Model Updating for Nam O Bridge Using Particle Swarm Optimization Algorithm and Genetic Algorithm.

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ID: 17891
2018
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Abstract
Vibration-based structural health monitoring (SHM) for long-span bridges has become a dominant research topic in recent years. The Nam O Railway Bridge is a large-scale steel truss bridge located on the unique main rail track from the north to the south of Vietnam. An extensive vibration measurement campaign and model updating are extremely necessary to build a reliable model for health condition assessment and operational safety management of the bridge. The experimental measurements are carried out under ambient vibrations using piezoelectric sensors, and a finite element (FE) model is created in MATLAB to represent the physical behavior of the structure. By model updating, the discrepancies between the experimental and the numerical results are minimized. For the success of the model updating, the efficiency of the optimization algorithm is essential. Particle swarm optimization (PSO) algorithm and genetic algorithm (GA) are employed to update the unknown model parameters. The result shows that PSO not only provides a better accuracy between the numerical model and measurements, but also reduces the computational cost compared to GA. This study focuses on the stiffness conditions of typical joints of truss structures. According to the results, the assumption of semi-rigid joints (using rotational springs) can most accurately represent the dynamic characteristics of the truss bridge considered.
Reference Key
tranngoc2018modelsensors Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Tran-Ngoc, H;Khatir, S;De Roeck, G;Bui-Tien, T;Nguyen-Ngoc, L;Abdel Wahab, M;
Journal sensors
Year 2018
DOI
E4131
URL
https://www.mdpi.com/1424-8220/18/12/4131
Keywords
RFID sensor networks lora iots cloud-based bim prefabrication components

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