Dataset of surrogate modeling and optimization for bolted timber joints in glulam structures

<p dir="ltr">This dataset supports the findings of the study titled </p><p dir="ltr">“Data-driven and interpretable surrogate modeling for mechanical performance prediction of bolted timber joints in long-span glulam structures”.</p><p><br></p><p dir="ltr">The dataset includes experimental data, finite element simulation results, and surrogate modeling data used to investigate the semi-rigid mechanical behavior of bolted timber joints.</p><p><br></p><p dir="ltr">Specifically, the dataset contains:</p><p dir="ltr">(1) Full-scale experimental test data of bolted timber joints, including moment–rotation (M–θ) curves;</p><p dir="ltr">(2) Validated finite element simulation results calibrated against experimental observations;</p><p dir="ltr">(3) Input–output datasets used for training and testing surrogate models (SVR, Random Forest, XGBoost, and MLP-BP);</p><p dir="ltr">(4) Prediction results of surrogate models for key mechanical performance indicators, including strength, initial stiffness, and ductility;</p><p dir="ltr">(5) Multi-objective optimization results obtained using NSGA-II, including Pareto-optimal solution sets.</p><p><br></p><p dir="ltr">The surrogate models are further interpreted using SHAP (SHapley Additive exPlanations) to quantify the contribution of input parameters to the predicted responses.</p><p><br></p><p dir="ltr">All data are provided in accessible formats (e.g., CSV, MAT, or XLSX) and can be used to reproduce the results presented in the associated publication.</p><p><br></p><p dir="ltr">This dataset is intended for researchers and engineers working on timber structures, structural connections, and data-driven structural design.</p>