Structural health monitoring refers to the evaluation of the health condition of engineering structures through real-time or periodic monitoring.Deep learning methods have gained attention due to their ability to extract high-level features from raw data.However,the diversity of damage types in practical applications and the lack of quantitative analysis for damage states remain challenging.In this paper,a partial skip-connected convolutional autoencoder-based approach for damage assessment and quantification is proposed.This method utilizes a convolutional autoencoder to process structural responses,reducing high-dimensional data to a low-dimensional feature space.A damage index is defined based on reconstruction error to assess health status,while a damage coefficient constructed from the low-dimensional features enables quantitative damage assessment.The effectiveness of the algorithm in damage detection and quantification is validated using the IASC-ASCE benchmark structures Ⅰ and Ⅱ datasets.Experimental results demonstrate that the damage index achieves 100％accuracy in identifying most damage states,with 96％accuracy in certain specific cases,and that the quantification aligns well with expected values across different damage states.
structural health monitoringconvolutional autoencoderdamage quantification
维普
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