Korean National Highways Research Articles

Development and validation of a temperature correction model for FWD backcalculated moduli

ABSTRACT A temperature correction model for Falling Weight Deflectometer (FWD) backcalculated dynamic modulus was developed and validated in this study. FWD test was conducted to determine the field dynamic modulus, while the indirect tensile (IDT) dynamic modulus test was used to measure laboratory dynamic modulus. A temperature correction model was developed considering the effect of FWD loading frequency and pavement temperature. It was validated using field FWD data obtained from 26 Westrack pavement sections and Korean national highways’ long-term pavement performance database. Also, the correction factor was compared with similar existing models using FWD testing in the field. It was observed from study results that the temperature correction model is capable of predicting the dynamic modulus accurately.

Development of the Road Pavement Deterioration Model Based on the Deep Learning Method

In Korea, data on pavement conditions, such as cracks, rutting depth, and the international roughness index, are obtained using automatic pavement condition investigation equipment, such as ARAN and KRISS, for the same sections of national highways annually to manage their pavement conditions. This study predicts the deterioration of road pavement by using monitoring data from the Korean National Highway Pavement Management System and a recurrent neural network algorithm. The constructed algorithm predicts the pavement condition index for each section of the road network for one year by learning from the time series data for the preceding 10 years. Because pavement type, traffic load, and environmental characteristics differed by section, the sequence lengths (SQL) necessary to optimize each section were also different. The results of minimizing the root-mean-square error, according to the SQL by section and pavement condition index, showed that the error was reduced by 58.3–68.2% with a SQL value of 1, while pavement deterioration in each section could be predicted with a high coefficient of determination of 0.71–0.87. The accurate prediction of maintenance timing for pavement in this study will help optimize the life cycle of road pavement by increasing its life expectancy and reducing its maintenance budget.

Long-term performance of fiber-grid-reinforced asphalt overlay pavements: A case study of Korean national highways

Abstract The objective of this study was to verify the effect of fiber grid reinforcement on the long-term performance of asphalt overlay pavements by performing field investigation, laboratory test, and finite element analysis. Crack, rutting, roughness, and other miscellaneous distresses of fiber-grid-reinforced overlay sections and ordinary overlay sections were compared. Pavement conditions of fiber grid overlay sections before and after the overlay were also compared. Cores were obtained from the fiber-grid-reinforced and ordinary sections to measure bonding shear strength between the intermediate and overlay layers. Fracture energy, displacement after yield, and shear stiffness of the cores were also obtained by analyzing the test results. Finite element analysis was performed by using the test results to validate the effect of the fiber grid reinforcement on the long-term performance of asphalt overlay pavements. Rutting and fatigue cracking were predicted for three cases: 1) fiber grid was not used; 2) glass fiber grid was used; and 3) carbon fiber grid was used. The cracking ratio of fiber-grid-reinforced sections was much smaller than that of ordinary sections. The results of field investigation, laboratory test, and finite element analysis showed that the fiber grid reinforcement improved the long-term performance of asphalt overlay pavements.

Correlation Analysis between Pavement Condition Indices in Korean Roads

The Korean road network has been aging rapidly. It is necessary to assess the pavement conditions of various road networks in order to come up with proper rehabilitation strategies against the aging road networks. Pavement management system is an essential tool for road management, and each road management organization has developed its own pavement condition index for assessing the road conditions. Nonetheless, because the pavement condition of various types of roads could not be represented by a single pavement condition index, it was difficult to comprehend the condition of the entire roads of Korea and its changing trend at a glance. Moreover, there has been a limitation on the persuasion of the justification for the use of road management and rehabilitation budget at national level to the people and the budget office of Korea. This study derived the relationship among the pavement condition indices of Korean expressways, Korean national highways, and Seoul city roads so that those pavement indices can be represented by one unified pavement condition index. A sensitivity analysis of how variables of pavement distress (roughness, rut depth, and crack ratio) influenced each of the pavement condition index was carried out. Then, correlation equations between the pavement condition indices were developed in order to represent them by a single pavement condition index (NHPCI) through a statistical analysis of the pavement condition monitoring system data over 8 years, and its application plan is proposed.

Calibration and validation of a rutting model based on shear stress to strength ratio for asphalt pavements

In this study, a rutting model in power law form was proposed considering the shear stress to strength ratio which can be calculated in terms of cohesion (c) and internal friction angle (ϕ) for different asphalt mixtures. Predictive equations for c and ϕ were first developed from laboratory testing at a reference temperature of 50°C using multiple regression analyses considering asphalt binder, aggregate and volumetric properties of different asphalt mixes. The predictive c and ϕ equations were found to have correlation coefficients of 0.87 and 0.86 respectively. The rutting model considers the number of load cycles (N), shear strength ratio, temperature and load duration as main parameters of the permanent strain wherein the coefficients were determined using tri-axial compressive strength and repeated load permanent deformation testing. It was calibrated using field rutting data from twenty-six Westrack pavement sections. Moreover, the rutting model was validated using field performance data obtained from Korean national highways’ long term pavement performance database. It was found from the validation that the model can accurately estimate rut depths under varying load and environmental conditions in the fields.

