Abstract
This paper presents an improved artificial neural network to predict the damage percentage in the test sample. The main objective is to show that the presence of holes and more generally of notches and other connection gaps lead to a weakening of the structure due to local overstresses, called stress concentrations. It is therefore good to avoid them, as much as possible. When the presence of stress concentrators is inevitable, it is necessary to know the stress concentration factor associated with each geometry, a notion introduced in this problem, in order to dimension the structures. Large holes result in high stress concentration factors. This behavior clearly shows that the presence of holes in a specimen is a place of stress concentration which can lead to the initiation and propagation of cracks. In this study, using an improved artificial neural network (ANN) model, we aim to predict the damage percentage in test samples with higher accuracy and reliability. This improved ANN integrates state-of-the-art algorithms (Arithmetic Optimization Algorithm-AOA, Balancing Composition Motion Optimization-BCMO and Jaya Algorithm), refined training methodologies and an extensive dataset of stress values of different sizes to ensure a more comprehensive and robust understanding of damage prediction, thereby contributing to more accurate assessments of the structural integrity and reliability of tested samples.
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References
Bouledroua, O., et al.: Effect of sandblasting on tensile properties, hardness and fracture resistance of a line pipe steel used in Algeria for oil transport. J. Fail. Anal. Prev. 17(5), 890–904 (2017)
Kang, J.-Y., et al.: Limit strains of X70 pipes with a semi-elliptical crack based on initiation and ductile tearing criteria. In: ASME 2018 Pressure Vessels and Piping Conference (2018)
Li, D., et al.: Fracture analysis of marble specimens with a hole under uniaxial compression by digital image correlation. Eng. Fract. Mech. 183, 109–124 (2017)
Seifi, R., Googarchin, H.S., Farrokhi, M.: Buckling of cracked cylindrical panels under axially compressive and tensile loads. Thin-Walled Struct. 94, 457–465 (2015)
Sampath, D., Akid, R., Morana, R.: Estimation of crack initiation stress and local fracture toughness of Ni-alloys 945X (UNS N09946) and 718 (UNS N07718) under hydrogen environment via fracture surface topography analysis. Eng. Fract. Mech. 191, 324–343 (2018)
Steinke, C., Kaliske, M.: A phase-field crack model based on directional stress decomposition. Comput. Mech. 63(5), 1019–1046 (2019)
Mohtadi-Bonab, M.A., et al.: Effect of different microstructural parameters on hydrogen induced cracking in an API X70 pipeline steel. Met. Mater. Int. 23(4), 726–735 (2017)
Sharma, L., Chhibber, R.: Mechanical properties and hydrogen induced cracking behaviour of API X70 SAW weldments. Int. J. Press. Vessels Pip. 165, 193–207 (2018)
Kim, J., et al.: Yield strength estimation of X65 and X70 steel pipe with relatively low t/D ratio. Steel Compos. Struct. 38(2), 151–164 (2021)
Sun, F., et al.: Comparative study on the stress corrosion cracking of X70 pipeline steel in simulated shallow and deep sea environments. Mater. Sci. Eng. A 685, 145–153 (2017)
Wang, S., Xu, M.: Modal strain energy-based structural damage identification: a review and comparative study. Struct. Eng. Int. 29(2), 234–248 (2019)
Kang, L., Suzuki, M., Ge, H.: A study on application of high strength steel SM570 in bridge piers with stiffened box section under cyclic loading. Steel Compos. Struct. 26(5), 583–594 (2018)
Zhao, Y., et al.: Effects of microstructure on crack resistance and low-temperature toughness of ultra-low carbon high strength steel. Int. J. Plast. 116, 203–215 (2019)
Dabiri, M., et al.: Neural network-based assessment of the stress concentration factor in a T-welded joint. J. Constr. Steel Res. 128, 567–578 (2017)
Yang, Y., et al.: Study of the design and mechanical performance of a GFRP-concrete composite deck. Steel Compos. Struct. 24(6), 679–688 (2017)
Khechai, A., et al.: Numerical analysis of stress concentration in isotropic and laminated plates with inclined elliptical holes. J. Inst. Eng. (India) Ser. C 100(3), 511–522 (2019)
Divse, V., Marla, D., Joshi, S.S.: Finite element analysis of tensile notched strength of composite laminates. Compos. Struct. 255, 112880 (2021)
Nassiraei, H., Rezadoost, P.: Stress concentration factors in tubular T/Y-connections reinforced with FRP under in-plane bending load. Mar. Struct. 76, 102871 (2021)
Bobyr’, N.I., Koval’, V.V.: Damage contribution to the assessment of the stress-strain state of structure elements. Strength Mater. 49(3), 361–368 (2017)
Jain, N.K., Banerjee, M., Sanyal, S.: Three dimensional analysis for effect of fibre orientation on stress concentration factor in fibrous composite plates with central circular hole subjected to in-plane static loading. In: 2013 7th International Conference on Intelligent Systems and Control (ISCO) (2013)
Yang, J.-F., et al.: Stress concentration factors test of reinforced concrete-filled tubular Y-joints under in-plane bending. Steel Compos. Struct. 22(1), 203–216 (2016)
Khatir, A., et al.: A new hybrid PSO-YUKI for double cracks identification in CFRP cantilever beam. Compos. Struct. 311, 116803 (2023)
Oulad Brahim, A., et al.: 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. Theor. Appl. Fract. Mech. 122, 103627 (2022)
Fahem, N., et al.: Prediction of resisting force and tensile load reduction in GFRP composite materials using artificial neural network-enhanced Jaya algorithm. Compos. Struct. 304, 116326 (2023)
Ouladbrahim, A., et al.: Experimental crack identification of API X70 steel pipeline using improved artificial neural networks based on whale optimization algorithm. Mech. Mater. 166, 104200 (2022)
Ouladbrahim, A., et al.: Prediction of Gurson damage model parameters coupled with hardening law identification of steel X70 pipeline using neural network. Met. Mater. Int. 28(2), 370–384 (2022)
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Oulad Brahim, A., Capozucca, R., Magagnini, E., Khatir, B., Khatir, A. (2024). Predict Damage Percentage in Test Specimens Using Improved Artificial Neural Network. In: Benaissa, B., Capozucca, R., Khatir, S., Milani, G. (eds) Proceedings of the International Conference of Steel and Composite for Engineering Structures. ICSCES 2023. Lecture Notes in Civil Engineering, vol 486. Springer, Cham. https://doi.org/10.1007/978-3-031-57224-1_11
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DOI: https://doi.org/10.1007/978-3-031-57224-1_11
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