Application of Probabilistic Damage Identification to Civil Engineering Structures

Abstract

The rapid development in statistics, information technology, and computational power have en- abled numerous innovative methods to emerge in Structural Health Monitoring (SHM) for structural damage detection, both in practical application and research. The intention of such methods is to use the data obtained from the monitoring system to extract sufficient information to identify damage types that may appear in the structure. However, the following type of questions are mostly unanswered for realistic structural types and monitoring systems: Which responses of the structure should be monitored? Which sensor locations and sensor combinations carry the most information? Among the currently available computational algorithms, which one is the most suit- able one in this context? Hence, this study aims to contribute on this front and provide answers for practical applicability. A comprehensive study of a realistic, prestressed concrete bridge built by the cantilever method - the Lezíria Bridge in Portugal is undertaken to provide insight into these questions.
The engineering challenge is studied in a probabilistic framework where the uncertainty sources are model uncertainty and measurement uncertainty. The Bayesian paradigm is used to handle the uncertainty component and a validated Finite Element (FE) model is applied to capture the mechanical behavior. The influence of the potential damage scenario, severity of damage, sensor type, the combination of sensors, prior knowledge of the structure, and the extent of uncertainty of the FE model and measurements are analyzed in this thesis. The informativeness of the Damage Identification (DI) process is reflected by the information content of its resulting posterior distributions. The information content is quantified using measures based on information entropy and the area of credible regions.
It is demonstrated that selecting different responses to monitor may lead to a significant change in the informativeness of the result. It is also shown that for all analyzed cases using the most informative sensor type provides adequate information that reflects the damage state, while the rest types could only complement very limited information. In addition, the hybrid Markov Chain Monte Carlo(MCMC) seems more efficient and effective to conduct Bayesian inference. The findings provide valuable, quantitative insight into the design of new monitoring systems.