PURPOSES : With the recent enactment of the 「Framework Act on Sustainable Infrastructure Management」 in Korea, the establishment of mid- to long-term management plans for social infrastructure and the feasibility evaluation of maintenance projects have become mandatory. To this end, the life cycle cost analysis is essential. However, owing to the absence of a deterioration model, trials and errors are in progress. METHODS : In this study, a deterioration model was established for bridges, which are the representative social infrastructures of roads, particularly for expansion joints that can cause enormous damage to not only the superstructure but also the substructure. The deterioration model was classified into rubber and steel, based on the material of the expansion joint. The analysis used the inspection and climate data conducted in Korea over the last 12 years. The Bayesian Markov Hazard model was applied as the analysis technique. RESULTS : The average life expectancy by type of expansion joint was analyzed to be 8.9 and 6.6 years for rubber and steel, respectively. For probabilistic life cycle cost analysis, the probability distribution of the life expectancy, validity range by confidence level, and Markov transition probability matrix were presented. CONCLUSIONS : In this study, the basis for deterministic and probabilistic life cycle cost analysis of expansion joints was laid. In future studies, it will be necessary to establish a standardized deterioration model for all types of infrastructure, including all bridge elements.

PURPOSES : The purpose of this study was to investigate directions for future smart city transportation policies and service development by evaluating road service satisfaction levels and predicting future service demand. METHODS : A nationwide survey was conducted in Korea to develop a transportation service evaluation system based on the functions and objects of transportation. The satisfaction level of road service was evaluated using an analytic hierarchy process (AHP), promising service sectors were identified using the revised importance-performance analysis (IPA) technique, and detailed service demands by sector were suggested. RESULTS : The most valuable service value felt by the people was "safety" (weight 0.4), and the overall satisfaction level was 68.9 points, slightly exceeding "normal." As a result of analyzing the promising service sectors by dividing them into urban and rural areas, "handicapped, elderly, and pedestrians" were important in both areas, and "road facility maintenance" was classified as an additional promising sector for rural areas. CONCLUSIONS : People demand that future smart city transportation policies and services should be "people" and "safety" centered. In addition, it is necessary to pay attention not only to the development of new services but also to the improvement of problems with existing services and policies.

PURPOSES : The purpose of this study is to enhance the reliability of artificial intelligence for a noise-based pavement condition rating system (to a target performance of 95 %). METHODS : By comparing four types of pattern recognition artificial intelligence, this work acquires high-quality learning data and optimizes data learning through analysis of error characteristics. RESULTS : The system reliability improved up to 97 % (82 % in a prior study). In addition, 100 % was achieved for the E(F) condition grade, which has a direct impact on maintenance decision making. CONCLUSIONS : KNN-DTW (K-nearest neighbor dynamic time warping) is judged to be the most suitable type of artificial intelligence for a noise-based pavement condition rating system; a 4-grade system is the most suitable for classifying pavement condition.

PURPOSES : The purpose of this study is to estimate the reduction in traffic noise in a double-layered specific porous pavement at roadsides based on variations in traffic volume and driving speed. METHODS : A statistical pass-by (SPB) method was employed in this study to measure noise. Variations in the following parameters were measured: running speed, heavy traffic percentage, and traffic volume. RESULTS : Quantitative analysis revealed that the double-layered porous pavement reduced noise levels by 9.16 dB(A) at a 95% confidence level at the sides of roads. CONCLUSIONS : As a countermeasure of traffic noise, porous pavement has been recommended. This research quantitatively proved that double-layered porous pavement can reduce traffic noise by more than 9.0 dB(A) at roadsides

PURPOSES : The purpose of this study is to estimate the reduction of traffic noise in a double-layered specific porous pavement based on the traffic speed variation. METHODS : The close-proximity method was used in noise measurement, and the running speed was measured at 10 km/h and from 50 to 80 km/h. RESULTS : From the quantitative analysis, it was found that the double-layered porous pavement reduced by 9.4 dB (A) on the average and 9.16 dB (A) at a 95% confidence level. CONCLUSIONS : The use of porous pavements have been recommended to minimize traffic noise. In this study, it is quantitatively demonstrated that the double-layered porous pavement can reduce the traffic noise by more than 9.0 dB(A).

