Impacts of Seasonal and Annual Weather Variations on Network-Level Pavement Performance

Abstract

This study was aimed to determine the extent of association between network-level pavement condition and seasonal and annual weather variations. Pavement condition data recorded in the Texas Department of Transportation’s Pavement Management Information System (PMIS) database between 2000 and 2008 was highlighted. Meteorological data collected at different districts across the whole state were used in the explanatory variables. Dynamic panel data analysis was used in the deterioration models to quantify the effects of temperature and precipitation variations on pavement conditions. Based on the statistical implication from the model estimation results, significant correlations were identified between pavement conditions, the average monthly rainfall, and the average monthly temperature recorded 1 to 23 months prior to pavement condition inspection.

Simulation of autonomous truck for minimizing asphalt pavement distresses

“…The resilient modulus of asphalt material varies with pavement temperature and significantly and affect the asphalt pavement performance. Resilient modulus is the critical variable in asphalt pavement design, increases at low temperature (Gao et al, 2019;Zhao et al, 2020). An attempt has been made in this study to minimise the effects of autonomous vehicles by scheduling vehicle movement in the period of the low-temperature of the day.…”

“…In the recent crisis of COVID-19, fully autonomous vehicles are being used for supplying medical necessities in some places of China (Donelson, 2020). Fee (2020) reported that autonomous vehicles would occupy 25% of all miles driven in the US by 2030, and new vehicle sales might get increased up to 55% by 2040. A technology company, Waymo, led one pilot programme for autonomous taxi service in Arizona.…”

Smartphone-Based Cost-Effective Pavement Performance Model Development Using a Machine Learning Technique with Limited Data

“…Younos et al utilized the PCI as an index to develop a performance model based on pavement age, pavement cracks, traffic loading, and maintenance effects using LTTP data through artificial intelligence and Markov chain techniques [34]. Gao et al investigated the effect of the ambient environment, i.e., variations of weather, along with seasonal and annual changes on the pavement performance at a network level [35].…”