PSI - Issue 47

ScienceDirect 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 Available online at www.sciencedirect.com

www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia

Procedia Structural Integrity 47 (2023) 932–937

27th International Conference on Fracture and Structural Integrity (IGF27) Optimized Linear Regression to Compute the Weibull Parameters 27th International Conference on Fracture and Structural Integrity (IGF27) Optimized Linear Regression to Compute the Weibull Parameters

Mohammed Lamine Moussaoui * * * Faculty of Mechanical and Process Engineering, University of Sciences and Technology Houari Boumediene, B.P.32 El Alia, Bab Ezzouar, Algiers 16111, Algeria Mohammed Lamine Moussaoui * * * Faculty of Mechanical and Process Engineering, University of Sciences and Technology Houari Boumediene, B.P.32 El Alia, Bab Ezzouar, Algiers 16111, Algeria

© 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 IGF27 chairpersons Abstract An efficient novel formulation for the computation of the and parameters is introduced. This procedure consists to numerically optimize the errors between the data values and the corresponding regression function values using the least squares method. A numerical example is presented to compare the obtained results and their efficiency in precision and computing time. © 2023 The Author. 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 IGF27 chairpersons Keywords: Weibull parameters; Probabilistic Law; Linear regression; Optimization; Least squares method. 1. Introduction Since several years from the past, it is well known that parameters have been graphically obtained from the plot on the as it is shown in Fig. 1 Bellaouar and Beleulmi � 2013 � . This method requires a meticulous representation and consumes a lot of time. It depends on the data values, their precision and their graphical representation. We introduce in this paper a novel numerical formulation to compute the and parameters. This procedure consists to optimize the errors between the data values and the corresponding regression function values Abstract An efficient novel formulation for the computation of the and parameters is introduced. This procedure consists to numerically optimize the errors between the data values and the corresponding regression function values using the least squares method. A numerical example is presented to compare the obtained results and their efficiency in precision and computing time. © 2023 The Author. 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 IGF27 chairpersons Keywords: Weibull parameters; Probabilistic Law; Linear regression; Optimization; Least squares method. 1. Introduction Since several years from the past, it is well known that parameters have been graphically obtained from the plot on the as it is shown in Fig. 1 Bellaouar and Beleulmi � 2013 � . This method requires a meticulous representation and consumes a lot of time. It depends on the data values, their precision and their graphical representation. We introduce in this paper a novel numerical formulation to compute the and parameters. This procedure consists to optimize the errors between the data values and the corresponding regression function values

* Corresponding author. E-mail address: mohammedlamine.moussaoui@usthb.edu.dz * Corresponding author. E-mail address: mohammedlamine.moussaoui@usthb.edu.dz

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 IGF27 Costanzo Bellini 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 IGF27 Costanzo Bellini

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 IGF27 chairpersons 10.1016/j.prostr.2023.07.024