Refine abstract wording for clarity and adjust figure width in methodology flow chart

Code/models/width_optimization/3W/ml_models/per_output_models_B34_H45/it0/cv_summary_per_output_B34_H45.csv

 output,best_model,cv_rmse,std_rmse,cv_rmse_dispersion,cv_mae,cv_r2,BEST_PARAMS,model_path,train_time_sec,gpr_kernel,selected_by
-exymax_tw3,SVR,0.007202212127447048,0.004481301562810276,0.6222118265209654,0.007202212127447048,,"{""svr__C"": 1117.125712746634, ""svr__epsilon"": 0.0006465958496706061, ""svr__gamma"": 0.004670356278081838}",../../models/width_optimization/3W/ml_models/per_output_models_B34_H45/it0/best_model_exymax_tw3.joblib,889.41,,lowest_cv_rmse_dispersion_within_5pct_rmse_band
-exymax_tw1,GradientBoosting,0.007300943573175188,0.007955575607590385,1.089664031484974,0.007300943573175188,,"{""learning_rate"": 0.016754195403105226, ""max_depth"": 3, ""max_features"": 0.6676595772974903, ""n_estimators"": 1169, ""subsample"": 0.7322018725625201}",../../models/width_optimization/3W/ml_models/per_output_models_B34_H45/it0/best_model_exymax_tw1.joblib,425.58,,lowest_cv_rmse_dispersion_within_5pct_rmse_band
-exymax_tw2,SVR,0.008460412231077893,0.011444731445050746,1.3527392203196034,0.008460412231077893,,"{""svr__C"": 9808.35589449746, ""svr__epsilon"": 0.00016322664292135178, ""svr__gamma"": 0.00038258041488189597}",../../models/width_optimization/3W/ml_models/per_output_models_B34_H45/it0/best_model_exymax_tw2.joblib,382.41,,lowest_cv_rmse_dispersion_within_5pct_rmse_band
-tfmmax_frame,SVR,1.7183858166563564,1.8940457119290446,1.1022237809285973,1.7183858166563564,,"{""svr__C"": 2338.7077018101695, ""svr__epsilon"": 0.0015121721150378067, ""svr__gamma"": 0.08059242367780602}",../../models/width_optimization/3W/ml_models/per_output_models_B34_H45/it0/best_model_tfmmax_frame.joblib,334.62,,lowest_cv_rmse_dispersion_within_5pct_rmse_band
-tfmmax_tw2,SVR,6.570230475951083,8.231729016702195,1.252882839777489,6.570230475951083,,"{""svr__C"": 9598.629415551462, ""svr__epsilon"": 0.008177764525277858, ""svr__gamma"": 0.06450806413377351}",../../models/width_optimization/3W/ml_models/per_output_models_B34_H45/it0/best_model_tfmmax_tw2.joblib,497.18,,lowest_cv_rmse_dispersion_within_5pct_rmse_band
-tfmmax_tw3,SVR,6.867884256625402,7.773194090294852,1.131817864110931,6.867884256625402,,"{""svr__C"": 4770.813296275575, ""svr__epsilon"": 0.0156951512570451, ""svr__gamma"": 0.04171961411779374}",../../models/width_optimization/3W/ml_models/per_output_models_B34_H45/it0/best_model_tfmmax_tw3.joblib,505.18,,lowest_cv_rmse_dispersion_within_5pct_rmse_band
-tfmmax_tw1,GaussianProcess,12.729753975937573,16.8283131798545,1.3219668826015203,12.729753975937573,,"{""gpr__amplitude"": 42.64486576023699, ""gpr__kernel_type"": ""Matern32"", ""gpr__length_scale"": 1.5803508571081364, ""gpr__n_restarts_optimizer"": 10, ""gpr__noise"": 2.537051332183834e-05, ""gpr__rq_alpha"": 0.01469218297101831}",../../models/width_optimization/3W/ml_models/per_output_models_B34_H45/it0/best_model_tfmmax_tw1.joblib,872.27,"1.98**2 * Matern(length_scale=4.65, nu=1.5) + WhiteKernel(noise_level=2.91e-12)",lowest_cv_rmse_dispersion_within_5pct_rmse_band
+exymax_tw3,GradientBoosting,0.0068603803201981095,0.004414078348347289,0.6434159831272768,0.0068603803201981095,,"{""learning_rate"": 0.06837225459225127, ""max_depth"": 1, ""max_features"": 1.0, ""n_estimators"": 665, ""subsample"": 0.7432434647174244}",../../models/width_optimization/3W/ml_models/per_output_models_B34_H45/it0/best_model_exymax_tw3.joblib,628.78,,lowest_cv_rmse_dispersion_within_5pct_rmse_band
+exymax_tw1,GradientBoosting,0.007508034631569018,0.007904693272151635,1.0528312214910127,0.007508034631569018,,"{""learning_rate"": 0.013992700494766131, ""max_depth"": 1, ""max_features"": 0.7284173699827374, ""n_estimators"": 653, ""subsample"": 0.7783052950286513}",../../models/width_optimization/3W/ml_models/per_output_models_B34_H45/it0/best_model_exymax_tw1.joblib,601.34,,lowest_cv_rmse_dispersion_within_5pct_rmse_band
+exymax_tw2,SVR,0.009186565415790389,0.012037200008128898,1.3103047181745069,0.009186565415790389,,"{""svr__C"": 7657.665720689394, ""svr__epsilon"": 0.0001005489514731489, ""svr__gamma"": 0.002834946185340044}",../../models/width_optimization/3W/ml_models/per_output_models_B34_H45/it0/best_model_exymax_tw2.joblib,458.08,,lowest_cv_rmse_dispersion_within_5pct_rmse_band
+tfmmax_frame,SVR,1.7177103097723787,1.8549725916653637,1.0799100297134354,1.7177103097723787,,"{""svr__C"": 6533.142258976154, ""svr__epsilon"": 0.0001, ""svr__gamma"": 0.08195502077068523}",../../models/width_optimization/3W/ml_models/per_output_models_B34_H45/it0/best_model_tfmmax_frame.joblib,811.06,,lowest_cv_rmse_dispersion_within_5pct_rmse_band
+tfmmax_tw3,SVR,5.810063419475952,6.282300755421094,1.0812792050362396,5.810063419475952,,"{""svr__C"": 10000.0, ""svr__epsilon"": 0.0001, ""svr__gamma"": 0.031517031726350814}",../../models/width_optimization/3W/ml_models/per_output_models_B34_H45/it0/best_model_tfmmax_tw3.joblib,990.88,,lowest_cv_rmse_dispersion_within_5pct_rmse_band
+tfmmax_tw2,SVR,6.5390910243938345,8.13574592251621,1.2441707711616359,6.5390910243938345,,"{""svr__C"": 10000.0, ""svr__epsilon"": 0.0001, ""svr__gamma"": 0.0616129434310082}",../../models/width_optimization/3W/ml_models/per_output_models_B34_H45/it0/best_model_tfmmax_tw2.joblib,1227.82,,lowest_cv_rmse_dispersion_within_5pct_rmse_band
+tfmmax_tw1,GaussianProcess,12.729995556678173,16.82853875079846,1.3219595149010235,12.729995556678173,,"{""gpr__amplitude"": 34.811393285568194, ""gpr__kernel_type"": ""Matern32"", ""gpr__length_scale"": 1.7360857163205443, ""gpr__n_restarts_optimizer"": 7, ""gpr__noise"": 6.149981313878758e-06, ""gpr__rq_alpha"": 0.32262023280202834}",../../models/width_optimization/3W/ml_models/per_output_models_B34_H45/it0/best_model_tfmmax_tw1.joblib,546.88,"1.98**2 * Matern(length_scale=4.65, nu=1.5) + WhiteKernel(noise_level=6.15e-06)",lowest_cv_rmse_dispersion_within_5pct_rmse_band

