- Introduced new markdown file R1_closed_changes_batch6C_Yao_Yu.md detailing the integration of Yao 2023 and Yu 2026 references into the manuscript.
- Added references to the bibliography for Yao 2023 and Yu 2026, including their respective citation contexts in the manuscript.
- Created R1_closed_changes_batch6_references.md to document the assessment of additional reviewer-suggested references, noting which were added and which were excluded with rationale.
- Added Reviewer_reference_Yao_Yu_audit.md to provide a focused audit on the Yao and Yu references, detailing their relevance and recommendations.
- Updated Reviewer_reference_audit.md to reflect the overall assessment of 20 suggested references, categorizing them based on relevance and necessity for the manuscript.

- Added multiple PDF files for reviewers in the R2, R3, and R4 directories.
- Included an Excel file with recommended references for reviewers.

Batch 3:
- Addressed reviewer comments on RBF training cost, motivation chain, novelty statement, component-level scope, seismic loading scope, resilience discussion, conclusions restructuring, consistency audit, and references.
- Clarified scope and deferred certain analyses while ensuring all changes are documented and tracked.

Batch 4:
- Reconstructed optimization formulation and clarified damage constraints.
- Introduced a hierarchical optimization approach with a focus on feasibility and shear-distortion performance.
- Updated manuscript sections, figures, and addressed reviewer comments regarding methodology and terminology.

- Conducted an audit of the original stability implementation in `evaluate_adaptive_convergence()`, addressing critical issues and enhancing the API for better stability reporting.
- Refactored `adaptive_acceptance.py` to include explicit return fields for stability metrics and removed redundant function definitions.
- Added 6 new regression tests to `test_new_optimization_logic.py`, increasing total test coverage to 44.
- Updated reporting mechanisms in `adaptive_acceptance.py` to improve clarity and accuracy of stability and performance metrics.
- Introduced a three-tier reporting taxonomy for failed criteria and enhanced adaptive stability reporting with percentage fields.
- Regenerated output files and ensured all tests pass successfully.

- Conducted an audit of the original stability implementation in `evaluate_adaptive_convergence()`, addressing critical issues and enhancing the API for better stability reporting.
- Refactored `adaptive_acceptance.py` to include explicit return fields for stability metrics and removed redundant function definitions.
- Added 6 new regression tests to `test_new_optimization_logic.py`, increasing total test coverage to 44.
- Updated reporting mechanisms in `adaptive_acceptance.py` to improve clarity and accuracy of stability and performance metrics.
- Introduced a three-tier reporting taxonomy for failed criteria and enhanced adaptive stability reporting with percentage fields.
- Regenerated output files and ensured all tests pass successfully.

- Introduced adaptive convergence constants in `adaptive_acceptance.py` to improve convergence evaluation.
- Added centralized adaptive convergence evaluation function to streamline convergence checks.
- Updated acceptance criteria to include new convergence metrics and ensure backward compatibility.
- Enhanced diagnostic messages for better clarity on convergence failures.
- Expanded robustness study configurations in `de_robustness_study.py` to include a new population size option (P100).

- Implemented `compute_oof_error_envelope` to calculate absolute prediction-error envelopes and safety margins for adaptive surrogate validation.
- Added `build_local_oof_error_table` to create a one-row-per-sample OOF residual table for local safety margins.
- Updated `run_nested_cv_rbf_for_target` to utilize the new functions and return validation envelopes and local OOF error data.
- Enhanced `main` function to save validation envelopes and local OOF errors to CSV files.
- Modified shell scripts to adjust parameters for training and optimization.
- Introduced unit tests for new optimization logic, including safety margin calculations and local OOF error table construction.

- Introduced safety margin modes: local OOF, global OOF, and none, with corresponding parameters.
- Added validation for safety margin factors and local margin settings.
- Updated lexicographic optimization to incorporate safety margins in TFD predictions.
- Enhanced output to include detailed diagnostics for local and global safety margins.
- Modified stage 2 to maximize distortion while preserving stage 1 results.
- Improved DE reproducibility diagnostics and reporting.
- Updated grid verification to reflect new safety margin considerations.

- Updated script to generate SHAP beeswarm plots for both the RBF objective and target surrogates.
- Refactored model loading functions for clarity and efficiency.
- Added functionality to skip objective plots via command line argument.
- Improved error handling for missing model files and data.
- Streamlined SHAP value computation and output saving processes.

Refine cross-validation strategy descriptions and improve manuscript clarity in the methodology section

Improve manuscript clarity and formatting in the section on buckling-delayed shear-link dampers. Adjust phrasing for consistency and enhance readability in various sections.

Remove the manuscript PDF for the adaptive FEM-validated surrogate optimization of buckling-delayed shear-link dampers.

Fix typos and improve clarity in the manuscript on surrogate optimization for optimal design layout. Update variable names for consistency and correct formatting in equations. Update binary file for synchronization.

- 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.