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T. Parsons, B. Pullen (2017)
Characterization of Pavement Condition Index Deterioration Curve Shape for USAF Airfield Pavements
Patrick Smagt, Ben Krose (2018)
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Development of Pavement Condition Index Model for Flexible Pavement in Baghdad CityThe Journal of Engineering, 14
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RESEARCH NOTE PREDICTION OF THE PAVEMENT CONDITION FOR URBAN ROADWAY A TEHRAN CASE STUDY
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M. Mahmood (2015)
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Validation and Improvement of Pavement ME Flexible Pavement Roughness Prediction Model Using Extended LTPP Database
Amr Elhadidy, S. El-Badawy, E. Elbeltagi (2019)
A simplified pavement condition index regression model for pavement evaluationInternational Journal of Pavement Engineering, 22
H. Shahnazari, M. Tutunchian, M. Mashayekhi, A. Amini (2012)
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Performance models for hot mix asphalt pavements in urban roadsConstruction and Building Materials, 116
Kan Wu (2015)
Development of PCI-based Pavement Performance Model for Management of Road Infrastructure System
M. Jalal, I. Flóris, L. Quadrifoglio (2017)
COMPUTER-AIDED PREDICTION OF PAVEMENT CONDITION INDEX (PCI) USING ANN
Pavement performance prediction is a primary concern for pavement researchers and practitioners. The impact of climatic conditions and traffic characteristics on pavement performance is indisputable. The main objective of this study is to investigate the combined effect of both climate and traffic loading on pavement performance. Multi-input performance prediction models in terms of the well-known Pavement Condition Index (PCI) are proposed. The Long-Term Pavement Performance (LTPP) database is used for the models development and validation. Data from 89 LTPP sections including 617 observations from the Specific Pavement Studies (SPS-1) with no maintenance activities are collected. These data cover the four climatic zones (wet, wet freeze, dry, and dry freeze) in the USA, different pavement structures, and different levels of traffic loading. Based on these data, PCI prediction models are developed using two modeling approaches: multiple linear regression analysis and artificial neural networks (ANNs). The proposed models predict the PCI as a function of climatic factors, namely average annual temperature, standard deviation of monthly temperature, precipitation, wind speed, freezing index, total pavement thickness, and weighted plasticity index. Additionally, traffic loading, expressed in terms of the classical equivalent single-axle loads, is considered. The regression model yielded a coefficient of determination (R2) value of 0.80, whereas the ANNs model results in a relatively higher R2 value of 0.88. The proposed models are not only simple and accurate; they also have the potentials of being adopted in countries experiencing similar climatic conditions and traffic loading.
Innovative Infrastructure Solutions – Springer Journals
Published: Feb 24, 2020
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