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 1D 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.

In the context of this thesis, the dataset and experimental setup introduced by [Author(s), Year] form the foundation for comparative analysis and algorithm testing. The authors have not only demonstrated the efficacy of a compact 1D CNN-based system for vibration-based structural damage detection, but also highlighted the value of using output-only acceleration data—a constraint shared in this thesis’s methodology. The decentralized design of their system, which allows each CNN to process only locally available data, is particularly aligned with this thesis's focus on efficient, sensor-level data analysis without requiring full-system synchronization. Furthermore, since the authors indicate plans to publicly release their dataset and source code, this thesis leverages that open data for applying alternative analysis methods such as support vector machines (SVM) or frequency domain feature extraction techniques, allowing a direct performance comparison between classical and deep learning-based SHM approaches. Thus, this work serves as both a benchmark reference and a data source in the development and evaluation of more accessible, lower-complexity alternatives for structural health monitoring systems.

