Application and feasibility analysis of knowledge-based machine learning in predicting fatigue performance of stainless steel
To better predict the fatigue-related S-N curves of different series of stainless steels, 570 sets of data including fatigue test results and other performance parameters for five common types of stainless steel materials were initially collected. Eight machine learning models were deployed and anal...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-07-01
|
| Series: | Case Studies in Construction Materials |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214509524012427 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850063238597705728 |
|---|---|
| author | Jia Wang Dongkui Fan C.S. Cai |
| author_facet | Jia Wang Dongkui Fan C.S. Cai |
| author_sort | Jia Wang |
| collection | DOAJ |
| description | To better predict the fatigue-related S-N curves of different series of stainless steels, 570 sets of data including fatigue test results and other performance parameters for five common types of stainless steel materials were initially collected. Eight machine learning models were deployed and analyzed using the dataset, and their predictive performances were evaluated using assessment metrics. Based on the best-performing model, corresponding S-N curves were constructed. The SHapley Additive exPlanations (SHAP) method was then applied to the optimal model to comprehensively describe and analyze the influence mechanisms of various factors on the number of cycles than stainless steel could withstand. Finally, the results predicted by the optimal model were compared with multiple design standards to verify the feasibility and effectiveness of the model in predicting the S-N curves of stainless steel materials. The results show that a genetic algorithm–optimized artificial neural network (GA-ANN) model possesses higher prediction accuracy than other models, with a correlation coefficient R2 of 0.98 and prediction data within a twofold error margin. The feature parameter constructed through feature engineering has the most significant impact on the number of cycles. The fatigue-related S-N curves predicted by the machine learning model can satisfy the requirements of design standards, demonstrating the feasibility of using this model to predict the fatigue property of stainless steel materials. |
| format | Article |
| id | doaj-art-8180d1450b0440f08d21d4a3ea35f85c |
| institution | DOAJ |
| issn | 2214-5095 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Construction Materials |
| spelling | doaj-art-8180d1450b0440f08d21d4a3ea35f85c2025-08-20T02:49:41ZengElsevierCase Studies in Construction Materials2214-50952025-07-0122e0409010.1016/j.cscm.2024.e04090Application and feasibility analysis of knowledge-based machine learning in predicting fatigue performance of stainless steelJia Wang0Dongkui Fan1C.S. Cai2Department of Bridge Engineering, School of Transportation, Southeast University, Nanjing 211189 ChinaCorresponding author.; Department of Bridge Engineering, School of Transportation, Southeast University, Nanjing 211189 ChinaDepartment of Bridge Engineering, School of Transportation, Southeast University, Nanjing 211189 ChinaTo better predict the fatigue-related S-N curves of different series of stainless steels, 570 sets of data including fatigue test results and other performance parameters for five common types of stainless steel materials were initially collected. Eight machine learning models were deployed and analyzed using the dataset, and their predictive performances were evaluated using assessment metrics. Based on the best-performing model, corresponding S-N curves were constructed. The SHapley Additive exPlanations (SHAP) method was then applied to the optimal model to comprehensively describe and analyze the influence mechanisms of various factors on the number of cycles than stainless steel could withstand. Finally, the results predicted by the optimal model were compared with multiple design standards to verify the feasibility and effectiveness of the model in predicting the S-N curves of stainless steel materials. The results show that a genetic algorithm–optimized artificial neural network (GA-ANN) model possesses higher prediction accuracy than other models, with a correlation coefficient R2 of 0.98 and prediction data within a twofold error margin. The feature parameter constructed through feature engineering has the most significant impact on the number of cycles. The fatigue-related S-N curves predicted by the machine learning model can satisfy the requirements of design standards, demonstrating the feasibility of using this model to predict the fatigue property of stainless steel materials.http://www.sciencedirect.com/science/article/pii/S2214509524012427Stainless steelNumber of cyclesS-N curveMachine learningPhysical modelSHAP |
| spellingShingle | Jia Wang Dongkui Fan C.S. Cai Application and feasibility analysis of knowledge-based machine learning in predicting fatigue performance of stainless steel Case Studies in Construction Materials Stainless steel Number of cycles S-N curve Machine learning Physical model SHAP |
| title | Application and feasibility analysis of knowledge-based machine learning in predicting fatigue performance of stainless steel |
| title_full | Application and feasibility analysis of knowledge-based machine learning in predicting fatigue performance of stainless steel |
| title_fullStr | Application and feasibility analysis of knowledge-based machine learning in predicting fatigue performance of stainless steel |
| title_full_unstemmed | Application and feasibility analysis of knowledge-based machine learning in predicting fatigue performance of stainless steel |
| title_short | Application and feasibility analysis of knowledge-based machine learning in predicting fatigue performance of stainless steel |
| title_sort | application and feasibility analysis of knowledge based machine learning in predicting fatigue performance of stainless steel |
| topic | Stainless steel Number of cycles S-N curve Machine learning Physical model SHAP |
| url | http://www.sciencedirect.com/science/article/pii/S2214509524012427 |
| work_keys_str_mv | AT jiawang applicationandfeasibilityanalysisofknowledgebasedmachinelearninginpredictingfatigueperformanceofstainlesssteel AT dongkuifan applicationandfeasibilityanalysisofknowledgebasedmachinelearninginpredictingfatigueperformanceofstainlesssteel AT cscai applicationandfeasibilityanalysisofknowledgebasedmachinelearninginpredictingfatigueperformanceofstainlesssteel |