Accurately predicting dynamic modulus of asphalt mixtures in low-temperature regions using hybrid artificial intelligence model

By Jiandong Huang, G. Shiva Kumar, Jiaolong Ren, Junfei Zhang, Yuantian Sun. 2021

46 citations recorded by the source corpus.

Abstract The applicability of the widely used Witczak model was evaluated for the first time regarding the asphalt mixtures produced in the East part of China. 16 asphalt mixtures by 2 binders and 8 aggregate gradations were designed and the dynamic modulus testing was performed under various loading temperatures and frequencies. The results obtained are similar to previous studies from different countries (areas): the dynamic modulus of the asphalt mixture in the low-temperature region is overestimated. So then, a hybrid artificial intelligence algorithm to replace the traditional Witcazak model is implemented in this paper to correct the overestimation, using the same input parameters in the Witczak model. A modified beetle antennae search (BAS) algorithm was proposed in this study to improve the searching efficiency in the random forest (RF) model. The calculation process showed fast convergence and higher efficiency. The comparative results between the predicted and actual dynamic modulus showed higher prediction accuracy at all the temperature and frequency ranges, overcoming the weaknesses of the traditional Witczak model. The variable importance results showed that G* and phase angle of the binders have the greatest impact on the dynamic modulus. Volumetric properties also show certain influence ability, but the change of the variable controlling aggregate gradation has a weak influence on the dynamic modulus.

Subfields: Civil and Structural Engineering

View Paper: https://www.semanticscholar.org/paper/b858fb4fb2ce7dd871ecdb06d7d6f95b89077433

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Hongxia Ma
2. Jiandong Liu
3. Jia Zhang
4. Jiandong Huang

Citations in the corpus

• Estimating the Compressive Strength of Cement-Based Materials with Mining Waste Using Support Vector Machine, Decision Tree, and Random Forest Models (2021, methodology): “Using artificial intelligence methods to evaluate and predict the mechanical properties of cement-based materials is a hot spot in the research field of cement-based composites [35, 39, 63].”