Real-time monitoring of residual strength in corroding steel reinforcement using ultrasonic-guided waves and multi-physics modelling

材料科学 腐蚀 抗弯强度 钢筋 结构工程 超声波传感器 屈曲 结构健康监测 复合材料 还原(数学) 声学 工程类 物理 几何学 数学
作者
Shanmukha Shetty,Sauvik Banerjee,Siddharth Tallur,Yogesh M. Desai
出处
期刊:Journal of Adhesion Science and Technology [Taylor & Francis]
卷期号:37 (20): 2841-2860 被引量:5
标识
DOI:10.1080/01694243.2022.2159292
摘要

Corrosion-induced material strength degradation is a major threat to the service life of reinforced concrete (RC) structures. Continued mass loss and pit formation after the initiation of corrosion in reinforcing steel consequently results in strength degradation. In this study, a non-destructive structural health monitoring (SHM) approach using ultrasonic guided waves with multi-physics modelling to monitor and forecast strength degradation of corroding steel is presented. Surface-bonded piezoelectric wafer transducers (PWTs) are used for actuating and sensing of guided waves in order to correlate the group velocity and amplitude of flexural wave modes with the strength reduction. The major findings of the work are presented in the form of empirical relationships between guided wave characteristics with reduced yield, ultimate, and buckling strength of corroded steel. It is observed that for a rebar of 12 mm diameter having slenderness ratio between 8 and 10, a 10% increase in amplitude and 8.44% increase in group velocity of flexural mode for corroding steel indicates a corresponding reduction of 10.278% in yield strength, 10.277% in ultimate strength, and 4.45% in buckling strength in progression phase of corrosion. A multi-physics numerical model of corrosion in RC structures that considers the effect of local exposure and environment is then elucidated with deviation less than 6.34% from experimental results in order to forecast the mass loss. The presented data fusion strategy, which combines guided wave-based monitoring and multi-physics modelling finds valuable application in assessing the current state of degradation in structural performance and strength-based service life prediction.
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