A promising approach using Fibonacci sequence-based optimization algorithms and advanced computing

H Tran-Ngoc; T Le-Xuan; S Khatir; G De Roeck; T Bui-Tien; Magd Abdel Wahab

doi:10.1038/s41598-023-28367-9

A promising approach using Fibonacci sequence-based optimization algorithms and advanced computing

Sci Rep. 2023 Feb 28;13(1):3405. doi: 10.1038/s41598-023-28367-9.

Authors

H Tran-Ngoc¹, T Le-Xuan¹, S Khatir², G De Roeck³, T Bui-Tien¹, Magd Abdel Wahab⁴

Affiliations

¹ Department of Bridge and Tunnel Engineering, Faculty of Civil Engineering, University of Transport and Communications, Hanoi, Vietnam.
² Faculty of Civil Engineering, Ho Chi Minh City Open University, Ho Chi Minh City, Vietnam.
³ Department of Civil Engineering, KU Leuven, 3001, Leuven, Belgium.
⁴ Soete Laboratory, Department of Electrical Energy, Metals, Mechanical Constructions, and Systems, Faculty of Engineering and Architecture, Ghent University, 9000, Gent, Belgium. magd.abdelwahab@ugent.be.

Abstract

In this paper, the feasibility of Structural Health Monitoring (SHM) employing a novel Fibonacy Sequence (FS)-based Optimization Algorithms (OAs) and up-to-date computing techniques is investigated for a large-scale railway bridge. During recent decades, numerous metaheuristic intelligent OAs have been proposed and immediately gained a lot of momentum. However, the major concern is how to employ OAs to deal with real-world problems, especially the SHM of large-scale structures. In addition to the requirement of high accuracy, a high computational cost is putting up a major barrier to the real application of OAs. Therefore, this article aims at addressing these two aforementioned issues. First, we propose employing the optimal ability of the golden ratio formulated by the well-known FS to remedy the shortcomings and improve the accuracy of OAs, specifically, a recently proposed new algorithm, namely Salp Swarm Algorithm (SSA). On the other hand, to deal with the high computational cost problems of OAs, we propose employing an up-to-date computing technique, termed superscalar processor to conduct a series of iterations in parallel. Moreover, in this work, the vectorization technique is also applied to reduce the size of the data. The obtained results show that the proposed approach is highly potential to apply for SHM of real large-scale structures.