Code/reports/width_optimization/3W/ml_optimization/it0/optimization_results_3W_B34_H45_TFD_W90.csv

@@ -4,14 +4,14 @@ Configuration_B,34.0
 Configuration_H,45.0
 Configuration_TFD_W,90.0
 Iteration,0.0
-tw1_optimal,7.333816578195384
-tw2_optimal,9.279818580001832
-tw3_optimal,10.121143978945401
-Objective_score,-1.0932680460256248e-06
-Exy_tw1,0.030541286356165126
-Exy_tw2,0.038966386800767694
-Exy_tw3,0.04736176051237062
-TFM_tw1,90.00000000888014
-TFM_tw2,90.0000000363739
-TFM_tw3,90.00000004969976
-TFM_frame,71.65152648580627
+tw1_optimal,7.335514425108832
+tw2_optimal,9.275405365097614
+tw3_optimal,10.131483041829242
+Objective_score,-1.164917859388524e-06
+Exy_tw1,0.03216894707627307
+Exy_tw2,0.037084433954330365
+Exy_tw3,0.0509747966289918
+TFM_tw1,90.0000000652608
+TFM_tw2,90.00000014055266
+TFM_tw3,90.00000000857099
+TFM_frame,71.57483700216243

Code/reports/width_optimization/5W/ml_optimization/it0/optimization_results_5W_B34_H60_TFD_W90.csv

