PSI - Issue 52

ScienceDirect Available online at www.sciencedirect.com Structural Integrity Procedia 00 (2023) 000–000 Available online at www.sciencedirect.com Available online at www.sciencedirect.com Available online at www.sciencedirect.com Procedia Structural Integrity 52 (2024) 667–678 Structural Integrity Procedia 00 (2023) 000–000

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© 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 Professor Ferri Aliabadi Abstract Model-based methods have gained significant attention as an on-line tool for impact diagnosis in composite structures. These methods involve the development of a mathematical model that simulates impact dynamics and identifies impact characteristics by minimizing the di ff erences between predicted and actual responses. However, the associated optimisation process is often compu tationally demanding and time-consuming. Traditional heuristic algorithms like the genetic algorithm may necessitate thousands of impact simulations to achieve convergence. To address the challenge of computational e ffi ciency in model-based methods, this study introduces the surrogate-assisted e ffi cient global optimisation algorithm to solve the minimization problem for impact iden tification. The proposed approach leverages the e ffi cient global optimisation framework, which utilizes a Kriging meta-model to capture the relationships among impact location, impact force (design variables), and response di ff erences (objective function). By iteratively infill sampling using the generalized expected improvement criterion, a suitable balance between exploration and exploitation is maintained during the optimisation process. This ensures e ff ective search in the design space. Additionally, local surrogates for impact location and impact peak force are constructed to adaptively bound these design variables within feasible ranges. This bounding technique narrows down the search space and accelerates convergence. The validity of the proposed method is demonstrated on the finite element model of a composite plate using impact force history obtained from experiments as input excitation. The results illustrate that the proposed method accurately identifies impacts with fewer than 100 impact simulations, highlighting its e ffi ciency and e ff ectiveness. By leveraging surrogate models and e ffi cient search strategies, the proposed algorithm significantly reduces the computational burden while ensuring precise and reliable impact identification. Keywords: Impact Identification; E ffi cient Global Optimisation; Generalized Expected Improvement; Time of Arrivals; Surrogate Model Fracture, Damage and Structural Health Monitoring Impact Identification Based on Surrogate-assisted E ffi cient Global Optimisation Dong Xiao*, Zahra Sharif Khodaei, M H Ferri Aliabadi Department of Aeronautics, Imperial College London, South Kensington, London SW7 2AZ, UK Abstract Model-based methods have gained significant attention as an on-line tool for impact diagnosis in composite structures. These methods involve the development of a mathematical model that simulates impact dynamics and identifies impact characteristics by minimizing the di ff erences between predicted and actual responses. However, the associated optimisation process is often compu tationally demanding and time-consuming. Traditional heuristic algorithms like the genetic algorithm may necessitate thousands of impact simulations to achieve convergence. To address the challenge of computational e ffi ciency in model-based methods, this study introduces the surrogate-assisted e ffi cient global optimisation algorithm to solve the minimization problem for impact iden tification. The proposed approach leverages the e ffi cient global optimisation framework, which utilizes a Kriging meta-model to capture the relationships among impact location, impact force (design variables), and response di ff erences (objective function). By iteratively infill sampling using the generalized expected improvement criterion, a suitable balance between exploration and exploitation is maintained during the optimisation process. This ensures e ff ective search in the design space. Additionally, local surrogates for impact location and impact peak force are constructed to adaptively bound these design variables within feasible ranges. This bounding technique narrows down the search space and accelerates convergence. The validity of the proposed method is demonstrated on the finite element model of a composite plate using impact force history obtained from experiments as input excitation. The results illustrate that the proposed method accurately identifies impacts with fewer than 100 impact simulations, highlighting its e ffi ciency and e ff ectiveness. By leveraging surrogate models and e ffi cient search strategies, the proposed algorithm significantly reduces the computational burden while ensuring precise and reliable impact identification. Keywords: Impact Identification; E ffi cient Global Optimisation; Generalized Expected Improvement; Time of Arrivals; Surrogate Model Fracture, Damage and Structural Health Monitoring Impact Identification Based on Surrogate-assisted E ffi cient Global Optimisation Dong Xiao*, Zahra Sharif Khodaei, M H Ferri Aliabadi Department of Aeronautics, Imperial College London, South Kensington, London SW7 2AZ, UK Abstract Model-based methods have gained significant attention as an on-line tool for impact diagnosis in composite structures. These methods involve the development of a mathematical model that simulates impact dynamics and identifies impact characteristics by minimizing the di ff erences between predicted and actual responses. However, the associated optimisation process is often compu tationally demanding and time-consuming. Traditional heuristic algorithms like the genetic algorithm may necessitate thousands of impact simulations to achieve convergence. To address the challenge of computational e ffi ciency in model-based methods, this study introduces the surrogate-assisted e ffi cient global optimisation algorithm to solve the minimization problem for impact iden tification. The proposed approach leverages the e ffi cient global optimisation framework, which utilizes a Kriging meta-model to capture the relationships among impact location, impact force (design variables), and response di ff erences (objective function). By iteratively infill sampling using the generalized expected improvement criterion, a suitable balance between exploration and exploitation is maintained during the optimisation process. This ensures e ff ective search in the design space. Additionally, local surrogates for impact location and impact peak force are constructed to adaptively bound these design variables within feasible ranges. This bounding technique narrows down the search space and accelerates convergence. The validity of the proposed method is demonstrated on the finite element model of a composite plate using impact force history obtained from experiments as input excitation. The results illustrate that the proposed method accurately identifies impacts with fewer than 100 impact simulations, highlighting its e ffi ciency and e ff ectiveness. By leveraging surrogate models and e ffi cient search strategies, the proposed algorithm significantly reduces the computational burden while ensuring precise and reliable impact identification. Keywords: Impact Identification; E ffi cient Global Optimisation; Generalized Expected Improvement; Time of Arrivals; Surrogate Model Fracture, Damage and Structural Health Monitoring Impact Identification Based on Surrogate-assisted E ffi cient Global Optimisation Dong Xiao*, Zahra Sharif Khodaei, M H Ferri Aliabadi Department of Aeronautics, Imperial College London, South Kensington, London SW7 2AZ, UK Structural Integrity Procedia 00 (2023) 000–000 www.elsevier.com / locate / procedia

