- Revised the abstract and introduction sections of the manuscript to clarify the optimization framework for buckling-delayed shear-link dampers.
- Updated design variables and parameters in the manuscript to improve clarity and accuracy.
- Enhanced the validation section with additional details on the FEM model and experimental setup.
- Improved the damage-aware optimization methodology, emphasizing the balance between damage control and energy dissipation.
- Added a new reference for a conference paper on nonlinear dynamic analysis of structures equipped with shear links.
- Introduced a new PDF document related to the optimization layout.

This commit introduces a new script, `rbf_objective_shap_beeswarm.py`, which generates a SHAP beeswarm plot for the RBF optimization objective function. The script includes functionality to load RBF models, prepare background data, compute SHAP values, and save the results in various formats. It also implements a uniform design for sampling and applies a specific styling for the plots.

- Replaced specific geometry family names (H30_B29, H30_B34, etc.) with generic identifiers (F_1, F_2, etc.) throughout the manuscript for confidentiality.
- Adjusted descriptions and captions in figures and tables to reflect the new identifiers.
- Updated design variable definitions and thickness ranges in the manuscript to align with the new naming convention.
- Enhanced clarity in the discussion of optimization results and surrogate model comparisons.
- Ensured consistency in the presentation of data across all sections of the manuscript.

- Updated the RESILINK surrogate optimization manuscript PDF to reflect recent changes.
- Added a new synctex.gz file for improved LaTeX editing and referencing.
- Revised the manuscript text to include conclusions in the workflow summary.
- Introduced a new section on dataset generation and surrogate modeling, detailing the use of FEM models and damage indicators.
- Removed redundant section header for clarity in the design of experiments description.

- Created new manuscript file for RESILINK surrogate optimization.
- Added PDF version of the manuscript.
- Updated LaTeX file to reference the correct journal name and include necessary figures.
- Adjusted figure paths to ensure correct referencing in the document.
- Modified USG.cls to correctly handle page numbering for the manuscript.
- Updated bibliography path in the manuscript for proper citation.

- Clarified the description of FEM simulations and loading patterns based on device height.
- Improved the explanation of damage indicators and their computation for optimization.
- Updated the definitions of local shear distortion and its role in energy dissipation.
- Enhanced the description of supervised ML surrogate models and their training process.
- Added detailed hyperparameter search spaces for various models used in Bayesian optimization.
- Revised the objective function formulation to emphasize damage control and energy dissipation balance.
- Improved clarity in the penalty definitions for window and frame damage.
- Summarized the validation process for optimized geometries and the iterative nature of the optimization loop.

Refine manuscript text for clarity and consistency in the description of design variables, optimization cycles, and surrogate model performance. Adjust punctuation and phrasing for improved readability throughout the document.

- Added new figures: TempBayesian.png and TempBayesian.svg to the Figures directory.
- Updated references in the manuscript to use PDF versions of figures for better quality:
  - MethodologyFlowChart.png to MethodologyFlowChart.pdf
  - BayesianSearchCV.png to BayesianSearchCV.pdf

- Updated manuscript title to reflect a focus on comparison of surrogate strategies for damage-aware optimization.
- Improved clarity in the results section by refining descriptions of surrogate model performance and validation metrics.
- Enhanced the discussion on the adaptive validation loop and its impact on optimization outcomes.
- Clarified the interpretation of objective-function values and the implications of damage control in the optimization framework.
- Added figures to illustrate the evolution of optimized window thicknesses and RBF objective surfaces during the optimization process.

- Created `surrogate_selection_summary_by_variable_type.csv` and `surrogate_selection_summary_by_variable_type_wide.csv` to summarize model performance across different variable types and window groups.
- Updated the manuscript to include new figures and tables reflecting the results of the surrogate model comparisons, including predictive performance and optimization results.
- Adjusted figure placements to improve layout and readability.
- Enhanced discussions on the effectiveness of supervised ML models versus RBF interpolation in the context of surrogate-assisted optimization.

Update manuscript to enhance clarity in RBF surrogate validation and streamline adaptive FEM validation section

- Updated the objective function to use area factors instead of volume factors for window contributions.
- Clarified the formulation of damage penalties, emphasizing the severity of frame damage.
- Adjusted terminology in the manuscript to reflect changes in the optimization approach.
- Revised table captions and contents to align with the new area factor usage.

