An innovative solution for determining stress intensity factors (SIF) using the GMDH neural network

The Stress Intensity Factor, SIF, is a conventional index to assess the elastic stress state at the tip of a mathematically sharp crack in fracture mechanics and also is an appropriate quantity for crack opening and fracture development in brittle materials. This paper presents a statistical solution based on the Group Method of Data Handling, GMDH, to determine the stress intensity fac-tors (KI). The results of this method are compared and validated with the previous analytical and numerical methods. In order to extract the SIFs, a certain number of tests were performed by changing the geometrical parameters, boundary and loading conditions using DOF (Design of Experiments) method. Each experiment is independently simulated in the Abaqus software and the values of stress intensity factors are calculated. The inputs of each test, including geometric dimensions, crack location, crack depth, boundary conditions and loading situation, along with the test output, are introduced to the GMDH algorithm. The best and most optimal connection between the inputs and outputs by considering the lowest error was set after processing the data in the algorithm. In this study, the stress intensity factors are calculated for beams under bending moment and transverse loading and plates containing two types of crack; single-edge crack and central crack. The accuracy of the presented statistical method is validated by J-integral method in Abaqus software. The comparison of the results from the current study with those obtained using existing the analytical and numerical methods are satisfied with less than 8.4% differences. It is known that there are limitations in the analytical method for crack depth (a/L>0.7). However, the statistical approach is strong enough to provide solutions for deep cracks. The main advantage of this method, based on the neural network, is its flexibility and versatility for any types of geometric, material property, loading and boundary conditions.

Authors

Institutions

Publication Details

Journal
Procedia Structural Integrity
Published
2025-01-01
DOI
https://doi.org/10.1016/j.prostr.2025.06.204
Citations
2
Primary Topic
Material Properties and Failure Mechanisms
Type
article
Field-Weighted Citation Impact
1.04
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

An innovative solution for determining stress intensity factors (SIF) using the GMDH neural network

Mohammad Zaman Kabir, Kamran Nikbin, Morteza Khomami Abadi
2 citations
Procedia Structural Integrity
Material Properties and Failure Mechanisms
1.04
article

An innovative solution for determining stress intensity factors (SIF) using the GMDH neural network

Mohammad Zaman Kabir, Kamran Nikbin, Morteza Khomami Abadi
article en
2 citations

Abstract

The Stress Intensity Factor, SIF, is a conventional index to assess the elastic stress state at the tip of a mathematically sharp crack in fracture mechanics and also is an appropriate quantity for crack opening and fracture development in brittle materials. This paper presents a statistical solution based on the Group Method of Data Handling, GMDH, to determine the stress intensity fac-tors (KI). The results of this method are compared and validated with the previous analytical and numerical methods. In order to extract the SIFs, a certain number of tests were performed by changing the geometrical parameters, boundary and loading conditions using DOF (Design of Experiments) method. Each experiment is independently simulated in the Abaqus software and the values of stress intensity factors are calculated. The inputs of each test, including geometric dimensions, crack location, crack depth, boundary conditions and loading situation, along with the test output, are introduced to the GMDH algorithm. The best and most optimal connection between the inputs and outputs by considering the lowest error was set after processing the data in the algorithm. In this study, the stress intensity factors are calculated for beams under bending moment and transverse loading and plates containing two types of crack; single-edge crack and central crack. The accuracy of the presented statistical method is validated by J-integral method in Abaqus software. The comparison of the results from the current study with those obtained using existing the analytical and numerical methods are satisfied with less than 8.4% differences. It is known that there are limitations in the analytical method for crack depth (a/L>0.7). However, the statistical approach is strong enough to provide solutions for deep cracks. The main advantage of this method, based on the neural network, is its flexibility and versatility for any types of geometric, material property, loading and boundary conditions.

Procedia Structural IntegrityVol. 68
Amirkabir University of Technology (IR), Imperial College London (GB)
Affordable and clean energy
Openalex Percentile: Top 24%
Material Properties and Failure Mechanisms
1.04
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.