• Title/Summary/Keyword: 도로공용성예측모델

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Methodology of a Probabilistic Pavement Performance Prediction Model Based on the Markov Process (확률적 포장 공용성 예측모델 개발 방법론)

  • Yoo, Pyeong-Jun;Lee, Dong-Hyun
    • International Journal of Highway Engineering
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    • v.4 no.4 s.14
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    • pp.1-12
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    • 2002
  • Pavement Management System has a special purpose that the rehabilitation strategy applied on pavement should be executable in view of technical and economical point after new pavement open to the traffic. To achieve that purpose, a reliable pavement performance prediction model should be embeded in the system. The object of this study is to develop a probabilistic pavement performance prediction model for evaluating asphalt pavements based on the Markov chain concept. In this paper, methodology of the Markov chain modeling principle is explained, and the application of this model to asphalt pavement is described. As the results, transition matrics for predicting asphalt pavement performance are obtained, and also performance life is estimated quantitatively by this system.

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LCCA-embedded Monte Carlo Approach for Modeling Pay Adjustment at the State DOTs (도로공사에서 생애주기비용을 사용한 지급조정모델 개발에 관한 연구)

  • Choi Jae-ho
    • Proceedings of the Korean Institute Of Construction Engineering and Management
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    • autumn
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    • pp.72-77
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    • 2002
  • The development of a Pay Adjustment (PA) procedure for implementing Performance-related Specifications (PRS) is known to be a difficult task faced by most State Highway Agencies (SHAs) due to the difficulty in such areas as selecting pay factor items, modeling the relationship between stochastic variability of pay factor items and pavement performance, and determining an overall lot pay adjustment. This led to the need for an effective way of developing a scientific pay adjustment procedure by incorporating Life Cycle Cost Analysis (LCCA) embedded Monte Carlo approach. In this work, we propose a prototype system to determine a PA specifically using the data in the pavement management information systems at Wisconsin Department of Transportation (WisDOT) as an exemplary to other SHAs. It is believed that the PRS methodology demonstrated in this study can be used in real projects by incorporating the more accurate and reliable performance prediction models and LCC model.

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Development of Pavement Distress Prediction Models Using DataPave Program (DataPave 프로그램을 이용한 포장파손예측모델개발)

  • Jin, Myung-Sub;Yoon, Seok-Joon
    • International Journal of Highway Engineering
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    • v.4 no.2 s.12
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    • pp.9-18
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    • 2002
  • The main distresses that influence pavement performance are rutting, fatigue cracking, and longitudinal roughness. Thus, it is important to analyze the factors that affect these three distresses, and to develop prediction models. In this paper, three distress prediction models were developed using DataPave program which stores data from a wide variety of pavement sections In the United States. Also, sensitivity studies were conducted to evaluate how the input variables impact on the distresses. The result of sensitivity study for the prediction model of rutting showed that asphalt content, air void, and optimum moisture content of subgrade were the major factors that affect rutting. The output of sensitivity study for the prediction model of fatigue cracking revealed that asphalt consistency, asphalt content, and air void were the most influential variables. The prediction model of longitudinal roughness indicated asphalt consistency, #200 passing percent of subgrade aggregate, and asphalt content were the factors that affect longitudinal roughness.

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Development of Prediction Method for Highway Pavement Condition (포장상태 예측방법 개선에 관한 연구)

  • Park, Sang-Wook;Suh, Young-Chan;Chung, Chul-Gi
    • International Journal of Highway Engineering
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    • v.10 no.3
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    • pp.199-208
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    • 2008
  • Prediction the performance of pavement provides proper information to an agency on decision-making process; especially evaluating the pavement performance and prioritizing the work plan. To date, there are a number of approaches to predict the future deterioration of pavements. However, there are some limitation to proper prediction of the pavement service life. In this paper, pavement performance model and pavement condition prediction model are developed in order to improve pavement condition prediction method. The prediction model of pavement condition through the regression analysis of real pavement condition is based on the probability distribution of pavement condition, which set to 5%, 15%, 25% and 50%, by condition of the pavement and traffic volume. The pavement prediction model presented from the behavior of individual pavement condition which are set to 5%, 15%, 25% and 50% of probability distribution. The performance of the prediction model is evaluated from analyzing the average, standard deviation of HPCI, and the percentage of HPCI which is lower than 3.0 of comparable section. In this paper, we will suggest the more rational method to determine the future pavement conditions, including the probabilistic duration and deterministic modeling methods regarding the impact of traffic volume, age, and the type of the pavement.

