Data-driven fatigue and load rating analysis of composite girder bridges

Composite girder bridges form a vital component of modern highway infrastructure, combining steel and concrete to achieve structural efficiency and economic viability. However, with increasing traffic volumes and aging infrastructure, assessing the fatigue life and load-carrying capacity of such bridges has become imperative. This study presents a data-driven approach to fatigue and load rating analysis of composite girder bridges using field data collected from in-situ sensors, traffic logs, and structural health monitoring systems. A case study bridge located in a high-traffic corridor was instrumented, and the collected data were processed using statistical and machine learning techniques to estimate stress ranges, fatigue damage, and updated load ratings. Results revealed significant variations in stress cycles across different girders, emphasizing the need for data-centric decision-making in maintenance planning. This approach enhances safety assessments, supports life-cycle management, and offers a scalable model for bridge authorities managing large inventories of composite bridges.