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333604

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

Article

Last updated: 28 Dec 2024

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Tags

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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.

DOI

10.21608/svusrc.2023.250899.1164

Keywords

Temperature, Rainfall, Concrete Pavement, ARIMA, Climate Change

Authors

First Name

Debo Brata Paul

Last Name

Argha

MiddleName

-

Affiliation

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

Email

arghapaul7148@gmail.com

City

San Marcos

Orcid

0000-0001-7645-770X

First Name

Md Ashik

Last Name

Ahmed

MiddleName

-

Affiliation

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

Email

mahmed1@aggies.ncat.edu

City

-

Orcid

0000-0001-6023-1028

Volume

5

Article Issue

1

Related Issue

43456

Issue Date

2024-06-01

Receive Date

2023-11-24

Publish Date

2024-06-01

Page Start

150

Page End

155

Print ISSN

2785-9967

Online ISSN

2735-4571

Link

https://svusrc.journals.ekb.eg/article_333604.html

Detail API

https://svusrc.journals.ekb.eg/service?article_code=333604

Order

333,604

Type

Original research articles

Type Code

1,585

Publication Type

Journal

Publication Title

SVU-International Journal of Engineering Sciences and Applications

Publication Link

https://svusrc.journals.ekb.eg/

MainTitle

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

Details

Type

Article

Created At

28 Dec 2024