PSI - Issue 48

Available online at www.sciencedirect.com Available online at www.sciencedirect.com ScienceDirect Structural Integrity Procedia 00 (2023) 000 – 000 Available online at www.sciencedirect.com ScienceDirect Structural Integrity Procedia 00 (2023) 000 – 000

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Procedia Structural Integrity 48 (2023) 356–362

© 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the IRAS 2023 organizers Abstract Appropriate reliability methods for estimating the failure probability using most efficient and robust properties are crucial in structural reliability analyses of engineering structures. In this paper, an enhanced first-order reliability method (FORM) using conjugate search direction was coupled with artificial intelligence technique called support vector regression (SVR) named as SVR CFORM for structural reliability analyses of complex limit state functions. The conjugate FORM (CFORM) is formulated using adaptive formulas to search the most probable point to improve the robustness of iterative FORM formula, whereas the SVR is used to predict the performance functions to improve the efficiency of the proposed reliability method. Several FORM formulas such as HL-RF, directional stability transformation method, and finite step length are used to compare the performance of the proposed SVR-CFORM in terms of both efficiency and robustness using different numerical/structural reliability examples. In comparison to other studied FORM formulas, the proposed SVR-CFORM method improves the iterative FORM formulation more efficiently and with more stable results. © 2023 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the IRAS 2023 organizers Keywords: Structural reliability; First-order reliability method; Support vector regression; Conjugate FORM; Failure probability. Abstract Appropriate reliability methods for estimating the failure probability using most efficient and robust properties are crucial in structural reliability analyses of engineering structures. In this paper, an enhanced first-order reliability method (FORM) using conjugate search direction was coupled with artificial intelligence technique called support vector regression (SVR) named as SVR CFORM for structural reliability analyses of complex limit state functions. The conjugate FORM (CFORM) is formulated using adaptive formulas to search the most probable point to improve the robustness of iterative FORM formula, whereas the SVR is used to predict the performance functions to improve the efficiency of the proposed reliability method. Several FORM formulas such as HL-RF, directional stability transformation method, and finite step length are used to compare the performance of the proposed SVR-CFORM in terms of both efficiency and robustness using different numerical/structural reliability examples. In comparison to other studied FORM formulas, the proposed SVR-CFORM method improves the iterative FORM formulation more efficiently and with more stable results. © 2023 The Authors. Published by ELSEVIER B.V. Keywords: Structural reliability; First-order reliability method; Support vector regression; Conjugate FORM; Failure probability. Second International Symposium on Risk Analysis and Safety of Complex Structures and Components (IRAS 2023) Structural reliability analysis using Conjugate FORM-based Support Vector Regression Mohamed El Amine Ben Seghier a,b, *, Behrooz Keshtegar c , José A.F.O. Correia d a Laboratory for Computational Mechanics, Institute for Computational Science and Artificial Intelligence, Van Lang University, Ho Chi Minh City, Vietnam b Faculty of Mechanical - Electrical and Computer Engineering, School of Technology, Van Lang University, Ho Chi Minh City, Vietnam Second International Symposium on Risk Analysis and Safety of Complex Structures and Components (IRAS 2023) Structural reliability analysis using Conjugate FORM-based Support Vector Regression Mohamed El Amine Ben Seghier a,b, *, Behrooz Keshtegar c , José A.F.O. Correia d a Laboratory for Computational Mechanics, Institute for Computational Science and Artificial Intelligence, Van Lang University, Ho Chi Minh City, Vietnam b Faculty of Mechanical - Electrical and Computer Engineering, School of Technology, Van Lang University, Ho Chi Minh City, Vietnam c Department of Civil Engineering, Faculty of Engineering, University of Zabol, P.B. 9861335856 Zabol, Iran d INEGI, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal. c Department of Civil Engineering, Faculty of Engineering, University of Zabol, P.B. 9861335856 Zabol, Iran d INEGI, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal.

* Corresponding author. Tel.: /; fax:/. E-mail address: benseghier@vlu.edu.vn * Corresponding author. Tel.: /; fax:/. E-mail address: benseghier@vlu.edu.vn

2452-3216 © 2023 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the IRAS 2023 organizers 2452-3216 © 2023 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the IRAS 2023 organizers

2452-3216 © 2023 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the IRAS 2023 organizers 10.1016/j.prostr.2023.07.121