Management scheme of road pavements considering heterogeneous multiple life cycles changed by repeated maintenance work

Road agencies provide maintenance work to serve a satisfactory level of road services to the public. However, as time goes on, pavement structure deteriorates for many reasons. Since repeated maintenance work upon deteriorated pavement structures can accelerate the deterioration speed, the pavements require periodic reconstruction work to recover original integrity. However, in the real world, it is difficult to carry out such a high level of maintenance work due to insufficient budgets, and no evidence for a guarantee of better economic efficiency. To support decision making in asset management, this study tries to define changing pavement performance by repeated maintenance work with empirical data. As an analytical tool, mixed hazard model with hierarchical Bayesian estimation method was applied. With the results, a best maintenance scheme on reconstruction timing was suggested by life cycle cost analysis. For the empirical study, a maintenance history data on Korean national highways, accumulated from 1965, was applied. The analysis procedures and results of this paper could be a good reference to build much realistic long-term maintenance strategy and reasonable budget allocation. In addition, the mixed hazard model with the hierarchical Bayesian estimation method is expected to be a useful tool in solving problems with heterogeneous population sampling, and in finding best practice and gaps among competitive alternatives.

Stochastic Forecasting of Life Expectancies Considering Multi-Maintenance Criteria and Localized Uncertainties in the Pavement-Deterioration Process

Abstract Understanding the deterioration characteristics of highway pavements is an essential factor for developing better maintenance strategies, as well as estimating mid- to long-term budget demands. In general, estimation results regarding life expectancy have often been represented with a constant in deterministic manners. However, in reality, these results do not correspond with real life spans because of the severe uncertainties in the pavement-deterioration process and various maintenance criteria. For these reasons, in this paper, the real life expectancy was assessed in a stochastic manner with the Bayesian Markov hazard model, which is suitable for estimating localized uncertainties in the progress of deterioration based on various deterioration indices. On the basis of this model, we have developed a new methodology that can determine the joint distribution of total maintenance demands by aggregating multiple deterioration processes using the concept of “safety-hazard zones.” In addition, we developed a method for disaggregating the joint distribution by maintenance types to mitigate the conventional problems associated with the probabilistic budget estimation approach. For our empirical study, we used data from the Korean national highways from 2007 to 2011 concerning pavement conditions, including crack, rut depth, and international roughness index (IRI). The results of this study are meaningful in that they establish the ideal conditions required for the Bayesian Markov hazard model to forecast the deterioration of pavement, allowing the incorporation of a practical viewpoint into an academic approach under the concept of asset management.

Life Cycle Cost Analysis on pavement inspection intervals considering maintenance work delay

Inspection strategy has a significant effect on the entire working process of pavement management. Most countries or road agencies casually define their inspection intervals without careful consideration. Moreover, most conventional pavement management analysis models do not treat inspection strategy as an important issue. Thus, with an economic analysis of inspection intervals, this paper aims to give a wider publicity to the importance of inspection strategy. An improved LCCA (Life Cycle Cost Analysis) model has been developed as an analytical tool. This model can take delays into consideration, as well as estimate various LCC contents and decision indicators. Both the hypothesis and the developed LCCA model have been empirically tested with pavement data from Korean National Highways. As a result, we can confirm that 1) the inspection interval affects overall management activities and LCCA results, 2) the suggested method shows an improvement in estimation results which consider the effects of delay in maintenance, and 3) either a 2 year or 3 year interval is the most beneficial interval, from both engineering and economical perspectives. In conclusion, the inspection interval needs to be carefully determined by analysis works. It is potentially a critical index that will improve current management strategy by in-house efforts.

Application of Bayesian estimation method with Markov hazard model to improve deterioration forecasts for infrastructure asset management

The heart of asset management systems for road infrastructure is the deterioration forecasting model. It provides the most fundamental information for better asset management. So far, there are many practices to build a reliable forecasting model using inspection data in conjunction with statistical theories. In many applications, however, an inadequate stock of inspection data or difficulty in applying sophisticated statistical methods have often been serious obstacles. As a solution, this paper suggests applying the Bayesian estimation method with a multi-state exponential hazard Markov chain model for simple and reliable deterioration forecasting for infrastructure. The main contents of this paper are an introduction of the model’s framework combining Markov chain, hazard theory, and Bayesian estimation method, and a demonstration of its practical application with an empirical study. The empirical study was conducted with time-series inspection data of pavement from the Korean National Highways. The estimation results from the suggested method would be useful for improving the current pavement maintenance strategy for Korean National Highways. However, the most important message of this paper is that the framework of the Bayesian Markov hazard model could be the best model to use for other civil infrastructure that has gradual changes in condition. The great advantages from the Bayesian estimation method may facilitate development of customized asset management systems.

