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.