- Introduced a new Python script `plot_load_patterns.py` to visualize loading pattern displacement histories from `LoadPatterns.csv`, generating figures in PNG, SVG, and PDF formats.
- Removed the outdated `PatronesCarga.csv` file as it is no longer needed.
- Revised the abstract and various sections of the manuscript to enhance clarity and detail, including the introduction of radial basis function surrogates and adjustments to the optimization methodology.
- Updated references in the bibliography to include new citations for SciPy and Differential Evolution.

Remove obsolete optimization result files and add new dataset for load patterns; update manuscript for clarity and detail in methodology.

Update FEM data files and model scripts for width optimization; add new data points and adjust training iterations

- Introduced `plot_fem_validation.py` to visualize FEM validation curves against experimental measurements.
- The script reads hysteresis and cumulative dissipated energy data from text files and generates a two-panel figure.
- Added `skeleton.txt` containing sample data for hysteresis and force measurements.
- Configured plot aesthetics for clarity and consistency in presentation.

- Added new figures: H30_B29.png, H30_B34.png, H45_B29.png, H45_B34.png, H60_B34.png, H30_29.png, H30_34.png, H45_29.png, H45_34.png, H60_34.png, and updated MethodologyFlowChart.png.
- Revised manuscript title to "Damage-aware surrogate optimization of buckling-delayed shear-link dampers with adaptive finite element validation."
- Updated abstract to reflect changes in focus and methodology.
- Modified keywords for improved relevance.
- Enhanced figure descriptions and captions for clarity.

Update FEM model description and add new references in the manuscript; adjust figure captions for clarity

Revise abstract and methodology sections for clarity; update design variables and geometry families in manuscript

- Updated cross-validation results for models in B29 and B34, including new performance metrics and model parameters.
- Added new model outputs for Gradient Boosting and SVR in the cv_summary_per_output_B29_H45.csv file.
- Introduced new figures and updated manuscript sections to reflect changes in model selection and optimization strategies.
- Included new binary files for model outputs and results in both B29 and B34 directories.
- Enhanced the Bayesian optimization process description in the manuscript to clarify the methodology and results.

- Added new CSV files for optimization results of 2W and 3W configurations.
- Updated the `ml_surrogate_train.py` to change cross-validation strategy for sample sizes between 21 and 80.
- Modified `run_rbf.sh` to execute training scripts for iteration 1 instead of 0.
- Updated manuscript to reflect changes in design variables and optimization methodology.
- Added new references to the bibliography for Bayesian optimization.
- Included new PDF of the manuscript in the repository.

Enhance manuscript content by refining design of experiments section and adding new references for Latin Hypercube Sampling methodologies

- Revised the text to clarify the damage-aware surrogate-assisted optimization methodology, emphasizing the balance between dissipative performance and damage indicators.
- Changed the figure reference from "illustrates" to "summarizes" for improved clarity.
- Adjusted the figure size for better presentation in the manuscript.
- Updated the figure caption to accurately reflect the content of the flowchart.

- Introduced a new flow chart image (MethodologyFlowChart.png) to visually represent the adaptive surrogate-assisted optimization framework.
- Expanded the manuscript to include detailed descriptions of the proposed methodology, emphasizing the balance between energy dissipation and damage control.
- Added systematic comparisons of surrogate techniques and outlined the adaptive FEM validation and retraining strategy.

- Added new figure: EspecificacionesML_Final_eng.png.
- Updated author name from J. Gonzalez to J. M. Gonzalez in the manuscript.
- Modified WileyNJDv5.cls to conditionally load packages and provide commands if not found, improving compatibility and avoiding errors related to missing packages.

- Introduced a comprehensive manuscript detailing the optimization framework for buckling-delayed shear-link dampers.
- Included sections on introduction, device description, FEM calibration, surrogate modeling, optimization algorithm, and adaptive validation.
- Implemented a damage-aware objective function and outlined the methodology for generating high-fidelity FEM datasets.
- Discussed the predictive performance of supervised ML models and RBF surrogates, along with planned numerical assessments.
- Added author contributions, acknowledgments, financial disclosures, and conflict of interest statements.

- Reorganized import statements in `ml_surrogate_train.py`, `rbf_model.py`, `rbf_optimization_de.py`, `rbf_surrogate_train.py` for clarity.
- Simplified dictionary and list comprehensions for better readability.
- Updated print statements for consistent formatting.
- Enhanced function definitions with clearer parameter formatting.
- Added a new `pyproject.toml` file for Ruff configuration to enforce code style.
- Removed unnecessary comments and improved inline documentation.