PURPOSES : In this study, a preliminary study on the optimal clustering techniques for the preprocessing of pavement management system (PMS) data was conducted using K-means and mean-shift techniques to improve the correlation between the dependent and independent variables of the pavement performance model. METHODS : The PMS data of Jeju Island was preprocessed using the K-means and mean-shift algorithms. In the case of the K-means method, the elbow method and silhouette score were used to determine the optimal number of clusters (K). Moreover, in the case of the mean-shift method, Scott’s rule of thumb and Silverman’s rule of thumb were used to determine the optimal cluster bandwidth. RESULTS : The optimal cluster sets were selected for the rut depth (RD), annual average daily traffic (AADT), and annual maximum temperature (AMT) for each clustering technique, and their similarities with the original data were investigated. Additionally, the correlation improvement between the dependent and independent variables were investigated by calculating the clustering score (CS). Consequently, the K-means method was selected as the optimal clustering technique for the preprocessing of PMS data. The K-means method improved the correlations of more variables with the dependent variable compared to the mean-shift method. The correlations of the variables related to high temperature—such as the annual temperature change, summer days, and heat wave days—were improved in the case wherein the AMT, a climate factor, was used as an independent variable in the K-means clustering method. CONCLUSIONS : The applicability of the clustering methods to preprocessing of PMS data was identified in this study. Improvements in the pavement performance prediction model developed using traditional statistical methods may be identified by developing a model using clustering techniques in a future study.

PURPOSES : Local governments in Korea, including Incheon city, have introduced the pavement management system (PMS). However, the verification of the repair time and repair section of roads remains difficult owing to the non-existence of a systematic data acquisition system. Therefore, data refinement is performed using various techniques when analyzing statistical data in diverse fields. In this study, clustering is used to analyze PMS data, and correlation analysis is conducted between pavement performance and influencing factors. METHODS : First, the clustering type was selected. The representative clustering types include K-means, mean shift, and density-based spatial clustering of applications with noise (DBSCAN). In this study, data purification was performed using DBSCAN for clustering. Because of the difficulty in determining a threshold for high-dimensional data, multiple clustering, which is a type of DBSCAN, was applied, and the number of clustering was set up to two. Clustering for the surface distress (SD), rut depth (RD), and international roughness index (IRI) was performed twice using the number of frost days, the highest temperature, and the average temperature, respectively. RESULTS : The clustering result shows that the correlation between the SD and number of frost days improved significantly. The correlation between the maximum temperature factor and precipitation factor, which does not indicate multicollinearity, improved. Meanwhile, the correlation between the RD and highest temperature improved significantly. The correlation between the minimum temperature factor and precipitation factor, which does not exhibit multicollinearity, improved considerably. The correlation between the IRI and average temperature improved as well. The correlation between the low- and high-temperature precipitation factors, which does not indicate multicollinearity, improved. CONCLUSIONS : The result confirms the possibility of applying clustering to refine PMS data and that the correlation among the pavement performance factors improved. However, when applying clustering to PMS data refinement, the limitations must be identified and addressed. Furthermore, clustering may be applicable to the purification of PMS data using AI.

PURPOSES : The actual service life of repair methods applied to cement concrete pavement is analyzed based on de-icing agent usage. METHODS : Highway PMS data pertaining to de-icing agent usage are classified into three grades: low (1~5 ton/lane/year), medium (5~8 ton/lane/year), and high (greater than 8 ton/lane/year). The repair methods considered include diamond grinding, patching, joint repair, partial depth repair, and asphalt overlay on five major highways. The service life of each repair method is analyzed based on the usage level of the de-icing agent. RESULTS : The service lives of the applied repair methods are much shorter than expected. It is confirmed that the service life afforded by diamond grinding, patching, and joint repair methods are not significantly affected by the use of de-icing agents, whereas that afforded by asphalt overlay and partial depth repair methods is affected significantly. The service life afforded by the asphalt overlay and partial depth repair methods decreases at high usage levels of the de-icing agent (greater than 8 ton/lane/year). CONCLUSIONS : Among the repair methods considered, the service life afforded by partial depth repair and asphalt overlay is affected significantly by the amount of de-icing agent used. Additionally, the differences between the expected and actual analyzed service lives should be considered in the next-generation maintenance strategy for cement concrete pavements.

