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| Format: | Recurso digital |
| Sprache: | Englisch |
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2026
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| Online-Zugang: | https://doi.org/10.5281/zenodo.19795706 |
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| author | Bayazit, Ali Mert |
| author_facet | Bayazit, Ali Mert |
| contents | <p class="font-claude-response-body break-words whitespace-normal leading-[1.7]">This Zenodo record contains the source code and dataset accompanying the M.Sc. thesis "Multi-Target Machine Learning for Stiffened Panel Stress Prediction", completed at Tallinn University of Technology in 2026.</p> <p class="font-claude-response-body break-words whitespace-normal leading-[1.7]">The framework includes an automated FEMAP–NX Nastran pipeline for generating a parametric finite element dataset of 1000 stiffened panel configurations using Latin Hypercube Sampling. It also includes a machine learning benchmarking suite evaluating nine regression architectures: Linear Regression, Decision Tree, Random Forest, Extra Trees, AdaBoost, Gradient Boosting, Hist-Gradient Boosting, XGBoost and LightGBM. The models are trained across five dataset sizes and twenty independent random seeds.</p> <p class="font-claude-response-body break-words whitespace-normal leading-[1.7]">The study uses a multi-target regression formulation that predicts plate and stiffener stresses as separate outputs, preserving the distinct mechanical behaviour of the two structural components. Tree-based ensemble models achieved high agreement with the finite element results, with Extra Trees reaching an average R² of 0.9991. The trained surrogate enables orders-of-magnitude faster stress prediction compared with repeated direct finite element analysis, with single-configuration inference in approximately 28.6 ms and batch inference of 10,000 designs in approximately 92 ms.</p> <p class="font-claude-response-body break-words whitespace-normal leading-[1.7]">The record also includes the Pearson correlation analysis code used to examine the relationship between input parameters, analytical scaling terms and FEM-derived stress outputs, and the inference benchmark script used to quantify the computational speedup against direct finite element analysis.</p> <p>Contents:</p> <ul> <li>FEMAP_data_generation.py</li> <li>FEMAPautomationv7_FULL_LOCATION</li> <li>ml_benchmark.py</li> <li>pearson_correlation_analysis.py</li> <li>inference_benchmark.py</li> <li>merge_datasets.PY</li> <li>STIFFENER_MULTI_TARGET_DATA.csv</li> <li>ML_BENCHMARK_DETAILED.csv</li> <li>ML_BENCHMARK_SUMMARY.csv</li> <li>STIFFENER_MASTER_DATASET.csv</li> </ul> <p>Supervisor: Mihkel Kõrgesaar, Tallinn University of Technology <br>Co-supervisor: Muhammed Adil Yatkın, Tallinn University of Technology</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19795706 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Multi-Target Machine Learning for Stiffened Panel Stress Prediction: Source Code and Dataset Bayazit, Ali Mert Machine Learning surrogate modelling stiffened panel finite element analysis multi-target regression Latin Hypercube Sampling marine structures Extra Trees tabular data FEMAP ship structural analysis <p class="font-claude-response-body break-words whitespace-normal leading-[1.7]">This Zenodo record contains the source code and dataset accompanying the M.Sc. thesis "Multi-Target Machine Learning for Stiffened Panel Stress Prediction", completed at Tallinn University of Technology in 2026.</p> <p class="font-claude-response-body break-words whitespace-normal leading-[1.7]">The framework includes an automated FEMAP–NX Nastran pipeline for generating a parametric finite element dataset of 1000 stiffened panel configurations using Latin Hypercube Sampling. It also includes a machine learning benchmarking suite evaluating nine regression architectures: Linear Regression, Decision Tree, Random Forest, Extra Trees, AdaBoost, Gradient Boosting, Hist-Gradient Boosting, XGBoost and LightGBM. The models are trained across five dataset sizes and twenty independent random seeds.</p> <p class="font-claude-response-body break-words whitespace-normal leading-[1.7]">The study uses a multi-target regression formulation that predicts plate and stiffener stresses as separate outputs, preserving the distinct mechanical behaviour of the two structural components. Tree-based ensemble models achieved high agreement with the finite element results, with Extra Trees reaching an average R² of 0.9991. The trained surrogate enables orders-of-magnitude faster stress prediction compared with repeated direct finite element analysis, with single-configuration inference in approximately 28.6 ms and batch inference of 10,000 designs in approximately 92 ms.</p> <p class="font-claude-response-body break-words whitespace-normal leading-[1.7]">The record also includes the Pearson correlation analysis code used to examine the relationship between input parameters, analytical scaling terms and FEM-derived stress outputs, and the inference benchmark script used to quantify the computational speedup against direct finite element analysis.</p> <p>Contents:</p> <ul> <li>FEMAP_data_generation.py</li> <li>FEMAPautomationv7_FULL_LOCATION</li> <li>ml_benchmark.py</li> <li>pearson_correlation_analysis.py</li> <li>inference_benchmark.py</li> <li>merge_datasets.PY</li> <li>STIFFENER_MULTI_TARGET_DATA.csv</li> <li>ML_BENCHMARK_DETAILED.csv</li> <li>ML_BENCHMARK_SUMMARY.csv</li> <li>STIFFENER_MASTER_DATASET.csv</li> </ul> <p>Supervisor: Mihkel Kõrgesaar, Tallinn University of Technology <br>Co-supervisor: Muhammed Adil Yatkın, Tallinn University of Technology</p> |
| title | Multi-Target Machine Learning for Stiffened Panel Stress Prediction: Source Code and Dataset |
| topic | Machine Learning surrogate modelling stiffened panel finite element analysis multi-target regression Latin Hypercube Sampling marine structures Extra Trees tabular data FEMAP ship structural analysis |
| url | https://doi.org/10.5281/zenodo.19795706 |