International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 07 | July 2024
p-ISSN: 2395-0072
www.irjet.net
Development of Pavement Performance Prediction Models Using Regression Methods Nazmus Sakib Ahmed Graduate Research Assistant, Iowa State University ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - This research focuses on developing pavement
along the wheel paths caused by the accumulation of permanent deformation in the pavement layers. Roughness, on the other hand, refers to the irregularities in the pavement surface that affect ride quality and safety. Pavement Roughness is known as the International Roughness Index (IRI). Understanding the development and extent of these distresses is crucial for effective pavement management and maintenance decision-making (Bhandari, Luo & Wang 2023).
performance prediction models using regression methods, examining two distinct climate zones, Dry-Non-Freeze and Wet-Freeze, and utilizing data from the Long-Term Pavement Performance (LTPP) program. This research aims to identify and analyze factors affecting pavement performance. The study investigates three main types of pavement distress: roughness (International Roughness Index), rutting, and alligator cracking, which significantly influence the overall pavement condition. The research assesses the impact of several variables obtained from the Literature Review on pavement performance, which are expected to affect pavement performance by applying multiple linear and logistic regression models. Multiple linear regression models were used for predicting pavement roughness and rutting, while logistic regression was used to predict the occurrence of alligator cracking. Findings from this comprehensive analysis are expected to provide actionable insights that can optimize pavement design, construction, and maintenance practices, ultimately enhancing road safety and extending pavement life across different environmental conditions.
The main objective of this study is to develop multiple linear regression and logistic regression models to predict the pavement performance and understand the variables that affect pavement performance in two different climate zones (i.e., Dry-Non-Freeze and Wet-Freeze climate zones) of USA. This research utilizes pavement performance data from the Long-Term Pavement Performance (LTPP) Program, with a primary focus on Specific Pavement Studies (SPS-1). The LTPP data is publicly accessible and can be downloaded from the LTPP InfoPave website. The SPS-1 experiment is designed to investigate the effects of various structural factors on the performance of flexible pavements, including the base type, hot mix asphalt concrete (HMAC) layer thickness, base layer thickness, traffic loading, age, and environmental conditions such as precipitation and temperature.
Key Words: International Roughness Index (IRI), Rutting, Alligator Cracking, Pavement Performance Modelling, Multiple Linear Regression, Logistic Regression
In summary, this comprehensive study investigates the factors affecting pavement roughness, rutting, and alligator cracking by utilizing multiple linear regression and logistic regression models and the extensive LTPP SPS-1 dataset; the researchers aim to provide actionable insights that can help optimize pavement design, construction, and maintenance practices, ultimately leading to improved road conditions and extended pavement life. The findings of this study are expected to contribute to understanding the factors that affect pavement performance in two different climate zones of the USA.
1. INTRODUCTION Pavement damage is a critical issue rapidly increasing due to various factors. The deterioration of pavements over time is a complex process influenced by multiple variables, including vehicle loads, structural capacity, materials, construction quality, and environmental conditions. Among these factors, heavy vehicle loads, and insufficient structural capacity of pavements have been identified as significant contributors to poor road conditions. To address this issue, researchers have been focusing on understanding the effects of these factors on pavement performance, with the goal of developing effective strategies for pavement preservation and life extension (Bhandari, Luo & Wang 2023).
2. BACKGROUND Pavement condition data is essential for evaluating the structural health and serviceability of road pavements. Accurate data collection requires assessing both the pavement surface and structural condition, as pavement structural condition cannot be predicted based on the pavement surface condition (Ahmed et al., 2022). To make informed pavement maintenance decisions, it is crucial to
This study focuses on three primary types of distress used as performance measures of road conditions: alligator cracking, rutting, and roughness. Alligator cracking is fatigue cracking that appears as interconnected cracks resembling an alligator’s skin pattern. Rutting is a longitudinal depression
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