SSA is a new OA proposed in 2017 18 that has recently appeared and immediately gained a lot of momentum. Afterward, the updated model possibly predicts the structural behaviour accurately. In the SHM field, OAs assist in reducing the deviations between the Finite Element Model (FEM) and measurements. Over the last decades, numerous OAs have been proposed and successfully applied for a wide range of fields 15, 16, 17. This ambient excitation source is possibly generated randomly at a low cost and does not interfere with the flow of traffic on the bridge 14. Ambient excitation can be produced by wind, micro-seismic, or by passing vehicles. On top of that, the lowest natural frequencies of large-scale structures are usually outside the frequency band of maximum artificial excitation. However, this approach is only suitable for small structures since it is challenging to generate responses large enough to capture the dynamic characteristics of large-scale structures 13. Artificial excitation can be accomplished using an artificial excitation source such as a hammer or a shaker. Experimental measurements can be conducted under ambient and/or artificial excitation. The performance of the modal identification measurements is essential to build a reliable model for assessing structural health 12. Therefore, dynamic behaviour-based methods are more sensitive to detecting damages occurring in the structures 11. While the former employs static responses such as stress, strain, or displacement to assess the structural health condition, the latter relies on dynamic responses such as natural frequencies, mode shapes, or damping ratio. SHM is mainly based on two main methods: (1) static behaviour-based method and (2) dynamic behaviour-based method 9, 10. The task of SHM systems is to monitor early damages based on measurement data to evaluate the severity of these damages before making timely repair decisions 6, 7, 8. Therefore, in recent decades, SHM systems have been widely deployed and captured special attention from the scientific community. This mechanical resonance may put bridges in potential danger. In addition, bridges also have their own vibration patterns that possibly cause amplified vibrations when the natural frequencies of the bridges coincide with those of moving vehicles. Similar content being viewed by othersÄuring service life, bridges are easily subjected to various damages due to natural impacts (storms, floods, earthquakes, etc.) or human-induced impacts (overload, collision, etc.) 1, 2, 3, 4, 5. The obtained results show that the proposed approach is highly potential to apply for SHM of real large-scale structures. Moreover, in this work, the vectorization technique is also applied to reduce the size of the data. 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. 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). Therefore, this article aims at addressing these two aforementioned issues. In addition to the requirement of high accuracy, a high computational cost is putting up a major barrier to the real application of OAs. However, the major concern is how to employ OAs to deal with real-world problems, especially the SHM of large-scale structures. During recent decades, numerous metaheuristic intelligent OAs have been proposed and immediately gained a lot of momentum. 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.
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