Geometry-Aware Crack Identification in Rectangular Cantilever Beam Families Using Frequency-Shift and Curvature-Damage Peak Features: Scripts, Data, and Results

Description

This dataset provides the MATLAB scripts, numerical data, trained regression models, intermediate computational outputs, and evaluation results supporting the study entitled “Geometry-Aware Crack Identification in Rectangular Cantilever Beam Families Using Frequency-Shift and Curvature-Damage Peak Features.” The dataset is organized into three compressed archives: 1. Geometry_Aware_Crack_Identification_Scripts.zip This archive contains the MATLAB scripts used to generate the beam-family data, extract modal features, compute curvature-damage indicators, prepare regression data, train the crack position and depth regression models, and perform the in-grid, noise, off-grid, out-of-family, Abaqus, and experimental evaluations. 2. Geometry_Aware_Crack_Identification_Data_and_Results.zip This archive contains the primary numerical data, intermediate processing outputs, trained model files, and results generated by the main computational workflow. It includes the data and outputs required to reproduce the reported analyses. 3. Geometry_Aware_Crack_Identification_Multi_Seed_Evaluation.zip This archive contains supplementary files generated using three different random seeds. These files are provided to assess the stability of the training procedure under different random initializations. The computational workflow uses three vibration modes and was implemented in MATLAB R2024b. Parallel execution requires the Parallel Computing Toolbox. The scripts may require updating user-specific file paths when transferred to another computer. The archives are provided as supporting research materials for reproducibility and transparency. The primary reported results correspond to the main workflow archive, while the multi-seed archive provides supplementary stability analysis.

Steps to reproduce

1. Download and extract the three ZIP archives. 2. Install MATLAB R2024b or a later version with the Parallel Computing Toolbox. 3. Update the user-specific file paths in the MATLAB scripts when necessary. 4. Run the scripts in the documented workflow order. 5. Use the primary data and results archive to inspect the intermediate outputs, trained regression models, and evaluation results. 6. Use the multi-seed evaluation archive to examine the stability of the training procedure under different random seeds.

Institutions

• University of Anbar

  Al Anbar, Ramadi

Categories

Licence