A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Deterioration Prediction Model Development and Analysis for Alberta’s Provincial Highway Road Network's Pavement Condition
Road and highway networks are among the most critical infrastructures that significantly impact society. Therefore, predictive analysis is needed to investigate these networks' future performance to better understand their efficient maintenance management and resource allocation. In this research, a dataset containing the provincial highway network's pavement conditions in Canada's Alberta Province for several consecutive years has been selected. By implementing the Artificial Neural Network (ANN) model and different regression methods, pavement performance prediction has been conducted based on the literature and previous research works. The models are developed and processed to achieve the most accurate deterioration model by obtaining numerical results. Finally, appropriate degradation models have been selected representing some important pavement condition factors such as the International Roughness Index (IRI) and rutting data. The corresponding departments and organizations and could perform activity planning and project prioritization by having a proper prediction model. For different road networks, decision-makers may establish and design the necessary corrective measures and actions such as maintenance and rehabilitation for highways and roads.
Deterioration Prediction Model Development and Analysis for Alberta’s Provincial Highway Road Network's Pavement Condition
Road and highway networks are among the most critical infrastructures that significantly impact society. Therefore, predictive analysis is needed to investigate these networks' future performance to better understand their efficient maintenance management and resource allocation. In this research, a dataset containing the provincial highway network's pavement conditions in Canada's Alberta Province for several consecutive years has been selected. By implementing the Artificial Neural Network (ANN) model and different regression methods, pavement performance prediction has been conducted based on the literature and previous research works. The models are developed and processed to achieve the most accurate deterioration model by obtaining numerical results. Finally, appropriate degradation models have been selected representing some important pavement condition factors such as the International Roughness Index (IRI) and rutting data. The corresponding departments and organizations and could perform activity planning and project prioritization by having a proper prediction model. For different road networks, decision-makers may establish and design the necessary corrective measures and actions such as maintenance and rehabilitation for highways and roads.
Deterioration Prediction Model Development and Analysis for Alberta’s Provincial Highway Road Network's Pavement Condition
Lecture Notes in Civil Engineering
Walbridge, Scott (editor) / Nik-Bakht, Mazdak (editor) / Ng, Kelvin Tsun Wai (editor) / Shome, Manas (editor) / Alam, M. Shahria (editor) / el Damatty, Ashraf (editor) / Lovegrove, Gordon (editor) / Esmaeili, Foad (author) / Fadaeefath Abadi, Mostafa (author) / Nasiri, Fuzhan (author)
Canadian Society of Civil Engineering Annual Conference ; 2021
Proceedings of the Canadian Society of Civil Engineering Annual Conference 2021 ; Chapter: 24 ; 251-264
2022-05-18
14 pages
Article/Chapter (Book)
Electronic Resource
English
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