Modular Bayesian damage detection for complex civil infrastructure

Abstract

We address the problem of damage identification in complex civil infrastructure with an integrative modular Bayesian framework. The proposed approach uses multiple response Gaussian processes to build an informative yet computationally affordable probabilistic model, which detects damage through inverse updating. Performance of structural components associated with parameters of the developed model was quantified with a damage metric. Particular emphasis is given to environmental and operational effects, parametric uncertainty and model discrepancy. Additional difficulties due to usage of costly physics-based models and noisy observations are also taken into account. The framework has been used to identify a reduction of a simulated cantilever beam elastic modulus, and anomalous features in main/stay cables and bearings of the Tamar bridge. In the latter case study, displacements, natural frequencies, temperature and traffic monitored throughout one year were used to form a reference baseline, which was compared against a current state, based on one month worth of data. Results suggest that the proposed approach can identify damage with a small error margin, even under the presence of model discrepancy. However, if parameters are sensitive to environmental/operational effects, as observed for the Tamar bridge stay cables, false alarms might occur. Validation with monitored data is also highlighted and supports the feasibility of the proposed approach.

Implementing bridge model updating for operation and maintenance purposes: examination based on UK practitioners’ views

“…cable slack) of cable-stayed bridges (Degrauwe, De Roeck, & Lombaert, 2009) • Crack pattern of masonry arch bridges (Conde et al, 2018) The majority of the surveyed studies, especially early ones, used modal frequencies and mode shapes to perform model updating and damage detection, which were generally not sensitive to local damage. Recently, there have been attempts of using potentially more damage sensitive features or measurements such as damping (Mustafa, Matsumoto, & Yamaguchi, 2018), mid-span displacement and strain (Jesus et al, 2019).…”

Deep learning for bridge load capacity estimation in post-disaster and -conflict zones

“…In general, a number of advanced computing methods have been developed over the last decades for monitoring and maintenance of civil infrastructure, including conventional vibration-based methods [22,23] and blind signal separation methods [24,25] as well as Bayesian approaches [26,27]. Recent research efforts have been made in automatic defect detection by using CNN in image processing.…”

A decentralized unsupervised structural condition diagnosis approach using deep auto‐encoders

“…With the aging of existing structures, the problem of performance degradation has become increasingly prominent, especially for long-span bridges. To ensure structural integrity, structural health monitoring (SHM) has become increasingly important and has received extensive attention in recent years (An, Spencer, & Ou, 2015;Blachowski, An, Spencer, & Ou, 2017;Jesus et al, 2019;Li, Park, & Adeli, 2017;Oh, Kim, Kim, Park, & Adeli, 2017;Sun, Liang, Li, & Li, 2019). However, the complexity and uniqueness of civil © 2021 Computer-Aided Civil and Infrastructure Engineering engineering infrastructure, the limitations of sensing technology, and the variability of operational and environmental conditions make SHM and condition assessment still a challenging task.…”