Recently, the number and scale of projects being carried out within the enterprise are increasing. Accordingly, many companies are competitively introducing a Project Management Office (PMO) to efficiently manage these projects, allocate resources, and effectively link the projects and corporate strategies. However, the project manager who directly manages the project wants to receive support from the PMO in many areas for successful project management, but the project manager does not like to be interfered with by the PMO. On the other hand, the PMO may not be able to satisfy all the requirements of each PM as PMO oversees the entire project with limited resources. In addition, since the PMO must monitor and control the project and support the project according to the priorities of each project, conflicts with each PM can be formed. Therefore, in this study, based on the case of a company, the difference between the perceptions of PMs and PMOs about the importance of the required roles of PMOs is to be examined. As a result of the study, it was confirmed that the core functions and sub-functions of PMO, which PMO members and PMs consider important, are different. It was identified that the PMs valued the PMO function that would be helpful for their successful project execution. On the other hand, PMO members revealed that they had a relatively high priority for the roles to monitor and control project performance for which they were directly responsible.

PURPOSES : This study analyzes the service life of the repair methods of jointed plain concrete pavement (JPCP) on expressways in Korea using PMS data. METHODS : The Korea Expressway Corporation PMS data acquired from five major expressways in Korea were used for the analysis. The service lives of the repair methods were considered for two different cases: 1) the previous repair methods had been completely rerepaired by another or the same method due to their damage, and 2) the current repair methods were still in use. RESULTS : The service lives of D/G and section repair were shown to be at least 30 % and 50 % shorter than expected, respectively. Joint sealing and crack sealing exhibited a service life similar to that expected. The Mill-and-Asphalt-overlay method showed an approximately 30 % longer service life; this might be because some damage to the asphalt overlay is typically neglected until subsequent maintenance and repair. When multiple repairs were applied in series for an identical pavement section, the service life of repairs on previously damaged secti ons become even shorter compared to their first application. CONCLUSIONS : It was found that the analyzed service life of most important repair methods did not reach the expected service life, and that the service life of the same repair method becomes shorter as applied to the previously repaired concrete pavement sections. These shorter service lives should be seriously considered in future JPCP repair strategy development.

PURPOSES : The purpose of this study is to develop a regression model to predict the International Roughness Index(IRI) and Surface Distress(SD) for the estimation of HPCI using Expressway Pavement Management System(PMS).METHODS : To develop an HPCI prediction model, prediction models of IRI and SD were developed in advance. The independent variables considered in the models were pavement age, Annual Average Daily Traffic Volume(AADT), the amount of deicing salt used, the severity of Alkali Silica Reaction(ASR), average temperature, annual temperature difference, number of days of precipitation, number of days of snowfall, number of days below zero temperature, and so on.RESULTS : The present IRI, age, AADT, annual temperature differential, number of days of precipitation and ASR severity were chosen as independent variables for the IRI prediction model. In addition, the present IRI, present SD, amount of deicing chemical used, and annual temperature differential were chosen as independent variables for the SD prediction model.CONCLUSIONS: The models for predicting IRI and SD were developed. The predicted HPCI can be calculated from the HPCI equation using the predicted IRI and SD.

The up-to-date small and medium-sized enterprises (SMEs) in Korea have tried to respond flexibly and rapidly to dynamic business environment and to establish efficient production management system based on information technologies. However, most of SMEs have faced with low applicability of the production management system resulting from high costs of introduction and maintenance. In this paper, a production planning and control system, that is S-PMS (production management system for SMEs), is proposed to solve the problem of low applicability and limited human resources. S-PMS enables production managers to efficiently collect and manage master data with the actual target production systems and explores the bottleneck process by means of simulation techniques to improve productivity. Furthermore, it implements rescheduling mechanism in terms of a variety of process routes. In essence, intuitive dispatching rules and integrated data management of S-PMS improve field applicability of production management system. Consequently, S-PMS is expected to be used as an efficient production management system of SMEs in Korea.