Deep learning-based automated damage assessment for RC double-column piers

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Deng, Hairong and Li, Haijiang and Xu, Lueqin and Deng, Zhewen; Moreno-Rangel, Alejandro and Kumar, Bimal, eds. (2025) Deep learning-based automated damage assessment for RC double-column piers. In: EG-ICE 2025. University of Strathclyde Publishing, GBR, pp. 569-577. ISBN 9781914241826 (https://doi.org/10.17868/strath.00093254)

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Abstract

Reinforced concrete (RC) double-column piers, essential bridge substructures, are highly susceptible to earthquake damage. Traditional damage assessment methods primarily depend on visual inspection and structural analysis, which are often subjective and inefficient. This study proposes a Hybrid Structural-Visual Damage Evaluation (HSVDE) framework integrating structural analysis and deep learning-based computer vision. The structural analysis provides an initial classification of performance levels using material strain and drift ratio. To enhance evaluation accuracy and enable rapid post-earthquake assessment, a modified DeepLabv3+ model is employed to identify concrete spalling and exposed rebar. Finite element analysis was utilised to determine drift ratio thresholds for each performance level. The modified DeepLabv3+ model significantly improved rebar detection accuracy, achieving an IoU of 42.80% compared to 33.37%, with only a slight decrease in spalling detection accuracy. The proposed HSVDE framework enhances the accuracy, reliability, and efficiency of seismic damage evaluation, supporting timely emergency response and recovery.

  • Item type:Book Section
    ID code:93254
    Dates:
    Date
    Event
    1 July 2025
    Published
    9 June 2025
    Accepted
    Subjects:Fine Arts > Architecture
    Department:Faculty of Engineering > Architecture
    Depositing user: Pure Administrator
    Date deposited:27 Jun 2025 10:37
    Last modified:05 Jun 2026 13:05
    URI:https://strathprints.strath.ac.uk/id/eprint/93254
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