@@ -4,20 +4,20 @@ Configuration_B,34.0
 Configuration_H,60.0
 Configuration_TFD_W,90.0
 Iteration,0.0
-tw1_optimal,5.694179976812822
-tw2_optimal,7.254866605325219
-tw3_optimal,8.140135383492023
-tw4_optimal,6.653343430447909
+tw1_optimal,5.968722571013082
+tw2_optimal,7.378597212571523
+tw3_optimal,8.561353982349441
+tw4_optimal,6.7035670059260966
 tw5_optimal,5.0
-Objective_score,17.106344387688598
-Exy_tw1,0.06127584151539267
-Exy_tw2,0.05514946520902724
-Exy_tw3,0.06789993494749069
-Exy_tw4,0.06857663218038944
-Exy_tw5,0.053409088461247346
-TFM_tw1,90.00000005473717
-TFM_tw2,90.00000072572061
-TFM_tw3,90.0000006322008
-TFM_tw4,90.00031542767577
-TFM_tw5,72.89365290337983
-TFM_frame,73.03210418991345
+Objective_score,10.582751997380363
+Exy_tw1,0.05963421438042871
+Exy_tw2,0.052582168193077816
+Exy_tw3,0.06459011210098246
+Exy_tw4,0.06971287318400625
+Exy_tw5,0.05512272509539415
+TFM_tw1,90.00000292077007
+TFM_tw2,90.00000008463275
+TFM_tw3,90.000008866776
+TFM_tw4,90.00000753883465
+TFM_tw5,79.41724526649921
+TFM_frame,74.65262096197034

Manuscript/RESILINK_surrogate_optimization_manuscript.tex

@@ -45,7 +45,7 @@
 
 \corres{J. Irazábal. \email{jirazabal@cimne.upc.edu}}
 
-\abstract[Abstract]{Buckling-delayed shear-link (BDSL) dampers are extensively used in seismic-resistant structures as passive devices that concentrate energy dissipation while limiting damage to the primary system. Their geometric optimization requires a compromise between high energy dissipation and control of local damage. Finite element method (FEM) models can reproduce with high accuracy the nonlinear cyclic response of these devices and provide internal quantities such as damage indicators and local distortion but their computational cost prevents their direct use inside iterative optimization loops. This work proposes an adaptive surrogate-assisted optimization framework for BDSL dampers. First, experimentally calibrated nonlinear FEM models are used to generate ground-truth datasets for damper configurations with different numbers of windows and geometric proportions. Supervised learning models are first evaluated, where Support Vector Regression (SVR) and Gaussian Process Regression (GPR)—both based on radial kernel functions—consistently provide the highest predictive accuracy. Motivated by this observation, Radial Basis Function (RBF) surrogates are subsequently introduced as a computationally efficient alternative. The surrogate predictions are coupled with a Differential Evolution algorithm through a damage-aware objective function that limits the damage and uses dissipated energy as a tie-breaking performance criterion. In addition, SHapley Additive exPlanations (SHAP) are employed to quantify the influence of window thickness on damage distribution, with particular emphasis on the response of the surrounding frame. Optimized geometries are finally re-evaluated with FEM. When the surrogate error exceeds the adopted tolerances, the new FEM result is added to the dataset and the surrogate models are retrained. The proposed framework provides a scalable route for an efficient damage-aware optimization of seismic energy dissipation devices.}
+\abstract[Abstract]{Buckling-delayed shear-link (BDSL) dampers are used in seismic-resistant structures as passive devices that concentrate energy dissipation while limiting damage to the primary system. Their geometric optimization requires a compromise between high energy dissipation and control of local damage. Finite element method (FEM) models can reproduce with high accuracy the nonlinear cyclic response of these devices and provide internal quantities such as damage indicators and local distortion but their computational cost prevents their direct use inside iterative optimization loops. This work proposes an adaptive surrogate-assisted optimization framework for BDSL dampers. First, experimentally calibrated nonlinear FEM models are used to generate ground-truth datasets for damper configurations with different numbers of windows and geometric proportions. Supervised learning models are first evaluated, where Support Vector Regression (SVR) and Gaussian Process Regression (GPR)—both based on radial kernel functions—consistently provide the highest predictive accuracy. Motivated by this observation, Radial Basis Function (RBF) surrogates are subsequently introduced as a computationally efficient alternative. The surrogate predictions are coupled with a Differential Evolution algorithm through a damage-aware objective function that limits the damage and uses dissipated energy as a tie-breaking performance criterion. In addition, SHapley Additive exPlanations (SHAP) are employed to quantify the influence of window thickness on damage distribution, with particular emphasis on the response of the surrounding frame. Optimized geometries are finally re-evaluated with FEM. When the surrogate error exceeds the adopted tolerances, the new FEM result is added to the dataset and the surrogate models are retrained. The proposed framework provides a scalable route for an efficient damage-aware optimization of seismic energy dissipation devices.}
 
 \keywords{Buckling-delayed shear link, seismic energy dissipation, surrogate modelling, machine learning, radial basis functions, Differential Evolution, FEM validation, TFDMap}
 
@@ -87,7 +87,7 @@ Figure \ref{fig:MethodologyFlowChart} summarizes the proposed workflow. The diff
 
 \begin{figure}[!ht]
   \centering
-  \includegraphics[width=0.6\textwidth]{./Figures/MethodologyFlowChart.png}
+  \includegraphics[width=0.75\textwidth]{./Figures/MethodologyFlowChart.png}
   \caption{Flow chart of the proposed adaptive surrogate-assisted optimization framework.}
   \label{fig:MethodologyFlowChart}
 \end{figure}