Development of HPCI Prediction Model for Concrete Pavement Using Expressway PMS Database

A Feasibility Study for the Prediction of Concrete Pavement Condition Index (CPCI) Based on Machine Learning

“…Since approximately 10 years ago, various studies using a sizable database covering all factors affecting pavement other than road pavement have been conducted for more systematic management of the ever-growing amount of pavement. In addition to distress, roughness, and structural number (SN), which are representative variables used for road pavement management thus far, the amount of change in the pavement condition index has been analyzed by introducing the concept of change amount for each element [8]. In addition to the management aspect of road pavement, management measures, including road user safety, have been proposed [9], and skid variables other than distress, roughness, and SN have been added.…”

“…The CPCI data used in this study were collected from the PMS DB in South Korea. The CPCI includes a function of the amount of surface distress and the international roughness index (IRI), as shown in Equation (8).…”

“…Prediction accuracy can be expressed along with the concept of relative error. Please refer to Equation (8). 𝜀 .…”

“…Prediction accuracy can be expressed along with the concept of relative error. Please refer to Equation (8). ε R.E denotes the relative error rate between the actual data and the predicted data, and CPCI predicted and CPCI detected denote CPCI predicted through particle filtering and actual CPCI, respectively.…”