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引用次数: 0
摘要
本文采用最大似然估计(MLE)方法来校准柔性路面的自下而上开裂、总车辙和国际粗糙度指数(IRI)传递函数。它使用了四种分布:伽马分布、指数分布、负二项分布和对数正态分布,并将结果与 LS 方法进行了比较。首先,生成了自下而上开裂的合成数据,以证明 MLE 比 LS 方法更有效。最后,使用 MDOT PMS 数据库中 256 个新柔性路面的测量数据来校准和验证传递函数。重采样方法与 MLE 相结合,以提高其稳健性。结果表明,在合成数据和测量数据方面,MLE 总体上优于 LS 方法。这种差异在自下而上开裂数据中更为明显,因为这种数据不服从正态分布。对于 MLE 方法来说,自下而上开裂和总车辙的伽马分布以及 IRI 的负二项分布是最合适的分布。
Local Calibration of Flexible Performance Models Using Maximum Likelihood Estimation Approach
This paper uses maximum likelihood estimation (MLE) approach to calibrate bottom-up cracking, total rutting, and international roughness index (IRI) transfer function for flexible pavements. It used four distributions: gamma, exponential, negative binomial, and log-normal, and results are compared with the LS approach. Initially, synthetic data is generated for bottom-up cracking to demonstrate the effectiveness of MLE over the LS approach. Finally, measured data for two hundred and fifty-six new flexible pavements is used from MDOT’s PMS database to calibrate and validate transfer functions. Resampling methods are combined with MLE to improve its robustness. The results show that overall, MLE outperforms the LS approach for synthetic and measured data. The difference is more evident in the case of bottom-up cracking data, which does not follow a normal distribution. Gamma distribution for bottom-up cracking and total rutting, whereas negative binomial for IRI is the most suitable distribution for the MLE approach.
期刊介绍:
The Canadian Journal of Civil Engineering is the official journal of the Canadian Society for Civil Engineering. It contains articles on environmental engineering, hydrotechnical engineering, structural engineering, construction engineering, engineering mechanics, engineering materials, and history of civil engineering. Contributors include recognized researchers and practitioners in industry, government, and academia. New developments in engineering design and construction are also featured.