1. Introduction 1. Introduction

Advancements in computing power have opened up possibilities for real-time identification of impacts using recorded impact responses from passive sensors. This on-line impact identification method relies on a mathematical model that simulates impact dynamics. Through an iterative process of minimizing the disparities between predicted Advancements in computing power have opened up possibilities for real-time identification of impacts using recorded impact responses from passive sensors. This on-line impact identification method relies on a mathematical model that simulates impact dynamics. Through an iterative process of minimizing the disparities between predicted Advancements in computing power have opened up possibilities for real-time identification of impacts using recorded impact responses from passive sensors. This on-line impact identification method relies on a mathematical model that simulates impact dynamics. Through an iterative process of minimizing the disparities between predicted ∗ Corresponding author. E-mail address: d.xiao21@imperial.ac.uk 1. Introduction

∗ Corresponding author. E-mail address: d.xiao21@imperial.ac.uk ∗ Corresponding author. E-mail address: d.xiao21@imperial.ac.uk

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 Professor Ferri Aliabadi 10.1016/j.prostr.2023.12.067 2210-7843 © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http: // creativecommons.org / licenses / by-nc-nd / 4.0 / ) Peer-review under responsibility of Professor Ferri Aliabadi. 2210-7843 © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http: // creativecommons.org / licenses / by-nc-nd / 4.0 / ) Peer-review under responsibility of Professor Ferri Aliabadi. 2210-7843 © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http: // creativecommons.org / licenses / by-nc-nd / 4.0 / ) Peer-review under responsibility of Professor Ferri Aliabadi.