Damage Detection on Structures Using Texture Mapped Laser Point Clouds

Abstract

Damage due to age or accumulated damage from hazards on existing structures poses a worldwide problem. In order to evaluate the current status of aging, deteriorating and damaged structures, it is vital to accurately assess the present conditions. It is possible to capture the in situ condition of structures by using laser scanners that create dense 3D point clouds. However, extracting meaningful information from point clouds automatically is challenging, and the current state-of-the-art requires a certain amount of user-interaction. Previously in our research, widely accepted point cloud processing steps were used to divide laser scanner data into meaningful object clusters, on which several damage detection methods were applied. Our current focus is on combining useful information extracted from laser scanner data with color information since novel laser scanners are capable of recording color data mapped on 3D point clouds. The color data provides additional information in the 4 th dimension, which makes it possible to detect damage types such as cracks, corrosion, and related surface defects that are generally difficult to detect by using only laser scanner data, and also helps to track volumetric changes on structures such as spalling. Even though crack and corrosion detection from images with varying resolution are extensively researched topics, damage detection by using laser scanners with and without images is a new research area that holds many opportunities for enhancing current practice in structural health monitoring. The aim of our research is to benefit from the best features of both laser scans and images, and combine them to create an automatic and effective damage detection method, which will complement the need for skilled labor during visual inspections of damaged structures and allow documenting related information automatically. This work is of interest to researchers who are focused on structural health monitoring and interested in the use of new laser scanners and damage detection algorithms for structures, as well as practitioners, who will benefit exploring new methods for improving their understanding of laser scanning technology and its capabilities for robust damage detection and documentation.

A Review of LIDAR Radiometric Processing: From Ad Hoc Intensity Correction to Rigorous Radiometric Calibration

“…Armesto-González et al [ 41 ] employed LIDAR intensity data to detect degraded stony materials in scans of historic buildings. Several researchers used LIDAR intensity to detect cracks in concrete structural components in their laboratory tests or post-disaster field investigations [ 42 , 43 , 44 ]. Kashani et al [ 46 , 47 , 48 ] indicated that the LIDAR intensity data is an appropriate means to automatically detect cladding damage of buildings after wind storm events.…”

Investigating Surface Fractures and Materials Behavior of Cultural Heritage Buildings Based on the Attribute Information of Point Clouds Stored in the TLS Dataset

“…However, only a few studies have been found to specifically identify the structural deformation and materials behavior of the building based on attribute point cloud information. For example, Armesto-González et al [38], Guldur and Hajjar [39], and Olsen et al [40] similarly examined surface degradation of buildings, mainly based on the intensity information. Several works have also been identified to utilize RGB color information for crack detection of the building surface.…”

An application of finite element model updating for damage assessment of a two-story reinforced concrete building and comparison with lidar

“…However, existing damage complicated the classification procedure which was dependent on identifying geometric features such as the corner edge of a beam. To detect damage within structural members from point clouds, Guldur and Hajjar 22 employed the calculation of vertex normals and compared it to a reference vector. The reference vector was constructed based on the analyzing member direction, which demanded a preprocessing step to identify the skeleton geometry.…”