One of the appealing alternatives, especially for detect, localize, and quantify the multiple simultaneously damages in structures, is using dataset-based damage detection (DBDD) technics. While machine learning (ML) algorithms have been applied to pattern recognition in DBDD studies, comprehensive research and sensitivity analysis on the effects of various parameters, such as ML algorithm choice, hyperparameter (HP) tuning, and feature engineering techniques, remain limited. This can hinder the selection of optimal parameter values, especially for large structures, without incurring significant computational costs. This study addresses this gap by employing extensive single-objective sensitivity analyses to evaluate the impact of these parameters on DBDD damage detection in a 3D sample structure, whose numerical model has been verified by comparing its Frequency Response Functions (FRFs) with experimental data. By comparing grid search outcomes, it was shown that leveraging insights from single-objective optimization can significantly reduce computational costs by limiting the parameter search space. Additionally, an innovative hybrid feature engineering method is proposed to enhance feature quality, reduce dataset size, and enable the feasibility of conducting extensive sensitivity analyses. Furthermore, the study investigates the impact of ensemble techniques with tuned algorithms and excitation point configuration on DBDD prediction accuracy. By using proposed feature engineering, optimal excitation configuration, optimized ML algorithms and HPs, in addition to shrinking the dataset to 5% of its original size, the accuracy of DBDD damage prediction can be improved by up to 60%. These results can be leveraged to pre-select optimal parameters for DBDD damage detection in similar structures, significantly reducing computational costs and improving accuracy.
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