TY - JOUR
T1 - Prediction of the peak load and crack initiation energy of dynamic brittle fracture in X70 steel pipes using an improved artificial neural network and extended Finite Element Method
AU - Oulad Brahim, Abdelmoumin
AU - Belaidi, Idir
AU - Fahem, Noureddine
AU - Khatir, Samir
AU - Mirjalili, Seyedali
AU - Abdel Wahab, Magd
N1 - Funding Information:
The authors acknowledge the financial support of LEMI Laboratory, Department of Mechanical Engineering, University M’hamed Bougara Boumerdes, Algeria. The authors wish to express their gratitude to Van Lang University, Vietnam for financial support for this research.
Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/12
Y1 - 2022/12
N2 - In this paper, a robust technique is presented to predict the peak load and crack initiation energy of dynamic brittle fracture in X70 steel pipes using an improved artificial neural network (IANN). The main objective is to investigate the behaviour of API X70 steel based on two experimental tests, namely Drop Weight Tear Test (DWTT) and the Charpy V-notch impact (CVN), for steel pipe specimens. The mechanical properties in the brittle fracture behaviour of API X70 steel pipes are predicted utilizing numerical approaches with different crack lengths. Next, to simulate the impact of API X70 steel pipes at lower temperatures through a numerical approach, a cohesive approach using the extended Finite Element Method (XFEM) is used. The data obtained are used as input for the proposed IANN using Balancing Composite Motion Optimization (BCMO), Particle Swarm Optimization (PSO) and Jaya optimization algorithms, to predict the peak load values and crack initiation energy of dynamic brittle fractures in API X70 steel with different crack lengths. The results show the effectiveness of ANN-PSO and ANN-BCMO based on the convergence of the results and the accuracy of the prediction of the peak load and crack initiation energy. Note that, the source codes are publicly available at https://github.com/Samir-Khatir/JAYA-ANN.git.
AB - In this paper, a robust technique is presented to predict the peak load and crack initiation energy of dynamic brittle fracture in X70 steel pipes using an improved artificial neural network (IANN). The main objective is to investigate the behaviour of API X70 steel based on two experimental tests, namely Drop Weight Tear Test (DWTT) and the Charpy V-notch impact (CVN), for steel pipe specimens. The mechanical properties in the brittle fracture behaviour of API X70 steel pipes are predicted utilizing numerical approaches with different crack lengths. Next, to simulate the impact of API X70 steel pipes at lower temperatures through a numerical approach, a cohesive approach using the extended Finite Element Method (XFEM) is used. The data obtained are used as input for the proposed IANN using Balancing Composite Motion Optimization (BCMO), Particle Swarm Optimization (PSO) and Jaya optimization algorithms, to predict the peak load values and crack initiation energy of dynamic brittle fractures in API X70 steel with different crack lengths. The results show the effectiveness of ANN-PSO and ANN-BCMO based on the convergence of the results and the accuracy of the prediction of the peak load and crack initiation energy. Note that, the source codes are publicly available at https://github.com/Samir-Khatir/JAYA-ANN.git.
KW - ANN-BCMO
KW - ANN-Jaya
KW - ANN-PSO
KW - API X70 steel
KW - Crack initiation energy
KW - CVN
KW - DWTT
UR - http://www.scopus.com/inward/record.url?scp=85141242768&partnerID=8YFLogxK
U2 - 10.1016/j.tafmec.2022.103627
DO - 10.1016/j.tafmec.2022.103627
M3 - Article
AN - SCOPUS:85141242768
SN - 0167-8442
VL - 122
JO - Theoretical and Applied Fracture Mechanics
JF - Theoretical and Applied Fracture Mechanics
M1 - 103627
ER -