This paper presents a novel approach to mitigating the effect of temperature variations on the bridges’ dynamic modal properties for more reliably detecting scour damage around bridge piles based on the vibration-based measurements. The novelty of the presented approach lies in its ability to reasonably remove the impacts on the modal properties of bridges, particularly caused by changes in material properties and structural boundary conditions due to temperature variations without explicitly modeling these complex effects. The main idea is to adopt the probabilistic-based machine learning method, Gaussian Process Model, to learn the correlation between the changes of modal properties of a monitored bridge and the corresponding temperature variations from in situ sensor measurements, and probabilistically infer the bridge scour based on the modified vibration measurements, which have mitigated the identified impacts of temperature variations, by applying Bayesian inference through the Transitional Markov Chain Monte Carlo simulation. The proposed approach and its applicability are presented and validated through the numerical simulation of a prototype bridge, demonstrating its potential for practical application for mitigating effects of temperature variations or other environmental impacts for vibration-based Structural Health Monitoring. The limitation of the presented study and future research needs are also discussed.

本文提出了一种新颖的方法来减轻温度变化对桥梁动力模态特性的影响，以便基于基于振动的测量结果更可靠地检测桥桩周围的冲刷破坏。提出的方法的新颖性在于它能够合理地消除对桥梁的模态特性的影响，特别是由于温度变化引起的材料特性和结构边界条件的变化而引起的，而无需对这些复杂的影响进行建模。主要思想是采用基于概率的机器学习方法，即高斯过程模型，以通过现场传感器测量来了解被监控桥梁的模态特性变化与相应温度变化之间的相关性，并根据修改后的振动测量结果来概率推断桥梁冲刷情况，该方法通过通过过渡马尔可夫链蒙特卡罗模拟应用贝叶斯推断，减轻了已确定的温度变化影响。通过对原型桥梁的数值模拟，提出并验证了所提出的方法及其适用性，证明了其在减轻基于振动的结构健康监测的温度变化或其他环境影响方面的实际应用潜力。还讨论了本研究的局限性和未来的研究需求。通过对原型桥梁的数值模拟，提出并验证了所提出的方法及其适用性，证明了其在减轻基于振动的结构健康监测的温度变化或其他环境影响方面的实际应用潜力。还讨论了本研究的局限性和未来的研究需求。通过对原型桥梁的数值模拟，提出并验证了所提出的方法及其适用性，证明了其在减轻基于振动的结构健康监测的温度变化或其他环境影响方面的实际应用潜力。还讨论了本研究的局限性和未来的研究需求。