01763618

Component

https://annualmeeting.myt...nlineProgram/Details/15791

Regression and machine learning-based |E*| models have previously been proposed for use in asphalt design procedures. Regression models include the linear, interactions linear, stepwise linear, robust linear, Hirsch, revised Hirsch, Al-Khateeb 1&2, NCHRP 1-40D, simplified global, and Bari-Witczak models. The advantage of these models is that the output of the regression is a closed-form equation which is relatively easy to implement. However, all the aforementioned models showed a significant bias in prediction when the dynamic modulus database was large and included unique mixtures such as those containing RAP. There was not one regression model that produced an R²>0.9 on testing on such a database. To address this issue, several machine learning-based |E*| models were developed using the following algorithms: genetic expression programming (GEP), regression trees, SVMs, GPRs, ensembles of trees, ANFIS, and artificial neural networks (ANNs). Generally, machine learning-based models had a better performance than regression models, especially considering that a significant portion of the test database included mixtures containing RAP. The issue to date with machine learning models is that to most engineers, they appear to be a black box with no ability to create practical equations or be implemented in a spreadsheet. In this paper, a step-by-step process is shown to allow a practicing engineer to directly implement a complicated ANN model using a spreadsheet software.

This paper was sponsored by TRB committee AKM40 Standing Committee on Asphalt Mixture Evaluation and Performance.

TRBAM-21-03326

English

0000-0003-0863-2224

17p

2021

Transportation Research Board 100th Annual Meeting

Digital/other

Figures; References; Tables

Asphalt mixtures; Dynamic modulus of elasticity; Machine learning; Mathematical models; Reclaimed asphalt pavements; Recycled materials; Regression analysis; Spreadsheets

Environment; Highways; Materials; Pavements

Transportation Research Board Annual Meeting 2021 Paper #TRBAM-21-03326

TRIS, TRB, ATRI

Dec 23 2020 11:07AM