K 최대근접이웃 방법을 이용한 통행시간 예측에 대한 연구

Travel-time is considered the most typical and preferred traffic information for intelligent transportation systems(ITS). This paper proposes a real-time travel-time prediction method for a national highway. In this paper, the K-nearest neighbor(KNN) method is used for travel time prediction. The KNN method (a nonparametric method) is appropriate for a real-time traffic management system because the method needs no additional assumptions or parameter calibration. The performances of various models are compared based on mean absolute percentage error(MAPE) and coefficient of variation(CV). In real application, the analysis of real traffic data collected from Korean national highways indicates that the proposed model outperforms other prediction models such as the historical average model and the Kalman filter model. It is expected to improve travel-time reliability by flexibly using travel-time from the proposed model with travel-time from the interval detectors.

도로포장자산관리를 위한 한국형 생애주기비용 모형 개발

도로관리는 도로의 건설과 동시에 반 영구적으로 지속하여야 하는 행위로써, 막대한 예산이 소요됨은 물론 국가 경제 및 국민의 이동과 안전에 영향을 미치는 중요한 요소이다. 이와 관련된 전략은 당연히 객관적인 정보 하에 수립되어야 하며, 그를 뒷받침 하는 의사결정시스템은 도로투자 효과에 대한 사회적 편익을 보다 현실적이고 다양한 측면에서 고려하여 개발될 필요가 있다. 이에 본 연구에서는 관리자비용은 물론 도로포장 상태를 함수로 하여 도로이용자 및 사회환경비용을 예측할 수 있는 한국형 생애주기비용모형을 개발하고자 하였다. 제시된 모형은 우리나라 국도 실정에 맞추어 개발되었으며, 결과의 공신력을 확보하기 위해 정부(국토교통부)가 제시하는 투자평가지침을 가능한 참조 하되 포장관리분야의 실정에 맞지 않거나 개선의 여지가 있는 사항들에 대해서는 추가, 대체, 부분수정을 통해 개발되었다. 이러한 자기화 된 시스템을 개발하고자하는 시도는 그간 상용화된 분석프로그램에 의존하던 소극적 도로자산관리를 탈피함은 물론, 도로관리로 인한 사회적 편익에 대한 인식개선에 중요한 첫걸음이 될 것이다. Road pavement management is an important activity that affects to national economy, movement and safety of people, and also demands huge amount of budget. Therefore, its management strategy must be established under objective information. In addition, decision support system that produces the management strategy needs to consider practical benefits from various aspects. Considering these aspects, this paper aimed to develop a customized Korean life cycle cost analysis model estimating various effects on road users and socio-environmental costs based on pavement condition. The suggested LCCA model focused on Korean national highway, and tried to adopt a national guideline recommended by Korean government for securing credibility of estimation results. In the development processes, some of the suggestions that do not fit well in the situations of pavement management field were added, altered, or partially modified. These attempts to develop customized asset management system would be an important step to break away from passive attitudes relying on ready-made software, but also to improve awareness about the social benefits from the better maintenance strategy.

통계기법을 활용한 포장파손자료의 잠재오차 보정

성공적인 자산관리시스템의 도입은 풍부하고도 신뢰할 수 있는 데이터와 함께 시작된다. 그러나 현실에서는 알려지지 않은 다양한 원인에서 비롯되는 관측 오차들은 항상 존재하기 마련이다. 이는 관리자의 유지보수활동을 방해하며 파손과정이나 생애주기비용 예측결과의 신뢰성에도 악영향을 미친다. 무엇보다도 관리자가 자신의 손으로 직접 조사한 자료를 믿지 못하는 모순된 결과를 낳는다. 이런 현상은 도로포장관리분야에서 특히나 심각하다. 도로관리자들은 이러한 사실에 대해 충분히 인식하고 있으나, 현실적으로 오차 없는 조사를 수행한다는 것은 하나의 큰 도전이라고도 할 수 있다. 이러한 점들을 고려하여 본 연구에서는 포장표면 조사자료에 포함된 오류 및 잠재오차를 보정하기 위한 통계적 방법론을 제시하고자 하였다. 방법론으로는 오차의 구조 및 신뢰도를 고려할 수 있도록 확률분포이론에 근거한 두 가지 기법(샘플기준법과 분포기준법)을 제안하였다. 제안된 방법론은 국도에서 수집된 포장상태자료들을 활용하여 적용 가능성을 살펴보았으며, 그 결과 눈에 보이는 오류들만을 제거하는 일반적인 방법론으로는 포장자료에 포함된 불확실성을 충분히 고려할 수 없음을 확인하였다. 제안된 방법론은 도로자산관리에 필요한 다양한 분석의 신뢰성을 개선하는데 유용하게 활용될 수 있을 것이다. Successful implementation of infrastructure asset management system can be started with rich and reliable data. However, measurement errors in the data have always existed in the real world caused for many unknown reasons. It disturbs maintenance activities of agencies, and makes negative effects to reliability of research results on forecasting deterioration process and life cycle cost. Above all, it makes a contradiction that road agencies cannot believe their inspection data surveyed by their hands. It is particularly serious in the road pavement management field. Although road agencies are well recognized the fact, inspecting without measurement error would be a great challenge. Considering the facts, this paper aimed to suggest statistical error processing methods to correct latent error included in pavement surface inspection data. As alternatives, this paper suggested two methods based on probability distribution to consider structure of error and reliability of the data. The suggested methods were empirically tested by using pavement inspection data from Korean National Highway. As the result, this paper confirmed that conventional error processing that just removes only visible errors is not enough to cover uncertainty in pavement deterioration process. The suggested methods would be useful for improving reliability of analysis results required for road infrastructure asset management.

