Statistical Elastodynamic Inversion for Pavement Nondestructive Evaluation

路面无损评价的统计弹动力反演

• 批准号: 0408390
• 负责人: Lu Sun
• 金额: --
• 依托单位: Catholic University of America
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-06-01 至 2008-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0408390&HistoricalAwards=false
• 关键词: Statistical; Elastodynamic; Inversion; Pavement; Nondestructive

AbstractNon-destructive evaluation (NDE) has generated great interest in assessing structural capacity of road, highway and airfield pavements. While considerable effort has been devoted to static analysis based inversion, little has been done to investigate inverse problems based on dynamic analysis while taking uncertainty into account. The proposed research will establish a rigorous and unified theoretical framework for pavement NDE that integrates statistical science, optimization theory, and computer simulation, and will develop an elastodynamic inverse analysis software toolkit for practical engineering applications. By using the entire time history of forward dynamic analysis in inversion, the proposed research will provide more stable and more accurate results. By formulating unknown parameter reconstruction using maximum likelihood estimate and its robust counterpart within the framework of Bayesian decision theory and Markov Chain Monte Carlo simulation, the research promises to bring tremendous benefits. These include allowing uncertainty to be treated naturally, allowing the effect of measurement noise and outliers to be mitigated, allowing engineers' experience to be incorporated as a priori information, and enabling the accuracy and reliability of reconstructed parameters to be quantitatively assessed using the provided parameter uncertainty. By investigating a variety of classical and non-classical optimizations algorithm and artificial neural network in the context of NDE in both the time and the transformed domain, the research promises to develop efficient, accurate and robust optimization methods for the implementation of the project, and to retrieve much richer information of unknown physical properties from measured pavement responses.The proposed methodological framework can be generally applicable to similar types of NDE, thereby adding new scientific knowledge to other fields. The project will generate cost-effective, fast and reliable pavement NDE techniques, which will result in significant economic benefits. As an inter-disciplinary research effort, the project will provide students cross-disciplinary exposure and unique experience in analytical, computational and experimental work.

摘要无损评估（NDE）在评估道路、高速公路和机场路面的结构能力方面引起了极大的兴趣。虽然人们在基于静态分析的反演方面付出了相当大的努力，但在考虑不确定性的同时研究基于动态分析的反演问题却做得很少。该研究将建立一个严谨、统一的路面无损检测理论框架，集统计科学、优化理论和计算机模拟于一体，并开发适用于实际工程应用的弹动力反演分析软件工具包。通过在反演中使用正向动力分析的整个时程，所提出的研究将提供更稳定、更准确的结果。通过在贝叶斯决策理论和马尔可夫链蒙特卡罗模拟的框架内使用最大似然估计及其稳健对应物来制定未知参数重建，该研究有望带来巨大的好处。这些包括允许自然地处理不确定性，允许减轻测量噪声和异常值的影响，允许将工程师的经验作为先验信息纳入，以及使用提供的参数定量评估重建参数的准确性和可靠性不确定。通过在时间域和变换域中研究 NDE 背景下的各种经典和非经典优化算法和人工神经网络，该研究有望为项目的实施开发高效、准确和鲁棒的优化方法，并从测量的路面响应中检索未知物理特性的更丰富的信息。所提出的方法框架可以普遍适用于类似类型的无损检测，从而为其他领域增加新的科学知识。该项目将产生具有成本效益、快速且可靠的路面无损检测技术，从而产生显着的经济效益。作为一项跨学科研究工作，该项目将为学生提供跨学科的接触和分析、计算和实验工作的独特经验。