Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Predicting Pavement Roughness as a Performance Indicator Using Historical Data and Artificial Intelligence
Pavement performance indicators, such as the international roughness index, rutting, and fatigue cracking, are usually employed to evaluate the effectiveness of pavement rehabilitation treatments and maintenance strategies. Although the short-term effect of these strategies can be evaluated using the performance indices, estimation of long-term performance could help the pavement management systems to optimize the maintenance operations and minimize the costs. The long-term pavement performance (LTPP) database is a valuable resource for studying the performance of different pavement structure under various environmental and traffic conditions. The database could be employed in developing performance prediction models and help to prioritize the maintenance strategies. In this study, a set of LTPP data was employed to develop a pavement roughness prediction model using traffic data, pavement age, and structural number as input variables. The performance of the prediction models was reasonably satisfactory. The results of this study could be extended to a wider range of pavement structures from the LTPP database and can be calibrated for the specific traffic data and pavement properties for state departments of transportation.
Predicting Pavement Roughness as a Performance Indicator Using Historical Data and Artificial Intelligence
Pavement performance indicators, such as the international roughness index, rutting, and fatigue cracking, are usually employed to evaluate the effectiveness of pavement rehabilitation treatments and maintenance strategies. Although the short-term effect of these strategies can be evaluated using the performance indices, estimation of long-term performance could help the pavement management systems to optimize the maintenance operations and minimize the costs. The long-term pavement performance (LTPP) database is a valuable resource for studying the performance of different pavement structure under various environmental and traffic conditions. The database could be employed in developing performance prediction models and help to prioritize the maintenance strategies. In this study, a set of LTPP data was employed to develop a pavement roughness prediction model using traffic data, pavement age, and structural number as input variables. The performance of the prediction models was reasonably satisfactory. The results of this study could be extended to a wider range of pavement structures from the LTPP database and can be calibrated for the specific traffic data and pavement properties for state departments of transportation.
Predicting Pavement Roughness as a Performance Indicator Using Historical Data and Artificial Intelligence
Lucey, Joseph (Autor:in) / Fathi, Aria (Autor:in) / Mazari, Mehran (Autor:in)
International Airfield and Highway Pavements Conference 2019 ; 2019 ; Chicago, Illinois
18.07.2019
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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