Evaluating strength behavior and failure mode of ice materials based on large-scale database and machine learning algorithms

The strength characteristic and failure mode of ice materials are widely used to analyze the interaction between ice and structure to ensure the construction stability in ocean engineering and ice engineering. This study establishes a large-scale database (comprising 5542 testing samples) for the ice mechanics to study the influences of six effective features (grain size, density, porosity, salinity, temperature, and strain rate) on mechanical behaviors. The correlation degrees between effective features and strength behavior (failure mode) are investigated through correlation analysis. The Regression algorithm and Classification algorithm of four machine learning models are respectively used to evaluate and predict the strength and deformation behaviors of ice materials. Four evaluation parameters (R², RMSE, MAE, and MBE) are adopted to further investigate the predictive ability of those machine learning models. Based on the partial dependence interpretation, the contributions of effective features to strength prediction are quantitatively described. The results indicate that temperature is the most important factor for strength behavior. In the classification prediction for failure mode, the prediction accuracy for ductile behavior is enhanced in the Random Forest algorithm to improve the overall classification accuracy, and the Random Forest algorithm exhibits well performance compared to the other three algorithms.

• Record URL:
  • https://doi.org/10.1016/j.oceaneng.2024.119379
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0029801824027173
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00298018
• Supplemental Notes:
  • © 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. Abstract reprinted with permission of Elsevier.
• Authors:
  • Ai, Xihui
  • Li, Jiao
  • Zhou, Zhiwei
  • Ma, Wei
• Publication Date: 2024-12-1

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 119379
• Serial:
  • Ocean Engineering
  • Volume: 313
  • Issue Number: 0
  • Publisher: Pergamon
  • ISSN: 0029-8018
  • EISSN: 1873-5258
  • Serial URL: http://www.sciencedirect.com/science/journal/00298018

Subject/Index Terms

• TRT Terms: Failure; Machine learning; Ocean engineering; Predictive models; Sea ice; Strength of materials
• Subject Areas: Data and Information Technology; Marine Transportation;

Filing Info

• Accession Number: 01934273
• Record Type: Publication
• Files: TRIS
• Created Date: Oct 17 2024 11:01AM