journal article·2023-07Open access

Vibration-based approach for structural health monitoring of ultra-high-performance concrete bridge

Siti Shahirah Saidin, Sakhiah Abdul Kudus, Adiza Jamadin, Muhamad Azhan Anuar, Norliyati Mohd Amin, Atikah Bt Zakaria Ya, Kunitomo Sugiura

Abstract

Structural Health Monitoring (SHM) is a valuable tool for keeping track of the performance and health of engineering structures over extended periods. The purpose of SHM is to ensure structural safety by relying on information derived from real-time measured data and offer suitable recommendations for the structure's maintenance and management. The fundamental idea of SHM is that the structure's response to vibrations will change noticeably if its mass or stiffness is altered. It is a way to keep tabs on how well a building keeps itself safe and sound with as little human intervention as possible. Throughout this research, a Finite Element (FE) model was updated, and an ambient vibration test was performed on an Ultra-High-Performance Concrete (UHPC) bridge, given its slimmer as well as lighter properties to get modal parameters, including natural frequency and mode shape. Measured modal parameters were acquired and verified with the aid of several Operation Modal Analysis (OMA) techniques, and the FE model has then modified accordingly. The bridge's condition was evaluated and predicted using a mathematical method called simple linear regression, which considered the variations in natural frequency produced by the structure's mass and stiffness. The findings indicate that the stiffness index ratio is a practical and efficient tool for evaluating the bridge's effectiveness. The applied method demonstrated the Sungai Raia bridge's safe functioning in real-world situations.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Structural Health Monitoring Techniques · Concrete Corrosion and Durability · Structural Engineering and Vibration Analysis

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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