bridges, it is becoming common to replace humans with autonomous systems that use unmanned aerial vehicles (UAV). In this paper, a framework for autonomous bridge inspection using a UAV is proposed with a four-step workflow: (a) data acquisition with an efficient UAV flight path, (b) computer vision comprising training, testing and validation of convolutional neural networks (ConvNets), (c) point cloud generation using intelligent hierarchical dense structure from motion (DSfM), and (d) damage quantification. This workflow starts with planning the most efficient flight path that allows for capturing of the minimum number of images required to achieve the maximum accuracy for the desired defect size, then followed by bridge and damage recognition. Three types of autonomous detection are used: masking the background of the images, detecting areas of potential damage, and pixel-wise damage segmentation. Detection of bridge components by masking extraneous parts of the image, such as vegetation, sky, roads or rivers, can improve the 3D reconstruction in the feature detection and matching stages. In addition, detecting damaged areas involves the UAV capturing close-range images of these critical regions, and damage segmentation facilitates damage quantification using 2D images. By application of DSfM, a denser and more accurate point cloud can be generated for these detected areas, and aligned to the overall point cloud to create a digital model of the bridge. Then, this generated point cloud is evaluated in terms of outlier noise, and surface deviation. Finally, damage that has been detected is quantified and verified, based on the point cloud generated using the Terrestrial Laser Scanning (TLS) method. The results indicate this workflow for autonomous bridge inspection has potential.

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<p>As reinforced concrete structures reach the end of their design lives, technology for improving accuracy and efficiency of inspections and structural health monitoring rapidly progresses. Concrete cracking and reinforcement strains are two relevant parameters in assessing damage and safety of these structures. The use of Digital Image Correlation (DIC) systems and distributed Fibre Optic Sensors (FOS) to evaluate these parameters are two of the technologies that have been gaining momentum due to their advantages over other approaches. This study presents an experimental investigation of crack propagation of a reinforced concrete beam specimen through FOS and DIC. The FOS were positioned inside a groove carved in the rebar and in the concrete immediately outside the bar for comparison. The results showed a significant difference between both positions, with more reliable data coming from inside the bar. The addition of the DIC crack propagation images to the FOS analysis complemented the results, and good visual correlation was identified between both methods. This study is part of a broader research program, which aims at applying DIC and FOS for structural health monitoring of a real scale bridge structure.</p>