Science Book Cover Design (Featuring water, bridge, and ai)

Genre: Science And Technology

Emotions: Real world engineering

Elements: Bridge management, artificial intellegence

Layout: 16. Abstract This project develops a methodology and tools for a risk-based multi-threat decision-support tool for long-term bridge asset management (BAM), with a particular focus on chronic aging-induced condition deterioration, and more abrupt and extreme seismic hazard impact. Specifically, a stochastic bridge condition deterioration and seismic damage simulation module is developed. Bridge condition deterioration is modeled through Markovian state transition dynamics considering different maintenance actions, using multiple information sources such as the National Bridge Inventory (NBI) bridge condition database and the FHWA Bridge Preservation Guide. Seismic fragility modeling and risk assessment is carried out, considering site-specific seismic hazard and the effect of seismic retrofitting actions. A life cycle cost analysis module is introduced to holistically quantify and aggregate the direct and indirect costs incurred from bridge condition deterioration, seismic damage, and intervention actions over a prolonged planning horizon. Benefit-cost analysis for various seismic retrofitting actions is also performed. Finally, by integrating the above bridge deterioration and seismic damage simulation module and the life-cycle cost analysis module with the advanced AI technique, deep reinforcement learning (DRL), a methodology for generating AI-based policies for sequential maintenance decision support for a portfolio of bridges is proposed. Departing from traditional reactive condition-based decision policies, these AI-based policies can offer much more proactive and adaptive decisions to minimize the expected long-term life-cycle costs. Owing to the parametrized DRL formulation, the AI-based policies can flexibly accommodate the decision needs from different individual bridges within a bridge portfolio in near real time. Practical action constraints are also introduced to align with real-world engineering practices. The proposed AI-based policies are evaluated based on individual bridges as well as on a portfolio of bridges, and demonstrate superior performance in reducing the life-cycle costs compared with other condition-based policies. In addition, the AI-based policies also exhibit robustness to potential human override. Finally, an investigation into the effect of seismic retrofitting, when coupled with AI-based agents, is conducted for more comprehensive life-cycle benefit-cost evaluation of seismic retrofit actions.

Colors: light blue