报告题目：Calibration of Mechanistic-Empirical Pavement Design Performance Models Using Network-Level Field Data
（利用路网级现场观测数据校准力学经验法路面设计性能模型）
报 告 人：吴荣宗博士（美国加州大学路面研究中心，研究员）
邀 请 人：张丽娟
报告时间：2022年9月23日上午9:00—10:00
报告地点：交通大楼410会议室（线下）
腾讯会议：782 5561 6605（线上）
欢迎广大师生踊跃参加。
土木与交通学院
2022年9月22日
报告人简介：
Dr. Rongzong Wu received his Ph.D. in civil engineering from the University of California at Berkeley in 2005 and has been working as a researcher at the University of California Pavement Research Center ever since. His main research interests involve modeling, testing, and design of flexible pavements. He is currently focusing on the mechanistic-empirical design of flexible pavements.
吴荣宗博士于2005年获得加州大学伯克利分校土木工程博士学位，此后在加州大学路面研究中心担任研究员至今。他的主要研究兴趣包括柔性路面的建模、测试和设计等方面。他当前专注于柔性路面力学-经验法设计的研究。
报告内容：
At the core of a mechanistic-empirical (M-E) pavement design method is a collection of performance models that each predicts the development of a specific pavement distress, such as fatigue cracking and surface rutting. Each model has both mechanistic and empirical parts. The empirical parts need to be calibrated to remove bias and increase prediction accuracy. This process has traditionally been conducted with small numbers of field sections for which materials may or may not have been sampled and tested. In this seminar, the speaker presents a new calibration approach that uses network-level field data and state-wide distributions of material properties, without having to sample and test every individual calibration section. The calibration of the fatigue and reflection cracking models for CalME, the M-E design software developed for the California Department of Transportation, is used as an example to illustrate the new approach. The new approach works by correlating the statistical distributions of M-E design inputs with the statistical distribution of pavement performance, both at the network level. The results showed that the new approach can overcome some of the network-level data limitations and provides a reasonable calibration ready for routine pavement design.
一个力学—经验（M-E）路面设计方法的核心是其包括的各种性能预测模型。每个性能模型用来预测一种路面病害的发展。这些病害具体可以是疲劳开裂，表面车辙等等。每种性能模型都包括力学部分和经验部分。其中经验部分需要校准以消除偏差并提高预测精度。传统上，经验部分是利用少量现场路段的观测数据进行校准，而且常常会包括对现场路段所使用的材料进行取样和测试。在本次研讨会上，报告人将介绍一种新的校准方法。该方法使用路网级现场观测数据和代表全网的路面材料数据来进行校准，而不需要对每个单独的校准路段进行采样和测试。本报告用为加州交通厅开发的M-E设计软件CalME中的疲劳和反射裂缝模型为例来展示这种新的校准方法。该方法在路网层面上建立M-E设计的输入数据和路面实测性能的统计分布之间的相关关系。结果表明，这个新方法可以克服一些路网级数据的不足，为常规路面设计提供合理的校准。