External post-tensioning tendons are key structural elements in bridge engineering, offering several construction advantages, but they are also vulnerable to corrosion and fatigue. Thus, vibration-based condition assessment techniques provide complementary non-destructive tests (NDT) to traditional inspection methods and other NDT techniques such as ultrasonic wave-based testing. Hence, this paper discusses on the practical usefulness and current challenges of vibration-based NDT of post-tensioning tendons (focused on taut external tendons, in particular, in grouted tendons) reviewing the following topics: i) bonded and unbonded tendons, ii) corrosion problems and their consequences, iii) the main challenges regarding vibration-based monitoring, iv) tension force estimation procedures and main considerations to take into account, iv) role of machine learning-based methods for clustering applications and regression models for anomaly detection and thermal correlation, and vi) numerical modelling for risk assessment and its practical application. Through the paper, practical experiences on in-field realizations are presented, focussing on the main challenges that require further research.
External post-tensioning tendons are key structural elements in bridge engineering, offering several construction advantages, but they are also vulnerable to corrosion and fatigue. Thus, vibration-based condition assessment techniques provide complementary non-destructive tests (NDT) to traditional inspection methods and other NDT techniques such as ultrasonic wave-based testing. Hence, this paper discusses on the practical usefulness and current challenges of vibration-based NDT of post-tensioning tendons (focused on taut external tendons, in particular, in grouted tendons) reviewing the following topics: i) bonded and unbonded tendons, ii) corrosion problems and their consequences, iii) the main challenges regarding vibration-based monitoring, iv) tension force estimation procedures and main considerations to take into account, iv) role of machine learning-based methods for clustering applications and regression models for anomaly detection and thermal correlation, and vi) numerical modelling for risk assessment and its practical application. Through the paper, practical experiences on in-field realizations are presented, focussing on the main challenges that require further research. Read More
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