A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Pavement Maintenance Threshold Detection and Network-Level Rutting Prediction Model Based on Finnish Road Data
Accurate prediction models for road structure deterioration increase the cost-effectiveness of road construction and the scheduling rehabilitation and maintenance of road structures. In this paper, a method to detect the minimum maintenance operation detection (MMOD) threshold and network-level pavement rutting prediction model are described. The MMOD threshold has the potential to filter network-level pavement rutting measurement data and improve prediction models. The model is a multilevel statistical time series model for rutting prediction without the need for measurement history. The model parameters used are pavement type and average daily traffic. The road maintenance planner estimates the need for a minimum sampling rate for future pavement performance measurements and predicts the pavement rut behavior. For asphalt concrete and soft asphalt concrete, the model gives realistic predictions for the first 10 years. For stone mastic asphalt, the realistic prediction window is the first six years.
Pavement Maintenance Threshold Detection and Network-Level Rutting Prediction Model Based on Finnish Road Data
Accurate prediction models for road structure deterioration increase the cost-effectiveness of road construction and the scheduling rehabilitation and maintenance of road structures. In this paper, a method to detect the minimum maintenance operation detection (MMOD) threshold and network-level pavement rutting prediction model are described. The MMOD threshold has the potential to filter network-level pavement rutting measurement data and improve prediction models. The model is a multilevel statistical time series model for rutting prediction without the need for measurement history. The model parameters used are pavement type and average daily traffic. The road maintenance planner estimates the need for a minimum sampling rate for future pavement performance measurements and predicts the pavement rut behavior. For asphalt concrete and soft asphalt concrete, the model gives realistic predictions for the first 10 years. For stone mastic asphalt, the realistic prediction window is the first six years.
Pavement Maintenance Threshold Detection and Network-Level Rutting Prediction Model Based on Finnish Road Data
Dettenborn, Taavi (author) / Hartikainen, Ari (author) / Korkiala-Tanttu, Leena (author)
2020-04-08
Article (Journal)
Electronic Resource
Unknown
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