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https://www.nationalacademies.org/trb/events

Inverse Problem Approaches for Bridge Structural Health Monitoring Using Displacement Data

This project aims to develop an inverse problem framework for bridge structural health monitoring (SHM) using displacement data as the primary diagnostic input. Traditional SHM methods based on finite element model updating and contact-based sensor networks are computationally demanding and require extensive field calibration, while acceleration-based techniques struggle to detect local damage. To address these limitations, the study applies inverse problem-solving methodologies that enable the direct inference of unknown structural parameters—such as stiffness variations, damage locations, and boundary conditions—from displacement measurements. Recent advancements in computer vision technologies have significantly improved the accessibility, accuracy, and cost-effectiveness of displacement data collection, making bridge condition assessment increasingly feasible. Through data analysis, inverse modeling, and validation, the research develops a validated framework for bridge condition assessment based on displacement data, reducing reliance on contact sensor networks and improving the accuracy of local damage detection across transportation infrastructure.