马尔科夫蒙特卡洛
贝叶斯概率
蒙特卡罗方法
空隙(复合材料)
贝叶斯推理
缩放比例
厚板
灰浆
大都会-黑斯廷斯算法
计算机科学
概率密度函数
核密度估计
算法
数学
统计
结构工程
工程类
材料科学
人工智能
几何学
复合材料
估计员
标识
DOI:10.1177/14759217231166117
摘要
This paper investigates the feasibility and practicability study on the use of Markov chain Monte Carlo (MCMC)-based Bayesian approach for identifying the cement-emulsified asphalt (CA) void of the slab track system utilizing the measured vibration data. A newly developed model class identification algorithm was extended and integrated with the MCMC-based Bayesian approach for the first time to identify the CA mortar void that may partly extend to a neighborhood region. Not only the most probable values of the scaling factors to the mortar stiffness can be calculated, but also the damage probability of model parameters using the posterior probability density function (PDF) can be estimated, and the void can be clearly identified by the MCMC-based Bayesian approach. The proposed methodology was experimentally verified and positive outcomes were obtained. The detection results illustrate that the proposed method not only can successfully assess the void location of the CA mortar but also provide the information of the damage severity, and the posterior PDFs of model parameters can be also calculated by using kernel density estimation to quantitatively describe the uncertainty of the model.
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