Finite Element Model Updating-based Load Rating of Bridges with Incomplete As-Built Information
2026-08-31 • Computational Engineering, Finance, and Science
Computational Engineering, Finance, and Science
AI summaryⓘ
The authors present a method to figure out how much weight a bridge can safely carry when detailed blueprints are missing or incomplete. They use a computer model of the bridge, which they improve using real test data and a mix of computer algorithms that try to guess unknown parts of the bridge’s structure. This updated model then helps calculate the bridge's load capacity more accurately. They tested their method on four real bridges and found it gave results close to expected values, showing it works well even with imperfect information.
load ratingfinite element modelmodel updatingABAQUSMATLAB interfacegenetic algorithmgradient-based optimizationstructural identificationbridge engineeringload capacity
Authors
Mehrdad S. Dizaji, Devin K. Harris, Mohamad Alipour, Abdou Ndong, Osman Ozbulut, Abdollah Bagheri
Abstract
Load rating is the engineering process of determining the safe load-carrying capacity of an existing bridge structure often through analysis of its load-bearing members and their cross sections. However, when structural drawings and details of the structure are missing or insufficient for such calculations, alternative solutions must be used to infer the capacity. This study proposes a rational and consistent engineering solution for capacity inference based on structural identification (St-Id). The proposed approach uses finite element model updating (FEMU) to estimate the unknown characteristics of structures for use in an analytical load rating, which requires the development of an initial model developed in ABAQUS that can be updated based on experimental test data. ABAQUS allowed for the development of an interface with MATLAB, which facilitated the integration of an automatic iterative parameter optimization algorithm. The optimization algorithm developed in this investigation incorporated the features of a genetic algorithm (GA) and a gradient-based scheme to iterate on the unknown parameters. The proposed approach was evaluated on four in-service highway bridges including two concrete slab and two T-beam structures in varying deterioration condition states, which had sufficient plans available but were treated as having varying degrees of unknown details. The results illustrated that the finite element (FE) model updating approach generated load ratings that were within 0-17% of the target load ratings while alleviating the main challenges of the existing alternatives.