Intelligent Assessment of Pavement Condition Indices Using Artificial Neural Networks

Sami Abdullah Osman, Meshal Almoshaogeh (), Arshad Jamal (), Fawaz Alharbi, Abdulhamid Al Mojil and Muhammad Abubakar Dalhat
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Sami Abdullah Osman: Transportation and Traffic Engineering Department, College of Engineering, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31451, Saudi Arabia
Meshal Almoshaogeh: Department of Civil Engineering, College of Engineering, Qassim University, Buraidah 51452, Saudi Arabia
Arshad Jamal: Transportation and Traffic Engineering Department, College of Engineering, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31451, Saudi Arabia
Fawaz Alharbi: Department of Civil Engineering, College of Engineering, Qassim University, Buraidah 51452, Saudi Arabia
Abdulhamid Al Mojil: Transportation and Traffic Engineering Department, College of Engineering, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31451, Saudi Arabia
Muhammad Abubakar Dalhat: Transportation and Traffic Engineering Department, College of Engineering, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31451, Saudi Arabia

Sustainability, 2022, vol. 15, issue 1, 1-30

Abstract: The traditional manual approach of pavement condition evaluation is being replaced by more sophisticated automated vehicle systems. Although these automated systems have eased and hastened pavement management processes, research is ongoing to further improve their performances. An average state road agency handles thousands of kilometers of the road network, most of which have multiple lanes. Yet, for practical reasons, these automated systems are designed to evaluate road networks one lane at a time. This requires time, energy, and possibly more equipment and manpower. Multiple Linear Regression (MLR) analysis and Artificial Neural Network (ANN) were employed to examine the feasibility of modeling and predicting pavement distresses of multiple lanes as functions of pavement distresses of a single adjacent lane. The successful implementation of this technique has the potential to cut the energy and time requirement at the condition evaluation stage by at least half, for a uniform multi-lane highway. Results showed promising model performances that indicate the possibility of evaluating a multi-lane highway pavement condition (PC) by single lane inspection. Traffic direction parameters, location, and lane matching parameters contributed significantly to the performance of the ANN PC prediction models.

Keywords: pavement condition; degradation; prediction; artificial intelligence; artificial neural network; regression analysis; pavement evaluation; Saudi Arabia (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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