Pavement deterioration creates conditions that undermine their performances,which gives rise to the need for maintenance and rehabilitation.This paper develops a mathematical multi-linear regression analysis (MLRA)model to determine a pavement sustainability index (PSTI) as dependent variable for flexible pavements in Maryland.Four categories of pavement performance evaluation indicators are subdivided into seven pavement condition indices and analyzed as independent variables for each section of pavement.Data are collected from five different roadways using field evaluations and existing database.Results indicate that coefficient of determination (R2) is correlated and significant,R2 =0.959.Of the seven independent variables,present serviceability index (PSI) is the most significant with a coefficient value of 0.032,present serviceability rating (PSR) coefficient value =0.028,and international roughness index (IRI)coefficient value =-0.001.Increasing each unit value of coefficients for PSI and PSR would increase the value of PSTI;thereby providing a more sustainable pavement infrastructure;which explains the significance of the model and why IRI will most likely impact environmental,economic and social values.
展开▼
机译：The State-of-the-Art Review on Applications of Intrusive Sensing,Image Processing Techniques,and Machine Learning Methods in Pavement Monitoring and Analysis
机译：The State-of-the-Art Review on Applications of Intrusive Sensing,Image Processing Techniques,and Machine Learning Methods in Pavement Monitoring and Analysis
机译：Erratum for “Preparation of Low-Temperature Phase Change Materials Microcapsules and Its Application to Asphalt Pavement” by Feng Li, Siqi Zhou, Sai Chen, Zhenglong Yang, Jian Yang, Xingyi Zhu, Yuchuan Du, Peiting Zhou, and Zhihao Cheng