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Development of Rutting Model for Asphalt Mixtures using Laboratory and Accelerated Pavement Testing (실내 및 포장가속시험를 이용한 아스팔트 혼합물의 소성변형 모형 개발)

  • Lee, Sang-Yum;Lee, Hyun-Jong;Huh, Jae-Won;Park, Hee-Mun
    • International Journal of Highway Engineering
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    • v.10 no.4
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    • pp.79-89
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    • 2008
  • The pavement performance model is the most important factor to determine the pavement life in the mechanistic-empirical pavement design guide (MEPDG). As part of Korean Pavement Research Program (KPRP), the Korean Pavement Design Guide (KPDG) is currently being developed based on mechanistic-empirical principle. In this paper, the rutting prediction model of asphalt mixtures, one of the pavement performance model, has been developed using triaxial repeated loading testing data. This test was conducted on various types of asphalt mixtures for investigating the rutting characteristics by varying with the temperature and air void. The calibration process was made for the coefficients of rutting prediction model using the accelerated pavement testing data. The accuracy of prediction model can be increased when by considering the effect of individual rutting properties of materials rather than shear stresses with depths.

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Selection of Long-Term Pavement Performance Sections for Development of Distress Prediction Model in National Asphalt Pavement (국도 아스팔트 포장 파손예측모델 개발을 위한 장기 관측 구간 선정에 관한 연구)

  • Kwon, Soo-Ahn;Yoo, Pyeong-Joon;Kim, Ki-Hyun;Cho, Yoon-Ho
    • International Journal of Highway Engineering
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    • v.4 no.1 s.11
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    • pp.123-134
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    • 2002
  • Special pavement test sections were selected to develop a distress prediction model on asphalt pavement of National Highway. Experimental design was conducted for the selection of LTPP sections on in-service pavement(new and overlaid pavement) using several variables affecting pavement performance. Preliminary sections that satisfied the design template were chosen from the national highway database, and final selection was fixed through field inspection. The number of monitoring section is 95 including 47 overlaid pavement. A pavement distress data such as crack and rutting were collected for two years. An interim pavement performance analysis was peformed to show feasibility of performance monitoring program. Data related pavement such as traffic, weather, material characteristic and crack etc. should be collected for next project years and distress prediction model will be developed through the statistical analysis.

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Methodology to Predict Service Lives of Pavement Marking Materials (도로 차선 재료의 공용수명 예측방법)

  • Oh, Heung-Un;Lee, Hyun-Seock;Jang, Jung-Hwa;Kang, Jai-Soo
    • International Journal of Highway Engineering
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    • v.10 no.4
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    • pp.151-159
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    • 2008
  • Performances of retroreflectivity vary place to place, according to traffic volumes and time lengths after striping, depending on pavement marking materials and colors. The present paper uses the nation wide data of retroreflectivity, which has been collected from freeways and then tries to develop the regression curve setting traffic volume and service life as independent variables and retroreflectivities as dependent variables. The DB system includes two year's measurement in $2005{\sim}2006$ over Korean freeway pavement marking at an interval of three months for the period. The mobile measurement system, a laserlux, was employed for the purpose. The DB has provided a lot of information about materials and performance of the specific pavement marking such as geometric features, traffic volumes, material characteristics and the installation date. This study provides the comparison of pavement marking performances under diversified conditions. Based on accumulated pavement marking performances, this study provides performance curves based on the diversified factors. The goal of the retroreflectivity modeling is to develop equations that can be used to estimate an average retroreflectivity of pavement markings as a function time since application and traffic volume. After representing the variation of retroreflectivities and estimating regression curves by linear, exponential, logarithmic and power function, the regression curve which had the highest coefficient of determination and the value similar to the last field measurement was regarded as the retroreflectivity decay model. As a result of verification, the decay model showed the signification within the 90% confidence level and especially showed the clear relation with field data according to increase of cumulative vehicle exposure. Accordingly, these models can be used to determine service lives, retroreflectivity degradation rates, and retroreflectivity of new markings.

