Partial‐Model‐Based Damage Identification of Long‐Span Steel Truss Bridge Based on Stiffness Separation Method
Feng Xiao, Yuxue Mao, Geng Tian, Gang S. Chen
Abstract
Damage detection in bridge structures has always been challenging, particularly for long‐span bridges with complex structural forms. In this study, a partial‐model‐based damage detection method was proposed for the damage identification of long‐span steel truss bridges. The proposed method employs partial models to estimate the parameters using the stiffness separation method. This approach obviates the need to construct complete stiffness information for the structure. In contrast, it depends solely on the arrangement of the structural members and material information in the recognized area. This technique can effectively circumvent the construction of an overall structural model and reduce the complexity of damage identification in large structures. A full‐scale long‐span steel truss bridge in service was used to illustrate the feasibility of the proposed method. The locations of the three partial models were considered in the model analysis, and the parameter estimation efficiency of the Nelder–Mead simplex and quasi‐Newton algorithms were compared.