PURPOSES : This study aimed to estimate road pavement life expectancy using Bayesian Markov Mixture Hazard Model, to support infrastructure asset management. In addition, the life expectancies for the pavement condition index were compared among regional construction and management administrations. METHODS : Eleven years of National Highway road pavement monitoring data fused with ESAL (Equivalent Single Axle Loads), SNP (Structural Number of Pavement, an indicator of structural capacity), and average low temperature, total rainfall, and de-icing were used for the deterioration modeling. Deterioration modeling was performed through the Bayesian Markov Mixture Hazard Model. RESULTS : The expected life expectancy of the crack was estimated at 12.28 to 18.51 years, rut depth was estimated at 15.93 to 25.3 years, and the International Roughness Index was estimated at 10.44 to 14.33 years. It was also confirmed that the heterogeneity factor proposed in the Bayesian Markov Mixture Hazard Model could be used to analyze group characteristics and differences in the benchmark. CONCLUSIONS: This study provided important information in that it compared the life expectancies and structural characteristics of the pavement condition indexes among regional construction and management administrations. Based on this result, it is expected that a pavement structure design and maintenance strategy suitable for deterioration characteristics among regional construction and management administrations will be established.

PURPOSES : This paper is aimed at suggesting a novel approach for determining the pavement condition rating based on the tire-surface friction noise using a machine learning algorithm as a low-end pavement condition monitoring system. METHODS : Vehicle on-board type noise measurement system according to the ISO11819-2, and the K-nearest neighbors with dynamic time warping algorithm were applied. The system and algorithm were empirically tested with a field study. RESULTS : The developed AI- and noise-based pavement condition monitoring system demonstrated significantly positive results with a precision 90.8%, recall 84.8%, and f1-score 86.1%. CONCLUSIONS: We herein confirmed that the acoustic property between the tire and road surface can be used for monitoring pavement conditions. It is believed this finding presented a new paradigm for monitoring pavement conditions based on visual information. However, extensive studies focused on the practical application of this method are required.

PURPOSES : The purpose of this study is to compare noise reduction quantities between before/after two-layer low noise pavement implementation using equivalent noise level analysis and to analyze the noise reduction effects of the two layer low noise pavement with a statistical method such as the Anderson-Darling Test.METHODS: In order to compare and to analyze noise reduction effects between before/after two-layer low noise pavement implementation, data acquisition as noise levels on a roadside and an apartment rooftop was conducted in the study area. The equivalent noise level was estimated in order to compare noise reduction quantities and the Anderson-Darling Test was carried out for estimating noise reduction effects of the two-layer low noise pavement.RESULTS: The equivalent noise levels of before/after two-layer low noise pavement implementation for the roadside during the daytime are 65.355 dB and 63.520 dB and during the nighttime are 62.463 dB and 59.088 dB. The equivalent noise levels for the apartment rooftop during daytime are 57.301 dB and 59.088 dB and during the nighttime are 54.616 dB and 52.464 dB. Also two-layer low noise pavement decreased the noise reduction effects estimated with the statistical method as the Anderson-Darling test for the roadside during the daytime by around 66.68% and decreased noise reduction effects on the roadside during the nighttime by 0.70%. Moreover it reduced noise reduction effects in the apartment rooftop during the daytime and nighttime by 0% and 96.32%, respectively.CONCLUSIONS : Based on the result of this study, two-layer low noise pavement can positively affect noise reduction during both the daytime and nighttime according to the results of estimating the equivalent noise levels and the Anderson-Darling test.

PURPOSES : This study is aimed at development of a stochastic pavement deterioration forecasting model using National Highway Pavement Condition Index (NHPCI) to support infrastructure asset management. Using this model, the deterioration process regarding life expectancy, deterioration speed change, and reliability were estimated. METHODS: Eight years of Long-Term Pavement Performance (LTPP) data fused with traffic loads (Equivalent Single Axle Loads; ESAL) and structural capacity (Structural Number of Pavement; SNP) were used for the deterioration modeling. As an ideal stochastic model for asset management, Bayesian Markov multi-state exponential hazard model was introduced. RESULTS: The interval of NHPCI was empirically distributed from 8 to 2, and the estimation functions of individual condition indices (crack, rutting, and IRI) in conjunction with the NHPCI index were suggested. The derived deterioration curve shows that life expectancies for the preventive maintenance level was 8.34 years. The general life expectancy was 12.77 years and located in the statistical interval of 11.10-15.58 years at a 95.5% reliability level. CONCLUSIONS : This study originates and contributes to suggesting a simple way to develop a pavement deterioration model using the total condition index that considers road user satisfaction. A definition for level of service system and the corresponding life expectancies are useful for building long-term maintenance plan, especially in Life Cycle Cost Analysis (LCCA) work.

