PURPOSES : For most local governments, including that of Gangwon-do, the establishment of an organized pavement management system is insufficient, resulting in problems such as inefficient distribution and use of maintenance budgets for deteriorated road pavements. In this study, we aimed to contribute to the establishment of a more reasonable road maintenance strategy by developing a model for predicting the annual international roughness index (IRI) change for national highway asphalt pavements in Gangwon-do based on big data analysis. METHODS : Data on independent and dependent variables used for model development were collected. The collected data were subjected to exploratory data analysis (EDA) and data preprocessing. Independent variable candidates were selected to reduce multicollinearity through correlation analysis and specific conditions. A final model was selected, and sensitivity analysis was performed. RESULTS : The final model that predicts annual IRI change uses independent variables such as annual temperature range, minimum temperature, freeze-thaw days, IRI, surface distress (SD), and freezing days. The sensitivity analysis confirmed that the annual IRI change was affected in the order of annual temperature range, minimum temperature, freeze-thaw days, IRI, SD, and freezing days. CONCLUSIONS : Road maintenance can be performed rationally by predicting future pavement conditions using the model developed in this study. The accuracy of the prediction model can be improved if additional data, such as material properties and pavement thickness, are obtained in future studies.

PURPOSES : To efficiently manage pavements, a systematic pavement management system must be established based on regional characteristics. Suppose that the future conditions of a pavement section can be predicted based on data obtained at present. In this case, a more reasonable road maintenance strategy should be established. Hence, a prediction model of the annual surface distress (SD) change for national highway pavements in Gangwon-do, Korea is developed based on influencing factors. METHODS : To develop the model, pavement performance data and influencing factors were obtained. Exploratory data analysis was performed to analyze the data acquired, and the results show that the data were preprocessed. The variables used for model development were selected via correlation analysis, where variables such as surface distress, international roughness index, daily temperature range, and heat wave days were used. Best subset regression was performed, where the candidate model was selected from all possible subsets based on certain criteria. The final model was selected based on an algorithm developed for rational model selection. The sensitivity of the annual SD change was analyzed based on the variables of the final model. RESULTS : The result of the sensitivity analysis shows that the annual SD change is affected by the variables in the following order: surface distress ˃ heat wave days ˃ daily temperature range ˃ international roughness index. CONCLUSIONS : An annual SD change prediction model is developed by considering the present performance, traffic volume, and climatic conditions. The model can facilitate the establishment of a reasonable road maintenance strategy. The prediction accuracy can be improved by obtaining additional data, such as the construction quality, material properties, and pavement thickness.

PURPOSES : The objective of this study is to develop regression models for surface distress (SD), rut depth (RD), and international roughness index (IRI) of Jeju Island local road by analyzing the correlations between the pavement performance and its influencing factors. METHODS : First, the differences between pavements in inland Korea and Jeju Island in terms of performance and influencing factors were investigated. Influencing factors were assigned to pavement sections on Jeju Island using the inverse distance weighting method, and the correlations between the pavement performance and influencing factors were analyzed. As a result, maximum temperature, heat wave days, annual temperature range, precipitation days, precipitation intensity, ESAL, etc. were determined as independent variables for the pavement performance prediction models. Multiple regression analysis was performed to develop the pavement performance models using the selected independent variables. RESULTS : The RD, maximum temperature, and precipitation days were determined to be the independent variables for the SD predictive model. The SD, maximum temperature, annual temperature range, heat wave days, and precipitation days were selected as independent variables of the RD prediction model. In addition, the RD, annual temperature range, heat wave days, precipitation days, and ESAL were selected as independent variables for the IRI prediction model. CONCLUSIONS : As a result of the study, an actual forecast model for SD, RD, and IRI was developed. Based on this model, it is possible to estimate the predictive value of the missing performance data in the studied interval. If the factors affecting performance are managed in terms of maintenance beyond a certain level, it can help those responsible for road maintenance to rationally select the maintenance method and timing.