Update .gitignore to include additional manuscript auxiliary files and refine…

.gitignore

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+Manuscript/*.pag
 
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-\abstract[Abstract]{The geometric optimization of shear-link beam (SLB) dampers under cyclic loading is challenged by strong nonlinear behavior and the need to control local damage mechanisms. High-fidelity finite element method (FEM) simulations provide detailed information on global response and internal damage, but their computational cost limits their use in design optimization.
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-This work proposes a surrogate-assisted optimization framework based on FEM-generated datasets covering SLB configurations with varying geometric complexity. Supervised learning models and Radial Basis Function (RBF) approximations are used to approximate performance indicators and enable efficient exploration of the design space. The different surrogate approaches are systematically compared in terms of predictive accuracy and computational cost. An interpretability analysis based on SHapley Additive exPlanations (SHAP) is incorporated to quantify the influence of geometric variables.
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-Results demonstrate an effective trade-off between accuracy and efficiency, and provide insight into key design drivers, enabling fast and reliable optimization of SLB dampers.}
+\abstract[Abstract]{The geometric optimization of shear-link beam (SLB) dampers under cyclic loading is challenged by strong nonlinear behavior and the need to control local damage mechanisms. High-fidelity finite element method (FEM) simulations provide detailed information on global response and internal damage, but their computational cost limits their use in design optimization. This work proposes a surrogate-assisted optimization framework based on FEM-generated datasets covering SLB configurations with varying geometric complexity. Supervised learning models and Radial Basis Function (RBF) approximations are used to approximate performance indicators and enable efficient exploration of the design space. The different surrogate approaches are systematically compared in terms of predictive accuracy and computational cost. An interpretability analysis based on SHapley Additive exPlanations (SHAP) is incorporated to quantify the influence of geometric variables. Results demonstrate an effective trade-off between accuracy and efficiency, and provide insight into key design drivers, enabling fast and reliable optimization of SLB dampers.}
 
 \keywords{Hysteresis, surrogate modeling, supervised learning, radial basis functions, structural optimization, FEM-calibrated models, shear-link dampers, model interpretability, SHAP}