Traditional structural health monitoring methods often rely on hand-crafted features and manually tuned classifiers, which pose challenges in terms of generalization, reliability, and computational efficiency. As highlighted by [Author(s), Year], these approaches frequently require a trial-and-error process for feature and classifier selection, which not only reduces their robustness across structures but also hinders their deployment in real-time applications due to the computational load of the feature extraction phase.

[Author(s), Year] introduced a CNN-based structural damage detection approach validated through a large-scale grandstand simulator at Qatar University. The structure, designed to replicate modern stadiums, was equipped with 30 accelerometers and subjected to controlled damage by loosening beam-to-girder bolts. Acceleration data, collected under band-limited white noise excitation and sampled at 1024 Hz, were segmented into 128-sample frames for training localized 1-D CNNs—one per joint—creating a decentralized detection system. Across two experimental phases, involving both partial and full-structure monitoring, the method demonstrated high accuracy in damage localization, achieving a training classification error of just 0.54\%. While performance remained strong even under double-damage scenarios, some misclassifications occurred in symmetric or adjacent damage cases. Overall, the proposed method presents a highly efficient and accurate solution for real-time SHM applications.