Finite Element Model Updating of Lifeline Truss Bridge Using Vibration-Based Measurement Data and Balancing Composite Motion Optimization

Abstract

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.

A Systematic Review of Optimization Algorithms for Structural Health Monitoring and Optimal Sensor Placement

“…Additionally, a variety of other methodologies are also available for OSP, such as the following: energy-efficient sensor deployment [ 265 ], backup sensor-based fault-tolerance SHM method [ 266 ], mixed variable pattern search algorithm [ 267 ], frequency domain-based OSP technique [ 268 ], three-phase sensor placement approach [ 269 ], Gram–Schmidt orthogonalization procedure [ 270 ], e-Estimator algorithms [ 271 ], and wave propagation-based local interaction simulation approach [ 272 ]. SHM problems can also be analyzed using other computational methodologies, including the guided water strider algorithm [ 273 ], grasshopper optimization algorithm (GOA) [ 274 ], improved imperialist competitive algorithm [ 275 ], atom search algorithm (ASO) [ 276 ], equilibrium optimizer algorithm [ 277 ], grey wolf optimizer algorithm [ 278 ], balancing composite motion optimization [ 279 ], Q-learning evolutionary algorithm [ 280 ], Kriging-particle swarm optimization algorithm [ 281 ], and topology optimization [ 282 ]. More details of these methodologies can be found in the associated studies.…”