Change detection and deformation monitoring of concrete structures using terrestrial laser scanning

Abstract

Concrete structures are routinely monitored to detect change and deformation in the field of engineering surveying and other overlapping disciplines such as civil and structural engineering. The monitoring of civil infrastructure is crucial to the safe operation and the longevity of the system. There is growing demand for the development of reliable non-destructive testing techniques for concrete structures in the assessment of the deteriorating condition of infrastructures or in an event of fire-damaged structures. This research investigated the use of terrestrial laser scanning (TLS) for structural health monitoring and the implemented workflow is designed for non full-waveform laser scanner data. Although the use of TLS is not new within the domain of structural health monitoring, the novelty of this research lies in the application of the technology in the specific area of assessing fire-damaged concrete on one hand and the assessment of robust point cloud processing algorithms for precise structural deformation analysis on the other hand. Laser intensity information has become an important object of study in recent years and several studies have shown the potential use of laser intensity data for a great variety of applications such as geomorphology, forestry and glaciology (Holfe and Pfeifer, 2007; Antilla et. al., 2011; Kaasalainen et al., 2011a). Laser intensity information can be used to aid segmentation and classification algorithms alongside geometrical information (Krooks et al., 2013). This evidence for detecting and classifying different materials using the laser intensity values necessitated an investigation into the idea of using the TLS intensity for post fire assessment of concrete. The use of TLS intensity to detect and assess fire-damaged concrete is a new area of research. In terms of the application of TLS for structural change detection and deformation monitoring, TLS is able to provide continuous spatial resolution and reliable 3D information with high redundancy. However, a recent review of studies that have applied TLS for change detection and deformation monitoring of structures has shown that the exploitation of the high data redundancy acquired by TLS is key to achieving good deformation measurement performance with TLS data and that this calls for the development and testing of robust tools. This being the case, several issues are still open to investigation such as rigorous methods of point cloud processing for change detection and deformation analysis. In view of this, the study also aimed at investigating and assessing algorithms for deformation analysis. This thesis presents the work undertaken during the entire period of the research project. The objectives of this research were twofold i.e. detecting and assessing fire-damaged concrete and well as structural deformation monitoring using laser scanning technique. In particular, the technique employed in detecting fire-damaged concrete involved modelling and analysing the laser intensity return. In the case of structural deformation monitoring, the study investigated robust techniques of processing laser scanner data for deformation analysis. This involved assessing the capability of using the multiscale model to model cloud comparison (M3C2) and the iterative similarity registration (ISR) algorithms for processing laser scanner data for deformation analysis. The achieved positive results relating intensity to exposure temperature of concrete demonstrate that laser scanning can be applied to detect and assess fire-damaged concrete and provide an understanding of the condition of concrete in relation to the strength changes of concrete when it is heated to elevated temperatures. In terms of structural monitoring, the study has ascertained that the M3C2 and the ISR algorithms are capable of resolving small scale displacements in the millimetre range which are needed in structural monitoring, due to their robustness.

Date of Award	11 Nov 2017
Original language	English
Awarding Institution	Univerisity of Nottingham
Supervisor	Gethin Roberts (Supervisor), Craig Hancock (Supervisor) & Khalil Al-Manasir (Supervisor)