A Machine Learning Approach to Understand the Impact of Temperature and Rainfall Change on Concrete Pavement Performance Based on LTPP Data

Argha, Debo Brata Paul; Ahmed, Md Ashik

doi:10.21608/svusrc.2023.250899.1164

A Machine Learning Approach to Understand the Impact of Temperature and Rainfall Change on Concrete Pavement Performance Based on LTPP Data

Document Type : Original research articles

Authors

¹ Ingram School of Engineering, Texas State University, San Marcos, Texas, 78666, USA

² Department of Nanoengineering, North Carolina Agricultural and Technical State University, Greensboro, NC 27405, USA

10.21608/svusrc.2023.250899.1164

Abstract

Climate change is one of the most concerning global issues and has the potential to influence every aspect of human life. Like different components of society, it can impose significant adverse impacts on pavement infrastructure. Although several research efforts have focused on studying the effects of climate change on natural and built systems, its impact on pavement performance has not been studied extensively. Due to the weather effect, the lifetime of pavement is getting shorter; on the other hand, maintenance costs are getting higher and higher. The data has been collected from the long-term pavement performance (LTPP) program website, and as a site, the State of Texas has been considered. The main goal of this project is to find out how changes in temperature and rainfall affect how pavement responds and how well it performs in the future using the ARIMA model and to create a logistic regression model to look at the forecast data.

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