도로포장 유지보수 전략에 따른 기대수명과 보수비용산정

도로포장은 충족되어야 하는 서비스 수준을 유지하기 위해 반복적인 유지보수를 필요로 한다. 그러나 노후화된 하부구조와 반복적인 유지보수는 포장의 파손속도를 가속화시키기도 하며, 이는 한정된 예산의 효율성을 저해하는 요소가 될 수 있다. 따라서 본래의 기능을 유지하기 위해 도로의 재포장이 주기적으로 요구된다. 특히, 국도는 그 건설수요가 한계점에 다다랐으며, 노후로 인해 재포장 및 유지관리의 필요가 점점 증가하고 있는 시점이다. 그러나 도로관리자들은 예산의 한계로 이러한 노후포장에 대해 재포장 및 효율적인 유지관리를 시행하기에 많은 어려움을 겪고 있다. 이는 의사결정에 필요한 장기적인 유지보수 전략의 부재 때문이라 할 수 있다. 이에 본 논문은 반복적인 유지보수로 인한 포장의 상태변화를 고려한 유지보수 전략을 도출하여 관리자들의 의사결정에 도움을 주고자 하였다. 분석을 위해 포장관리시스템(PMS)이 도입된 1986년부터 장기간 누적된 국도의 유지보수 이력데이터를 활용하였으며, 방법론으로는 유지보수 횟수에 따른 수명분포 도출 및 위험률(hazard) 함수의 변화과정을 분석한 후, 이 결과를 근거로 다양한 유지보수 대안들에 대해 중장기 유지보수비용을 산정하였다. 이를 위해 포장파손과정의 불확실성을 고려하고, 도로관리자들에게 보다 실용적인 정보를 제공하기 위해 확률론적 방법(몬테카를로기법)을 추가로 도입하였다. 또한, 신뢰성 이론을 활용하여 유지보수에 대한 품질보증과 관련된 정보도 도출하고자 하였다. 이러한 정보는 장기유지보수전략 수립에 중요한 정보로 활용할 수 있다. Road pavement requires repetitive maintenance works to maintain satisfactory service level to the public. However, the repetitive maintenance works upon deteriorated pavement structure make negative effects to deterioration speed. It often leads to inefficient use of limited budget. For that reason, the pavements require reconstruction work to recover their original performance. Recently, construction demands in the Korean national highway have already been reached to maximum level, and the aged pavements start to demand much more reconstruction works. However, in the real world, road agencies have often been confused when they determine maintenance design for such aged road sections due to budget constraint. It is because there is no reliable long-term maintenance strategy that supports their decision making. To support their decision making, this paper aimed to suggest the best maintenance strategy considering changing process of pavement performance by repetitive maintenance works. As an analysis method, probability distribution and hazard function to estimate the life expectancy were adopted, and then the results were used for long-term life cycle cost analysis with deterministic or Monte-Carlo method under various scenarios. As an empirical study, the Korean national highway data that has long-maintenance history data since 1986 has been applied. Last, this paper considered quality assurance of maintenance work to improve maintenance quality. These could be important information as a part of long-term maintenance strategy of pavement.

신뢰도 분석을 통한 고속도로 교량의 바닥판 잔존 수명 예측

Korean national highway has been increased 2 times more for the past ten years because of many highway geometric improvements and new routes since 2000. According to the reasons, maintenance cost has been increased continuously. Deterioration of concrete bridge decks caused by asphalt pavement deformation occupies a high proportion of overall bridge management budget. The number of current highway bridges has reached over 7,800 in 2011, and It is difficult to determine to some future budget. This study predicted the remaining life of concrete bridge decks using the reliability analysis based on Weibull distribution. and The expected future maintenance cost was estimated.