Mubarak Faisal Abu Zouriq

A key component to determining bridge conditions and functionality is implementing structural health monitoring (SHM) systems that analyze structural behavior under variable loading conditi...

While supervised structural health monitoring (SHM) systems can potentially accurately detect and localize damage in bridge structures, they require labeled datasets containing healthy and damaged states to develop decision-making models. Obtaining supervised data from in situ bridges is often impractical. Unsupervised algorithms address this challenge by leveraging baseline (i.e., “healthy”) data, thereby eliminating the need for damaged datasets to detect anomalies. Conventional SHM techniques rely on sensor data to assess the structural condition; however, accurately detecting damage remains a challenge, especially when dealing with differing structure types and sizes and varying loading conditions. This study introduces an unsupervised damage detection methodology that aims to classify bridge health using structural response data under variable loading by leveraging deep learning. A variational autoencoder (VAE) network was employed to develop a data-driven methodology to identify structural anomalies based on response data collected during tests of an in situ bridge. Collected datasets included strain time histories representing three structural states (i.e., “healthy,” two levels of damage). The model was trained using strain data collected from healthy bridge loading tests and validated on separate healthy trials, while testing included both healthy and damaged scenarios to evaluate the method’s generalization and damage sensitivity. A VAE network was trained to learn underlying patterns of healthy behavior under moving live load. The training helped develop a compressed strain time-history representation using a network encoder and accurately reconstructed that input from a lower-dimension space. Normalized data reconstruction errors were used to define a damage index that permitted quantitative assessment of structural deterioration. In addition, clustering each test dataset corresponding as a function of structural state helped quantify response deviation from the healthy state. Results demonstrated that the proposed approach effectively detected increasing damage levels, thereby distinguishing different structural states from one another. Results highlighted the potential robustness and adaptability of the proposed approach to real-world SHM applications.

Numerous catastrophic events, including fire, vehicle collisions, and air blasts, have highlighted the significance of examining bridge performance under multi-hazard scenarios. While these hazards cause extensive damage, the loss of life, and drastically impact economies, limited attention has been devoted to study the behavior of bridge structural elements under such extreme demand combinations. Hence, comprehensive research to understand the resiliency of bridges and their response to combinations of fire, vehicular impact, and air blast is warranted so that effective retrofitting techniques can be developed and design recommendations be made. To address this research gap, present investigations utilized previously validated finite element (FE) models in LS-DYNA to study the structural behavior of two-, three-, and four-column piers under post-fire medium truck collision and subsequent air blast. The response of multi-column piers was quantified and evaluated based on damage propagation, failure patterns, and permanent deformation sets. The effectiveness of selected retrofitting techniques that employed carbon-fiber-reinforced polymers (CFRPs) to mitigate damage was investigated. Study findings enhance current understanding, provide valuable insights, and can ultimately be used to ensure safety and improve the structural integrity of bridge piers under coupled vehicle collision and air blast following fire exposure.