Condition Assessment of Bridges Using Terrestrial Laser Scanners

Abstract

Assessing the current condition of structures and infrastructure systems has long been critical to ensure their effectiveness and remaining life. Even though nondestructive evaluation technologies have improved significantly, visual inspection is still a main tool used to assess the condition of structures, especially bridges. This research investigates the use of laser scanners coupled with images as a tool for improving the current visual inspection strategies. Laser scanning capabilities have advanced in recent years and have gained more recognition as a tool for applications in numerous fields. It is now possible to collect millions of texture-mapped data points that are accurate to within millimeters. In this work, captured texture-mapped datasets are processed using several damage detection strategies, which integrate existing condition rating criteria for a wide range of damage types, in order to locate, quantify and document the surface damage. These damage detection strategies include methods developed for detecting both element and surface damage that are present on the investigated structures. In order to show that defect localization, quantification, and documentation are performed successfully, the proposed methods are used to process the texture-mapped 3D point cloud collected from test-bed bridges. The investigated test-bed bridges include a range of element and surface damage that includes cracks, spalled concrete regions, steel section loss, delamination, and corrosion. The obtained results show that texturemapped 3D point clouds may be used effectively to detect and document quantitative information on present conditions of bridges. Structures Congress 2015 355

A Decade of Modern Bridge Monitoring Using Terrestrial Laser Scanning: Review and Future Directions

“…Particularly, for small cracks, the problems such as noisy data or mixed pixels may result in inaccurate information [90]. The RGB color model is effectively utilized as an additional attribute for crack detection to solve these problems [91], but it requires high accuracy of mapping RGB colors from images to the point cloud. The dependency of data quality and accuracy on the scan position and scan range are the other drawbacks to be considered while using TLS for crack detection [47].…”

An Automatic Concrete Crack-Detection Method Fusing Point Clouds and Images Based on Improved Otsu’s Algorithm

“…There are two main methods for extracting cracks from 3D point clouds. (1) projecting dense 3D point clouds into 2D images and extracting cracks using image-processing techniques [ 21 , 22 ]; and (2) extracting cracks directly from the 3D point clouds [ 23 , 24 ]. Based on the first method, Yang et al [ 25 ] used the reflectance information from point clouds to detect cracks in a tunnel structure through morphology and a Canny edge detection operator, but there were many fractures in the detected cracks.…”

Mathematical Modeling and Computer-Aided Simulation of the Acoustic Response for Cracked Steel Specimens

“…Therefore, improvement in existing methods and/or development of a new technique for a specified purpose has attracted several research groups. Recently, advanced signal processing techniques, e.g., blind feature extraction, sparse representation classification, artificial intelligence algorithms, and compressive sensing algorithms, have been successfully applied in structural health monitoring and damage detection, especially for rotating bodies such as bearings [30] and huge structures such as bridges [31]. Guided wave propagation-based NDT diagnostics are popular because they enable inspection of large areas in a single measurement, but the main limitation is the requirement of sensor installation at the target [32].…”