Integrating Machine Learning for Improved Prediction of Temperature and Moisture in Pavement Granular Layers

Author:

Huang Yunyan1,Molavi Nojumi Mohamad1,Hashemian Leila1,Bayat Alireza2ORCID

Affiliation:

1. Department of Civil and Environmental Engineering, University of Alberta 1 , 116 St. and 85 Ave., Edmonton, Alberta, T6G 1H9, Canada , https://orcid.org/0000-0002-1710-029X (L.H.)

2. Department of Civil and Environmental Engineering, University of Alberta 2 , 116 St. and 85 Ave., Edmonton, Alberta, T6G 1H9, Canada (Corresponding author), e-mail: abayat@ualberta.ca , ORCID link for author moved to before name tags https://orcid.org/0000-0002-0094-1850

Abstract

Abstract Pavement temperature and moisture content within the base and subgrade layers affect the load-bearing capacity of the pavement and dominate the pavement performance in cold regions. Accurately predicting pavement temperature and moisture content can improve pavement design and management. Conventional approaches, including numerical and statistical models, have been implemented to predict pavement temperature and soil moisture content. However, they have weaknesses, such as being only suitable for warm regions or only for predicting pavement temperature within the asphalt layer. Furthermore, none of them can simultaneously predict the pavement temperature and moisture content. To address this issue, data collected from an instrumented test road in Alberta, Canada, were used to train a model to predict the daily average pavement temperature and moisture content at various depths through three parameters, namely depth, day of the year, and air temperature. The MATLAB toolbox, Neural Net Fitting, was used, and the performance of three built-in algorithms, Levenberg–Marquardt, Bayesian regularization, and Scaled conjugate gradient backpropagation, was compared. The model with Bayesian regularization showed the highest accuracy, with an R2 value of 0.99 and a root mean square error (RMSE) of 1.49°C for pavement temperature prediction, and an R2 value of 0.95 and an RMSE of 0.025 m3/m3 for moisture content prediction. The model developed in this research is the first to simultaneously estimate pavement temperature and moisture content, so its performance was separately compared with two existing models in the literature. The artificial neural network (ANN) model shows higher accuracy than the two existing models, so it was found that the ANN could be a robust method for pavement temperature and moisture content prediction at various depths.

Publisher

ASTM International

Reference49 articles.

1. Smith B. S. , Design and Construction of Pavements in Cold Regions: State of the Practice (master’s thesis, Brigham Young University, 2006).

2. Climate Impacts and Adaptations on Roads in Northern Canada;Tighe,2006

3. Guide for the Mechanistic-Empirical Design of New and Rehabilitated Pavement Structures Materials Characterization: Is Your Agency Ready;Olidis,2004

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