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Predicting Highway Concrete Pavement Damage using XGBoost (XGBoost를 활용한 고속도로 콘크리트 포장 파손 예측)

  • Lee, Yongjun;Sun, Jongwan
    • Korean Journal of Construction Engineering and Management
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    • v.21 no.6
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    • pp.46-55
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    • 2020
  • The maintenance cost for highway pavement is gradually increasing due to the continuous increase in road extension as well as increase in the number of old routes that have passed the public period. As a result, there is a need for a method of minimizing costs through preventative grievance Preventive maintenance requires the establishment of a strategic plan through accurate prediction old Highway pavement. herefore, in this study, the XGBoost among machine learning classification-based models was used to develop a highway pavement damage prediction model. First, we solved the imbalanced data issue through data sampling, then developed a predictive model using the XGBoost. This predictive model was evaluated through performance indicators such as accuracy and F1 score. As a result, the over-sampling method showed the best performance result. On the other hand, the main variables affecting road damage were calculated in the order of the number of years of service, ESAL, and the number of days below the minimum temperature -2 degrees Celsius. If the performance of the prediction model is improved through more data accumulation and detailed data pre-processing in the future, it is expected that more accurate prediction of maintenance-required sections will be possible. In addition, it is expected to be used as important basic information for estimating the highway pavement maintenance budget in the future.

Development of Rutting Prediction Model of Flexible Pavement using Repetitive Axial Loading Test (반복 축하중 시험을 이용한 연성포장의 소성변형 예측모델 개발)

  • Kim, Nakseok
    • Journal of the Society of Disaster Information
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    • v.13 no.4
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    • pp.491-498
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    • 2017
  • The primary objective of this research is to develop a rutting performance prediction model of flexible pavement. Extensive laboratory testings were conducted to achieve the objective. A new test method employing repetitive axial loading with confinement was also adopted to estimate the rutting performance of asphalt concrete in the research. The rutting prediction model employes a layer-strain theory. The required rutting coefficients for the prediction model were determined through the laboratory rutting characterizations of the asphalt concrete layer materials. Within the limits of this study, a laboratory rutting prediction model of flexible pavement using repetitive axial loading test was presented. It is noted that the developed rutting prediction model simulates propery the behaviors of flexible pavement layer materials.

Evaluation of Permanent Deformation Characteristics in Crushed Subbase Materials Using Shear Stress Ratio and Large Repeated Triaxial Compression Test (대형반복삼축시험과 전단응력비 개념을 이용한 쇄석 보조기층의 영구변형 특성평가)

  • Lim, Yu-Jin;Kim, In-Tae;Kwak, Ki-Heon
    • International Journal of Highway Engineering
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    • v.13 no.4
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    • pp.41-50
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    • 2011
  • It is well-known that pavement is easily damaged by several factors including permanent deformation and fatigue crack, causing service life of the pavement to be shorter than expected. It is very important to predict amount of permanent deformation for designing pavement and developing design method of pavement. A new model of permanent deformation of pavement materials based on concept of shear stress ratio has been proposed because the lower pavement materials are highly affected by shear strength of the material. In this study a large repetitive triaxial load test has been adapted for performing test of permanent deformation of crushed subbase materials. The test procedure which includes concept of shear stress ratio has been newly developed. Several important model parameters can be obtained from the test that can be used for making correct permanent deformation model of the material.