Zhou Ji¹, Mengmeng Zhou², Qiang Wang², Jiandong Huang^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1557-1582, 2024, DOI:10.32604/cmes.2023.046025 - 29 January 2024

Abstract To improve the prediction accuracy of the International Roughness Index (IRI) of Jointed Plain Concrete Pavements (JPCP) and Continuously Reinforced Concrete Pavements (CRCP), a machine learning approach is developed in this study for the modelling, combining an improved Beetle Antennae Search (MBAS) algorithm and Random Forest (RF) model. The 10-fold cross-validation was applied to verify the reliability and accuracy of the model proposed in this study. The importance scores of all input variables on the IRI of JPCP and CRCP were analysed as well. The results by the comparative analysis showed the prediction accuracy of… More > Graphic Abstract

Chun-Ying. Wu¹, R. Al-Khoury², C. Kasbergen², Xue-Yan. Liu², A. Scarpas²

CMES-Computer Modeling in Engineering & Sciences, Vol.17, No.3, pp. 163-172, 2007, DOI:10.3970/cmes.2007.017.163

Abstract 3D spectral element method in the article is presented to predict the pavement layer modules using field measurement of Falling Weight Deflectometer (FWD). To improve the computational efficiency of the layer-condition assessment, one type of spectral element is proposed to develop the dynamic analysis of 3D multi-layered system subjected to an impulsive load. Each layer in structure is simulated as two-noded layer spectral element or one-noded spectral element in frequency domain. In order to identify the parameters of layered structures, a nonlinear optimization method called Powell hybrid algorithm is employed. The optimization procedure is performed More >