Located in the heart of Hanoi (Vietnam), Chuong Duong bridge is a major truss bridge that connects one of the most heavily trafficked routes in the countrythe 1A National road. Being built in the 80s of the twentieth century, after nearly 40 years of service, degradation and damages have threatened the integrity and safety of the structure. Physical and numerical evaluation of the bridge is required for the maintenance process. In this paper, we proposed a new approach to model updating of Chuong Duong bridge using vibration-based measurement data and Balancing Composite Motion Optimization (BCMO). BCMO is a newly developed metaheuristic optimization algorithm based on individual's balancing composite motion properties which has proved to provide highly-accurate result in determining the optimal solution in mathematical problem. BCMO is applied to update the different parameters of the baseline numerical model of Chuong Duong bridge, followed by comparing the obtained dynamic properties of the updated bridge with the measured one. The final result shows that BCMO has comprehensively updated the model with a high level of accuracy, thus could be potential used to solve practical problems of lifeline structures.
Optimization algorithms (OAs) are a vital tool to deal with complex problems, and the improvement of OA is inseparable from practical strategies and mechanisms. Among the OAs, Bee Algorithm (BA) is an intelligent algorithm with a simple mechanism and easy implementation, in which effectiveness has been proven when handling optimization problems. Nevertheless, BA still has some fundamental drawbacks, which can hinder its effectiveness and accuracy. Therefore, this paper proposes a novel approach to tackle the shortcomings of BA by combining it with Genetic Algorithm (GA). The main intention is to combine the strengths of both optimization techniques, which are the exploitative search ability of BA and the robustness with the crossover and mutation capacity of GA. An investigation of a real-life suspension footbridge is considered to validate the effectiveness of the proposed method. A baseline Finite Element model of the bridge is constructed based on vibration measurement data and model updating, which is used to generate different hypothetical damage scenarios. The proposed HBGA is tested against BA, GA, and PSO to showcase its effectiveness in detecting damage for each scenario. The results show that the proposed algorithm is effective in dealing with the damage assessment problems of SHM.
During their lifecycle, bridge structures have to withstand various uncertainties loads such as wind, typhoon, accident loads, etc, which may pose serious threats to the integrity as well as the safety of the structure, especially when they induced significant damages to the structure. For many years, researchers have been trying to develop heath monitoring tools, which can identify accurately not only the location, but also the level of structural damage. In this paper, two novel avian-based optimization algorithms-Artificial Hummingbird Algorithm (AHA) and African Vulture Optimization Algorithm (AVOA) are reviewed for their feasibility in detecting structural damages in truss bridge. The accuracy of the proposed algorithms is compared against two other famous algorithms: Particle Swarm Optimization (PSO) and Cuckoo Search (CS). The results of the feasibility review for damage detection capability are discussed.
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