Can past failures help identify vulnerable bridges to extreme events? A biomimetical machine learning approach

With limited resources to properly maintain and upgrade transportation infrastructure, bridges often end up exceeding their expected service lifespan; thus, becoming vulnerable to the adverse effects of aging and extreme loading conditions. In order to better assess the vulnerability of these structures, this study showcases the outcome of an observational analysis that utilizes biomimetical (bio-inspired) machine learning algorithms to predict the vulnerability and expected degree of damage in bridges in the aftermath of an extreme loading event (such as fire, flood, earthquake, etc.). These algorithms comprise deep learning, decision tree, genetic algorithm and genetic programing and were trained and validated using 299 international incidents covering a wide variety of bridge systems/configurations, traffic demands, etc. Based on this analysis, user-friendly assessment tools that can be used to evaluate propensity of a given bridge to undergo high levels of damage and/or collapse are developed. These tools can aid designers and decision-makers in evaluating performance of new or existing bridges against a variety of hazards, as well as in developing relevant design strategies for mitigating disaster-induced failures as to minimize disruptions to supply chain operations and/or evacuations during an emergency.

Automated summary

This study uses bio-inspired machine learning algorithms to predict the vulnerability and expected damage degree of bridges after extreme loading events, and develops assessment tools to aid designers and decision-makers in evaluating bridge performance and reducing disaster-induced losses.