A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Vibration-Based Damage Detection of Arch Dams Using Least-Square Support Vector Machines and Salp Swarm Algorithms
This paper presents a vibration-based damage-detection approach for arch dams using least-square support vector machines and salp swarm algorithms (SSAs). Least-square support vector regression is used to establish a surrogate model representing the relationship between the dynamic elastic modulus and modal parameters (natural frequency and mode shape). The SSA is applied for dynamic parameter identification by minimizing an objective function composed of vibration data. To verify the performance of the proposed method, we consider a hyperbolic concrete arch dam as a numerical example. Furthermore, the SSA is compared with several other population-based global optimization algorithms. Results show that the proposed approach can significantly improve the damage identification efficiency without compromising accuracy. The SSA is promising for solving dynamic parameter identification problems.
Vibration-Based Damage Detection of Arch Dams Using Least-Square Support Vector Machines and Salp Swarm Algorithms
This paper presents a vibration-based damage-detection approach for arch dams using least-square support vector machines and salp swarm algorithms (SSAs). Least-square support vector regression is used to establish a surrogate model representing the relationship between the dynamic elastic modulus and modal parameters (natural frequency and mode shape). The SSA is applied for dynamic parameter identification by minimizing an objective function composed of vibration data. To verify the performance of the proposed method, we consider a hyperbolic concrete arch dam as a numerical example. Furthermore, the SSA is compared with several other population-based global optimization algorithms. Results show that the proposed approach can significantly improve the damage identification efficiency without compromising accuracy. The SSA is promising for solving dynamic parameter identification problems.
Vibration-Based Damage Detection of Arch Dams Using Least-Square Support Vector Machines and Salp Swarm Algorithms
Iran J Sci Technol Trans Civ Eng
Zar, Ali (author) / Kang, Fei (author) / Li, Junjie (author) / Wu, Yingrui (author)
2022-12-01
22 pages
Article (Journal)
Electronic Resource
English
| Engineering Index Backfile | 1963
| Engineering Index Backfile | 1930
| Springer Verlag | 2015