PURPOSES :This study aims to improve complex modeling of multivariable, nonlinear, and overdispersion data with an artificial neural network that has been a problem in the civil and transport sectors.METHODS :Deep learning, which is a technique employing artificial neural networks, was applied for developing a large bus fuel consumption model as a case study. Estimation characteristics and accuracy were compared with the results of conventional multiple regression modeling.RESULTS :The deep learning model remarkably improved estimation accuracy of regression modeling, from R-sq. 18.76% to 72.22%. In addition, it was very flexible in reflecting large variance and complex relationships between dependent and independent variables.CONCLUSIONS :Deep learning could be a new alternative that solves general problems inherent in conventional statistical methods and it is highly promising in planning and optimizing issues in the civil and transport sectors. Extended applications to other fields, such as pavement management, structure safety, operation of intelligent transport systems, and traffic noise estimation are highly recommended.

PURPOSES: This study aimed to evaluate the performance of pavement management works and to develop a function for estimating the level of service (LOS) and cost of service (COS) for the systematic and quantitative management of pavement performance in the public sector. METHODS: The International Roughness Index (IRI) was used as the performance index for pavement management. Long-term pavement performance data for a period of 7 years (2007-2014) collected by the National Highway Pavement Management System and historical maintenance budget data published by the South Korean government were used to develop the LOS-COS function. Based on the function, a model for estimating the appropriate budget as well as the network conditions was suggested. RESULTS : There was high degree of correlation between pavement performance and the investment level (R = - 0.74). The developed LOS-COS function suggested that the unit cost to improve the network IRI to 1 m/km was 32.6 billion KRW. Further, the maintenance costs normalized with respect to the LOS levels were LOS-A = 88.2 billion KRW, LOS-B = 55.6 billion KRW, and LOS-C = 23.0 billion KRW. CONCLUSIONS: This study proposes a simple way of developing a LOS-COS function. It also shows how to develop a network budget demand and condition estimation model using the LOS-COS function. In addition, it is the first attempt to evaluate the road maintenance budget in South Korea. It is expected that these results will help in the negotiations between the road managers and budget makers.

PURPOSES : This study aims to examine the differences between the existing traffic demand forecasting method and the traffic demand forecasting method considering future regional development plans and new road construction and expansion plans using a four-step traffic demand forecast for a more objective and sophisticated national highway maintenance. This study ultimately aims to present future pavement deterioration and budget forecasting planning based on the examination. METHODS: This study used the latest data offered by the Korea Transport Data Base (KTDB) as the basic data for demand forecast. The analysis scope was set using the Daejeon Metropolitan City’s O/D and network data. This study used a traffic demand program called TransCad, and performed a traffic assignment by vehicle type through the application of a user equilibrium-based multi-class assignment technique. This study forecasted future traffic demand by verifying whether or not a realistic traffic pattern was expressed similarly by undertaking a calibration process. This study performed a life cycle cost analysis based on traffic using the forecasted future demand or existing past pattern, or by assuming the constant traffic demand. The maintenance criteria were decided according to equivalent single axle loads (ESAL). The maintenance period in the concerned section was calculated in this study. This study also computed the maintenance costs using a construction method by applying the maintenance criteria considering the ESAL. The road user costs were calculated by using the user cost calculation logic applied to the Korean Pavement Management System, which is the existing study outcome. RESULTS : This study ascertained that the increase and decrease of traffic occurred in the concerned section according to the future development plans. Furthermore, there were differences from demand forecasting that did not consider the development plans. Realistic and accurate demand forecasting supported an optimized decision making that efficiently assigns maintenance costs, and can be used as very important basic information for maintenance decision making. CONCLUSIONS : Therefore, decision making for a more efficient and sophisticated road management than the method assuming future traffic can be expected to be the same as the existing pattern or steady traffic demand. The reflection of a reliable forecasting of the future traffic demand to life cycle cost analysis (LCCA) can be a very vital factor because many studies are generally performed without considering the future traffic demand or with an analysis through setting a scenario upon LCCA within a pavement management system.

PURPOSES: This paper aims at the implementation of a balanced scorecard that can be widely applied to modern business management for use in the public road management sector. METHODS: This study applied the newly developed LOS-based balanced scorecard system instead of a traditional Key Performance Index (KPI) for better decision making in asset management planning. As an evaluation technique, a“ hierarchical alignment and cascading method” is also suggested. Finally, the suggested system has been empirically applied to a regional government. RESULTS : To provide stable and sustainable road services, the balanced scorecard informs the regional government of needed improvements in its asset management plans regarding budget optimization, structural management, the development of inner-business processes, and human resources. CONCLUSIONS : An LOS-based balanced scorecard for managing road services and organizations in a quantitative manner has been successfully developed and tested through a field study. The developed scorecard is a timely topic and a useful analytical tool for coping with the new phases of an aging infrastructure, tighter budgets, and demand for greater public accountability.