A comparative study of machine learning methods for predicting mode I and II brittle fracture in notched bend specimens.

Linfeng Li, Haytham F Isleem, Mohammad Khishe, Ghanshyam G Tejani

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 authors.

Linfeng LiDepartment of Civil Engineering, Changzhi Vocational and Technical College, Changzhi, 046000, Shanxi Province, China.

Haytham F IsleemSchool of intelligent Construction, Qingdao Hengxing University of Science and Technology, Qingdao 266100, Shandong Province, China. isleemhaytham88@gmail.com.

Mohammad KhisheDepartment of Electrical Engineering, Imam Khomeini Naval Science University of Nowshahr, Nowshahr, Iran. m_khishe@alumni.iust.ac.ir.

Ghanshyam G TejaniDepartment of Research Analytics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, 600077, India.

Funding

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6 · The paper itself

Abstract

This study evaluates the efficacy of statistical, artificial neural network (ANN), and deep learning (DL) methods in developing mathematical models for estimating dimensionless fracture parameters in single-edge notched bend (SENB) specimens under Mode I/II brittle fracture conditions. The research focuses on this specimen, a commonly used configuration in fracture mechanics studies. The primary objective is to benchmark the accuracy, computational efficiency, and generalization capabilities of various modelling approaches, including linear regression, polynomial regression, Random Forest Regression (RFR), and Bidirectional Long Short-Term Memory (BiLSTM) networks. The study leverages a comprehensive dataset encompassing various geometric configurations and loading conditions to develop predictive models for key fracture parameters such as Y

Indexed as

Deep learningMechanicsMode I/II fractureNeural modelRegression

Identifiers

PMID41107417

DOI10.1038/s41598-025-20349-3

PMCPMC12534656

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LicenceCC BY-NC-ND

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