• 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.

• 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.

• 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.

• 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.

• International Journal of Highway Engineering
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• v.18 no.1
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• pp.91-97
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• 2016

PURPOSES : The objective of this paper is to develop a pavement performance model based on the Bayesian algorithm, and compare the measured and predicted performance data. METHODS : In this paper, several pavement types such as SMA (stone mastic asphalt), PSMA (polymer-modified stone mastic asphalt), PMA (polymer-modified asphalt), SBS (styrene-butadiene-styrene) modified asphalt, and DGA (dense-graded asphalt) are modeled in terms of the performance evaluation of pavement structures, using the Bayesian algorithm. RESULTS : From case studies related to the performance model development, the statistical parameters of the mean value and standard deviation can be obtained through the Bayesian algorithm, using the initial performance data of two different pavement cases. Furthermore, an accurate performance model can be developed, based on the comparison between the measured and predicted performance data. CONCLUSIONS : Based on the results of the case studies, it is concluded that the determined coefficients of the nonlinear performance models can be used to accurately predict the long-term performance behaviors of DGA and modified asphalt concrete pavements. In addition, the developed models were evaluated through comparison studies between the initial measurement and prediction data, as well as between the final measurement and prediction data. In the model development, the initial measured data were used.

• Proceedings of the Korea Concrete Institute Conference
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• 1996.04a
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• pp.363-369
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• 1996

PMS(Pavement Management System) is the effective and efficient decision making system to provide pavements in an acceptable condition at the lowest life-cycle cost. As the highway system become larger, the necessity of the PMS in increasing. As of December 1995, the 3rd stage of PMS project was completed. The accomplishment of the research work can be itemized to the followings : $\bullet$ Calibration of PMS submodules (1) Pavement Condition Evaluation Model (2) Pavement Distress Prediction Model (3) Pavement Performance Prediction Mode (4) Selection of Pavement Rehabilitation Criteria (5) Optimization Technique for PMS Economic Analysis $\bullet$ Development of Computer Program to Implement PMS Logic $\bullet$ A Study to Implement the Automized Pavement Condition Survey Equipment to PMS $\bullet$ PMS Test Run $\bullet$ Development of PMS Operation Guideline $\bullet$ The 2nd Pavement Condition Survey for Long-Term Pavement Performance Monitoring.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.596-601
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• 2013

In the current pavement construction practice, the state agencies traditionally determine the quality of the as-constructed pavement mix based on individual mixture material parameters (e.g., air voids, cement or asphalt content, aggregate gradation, etc.) and consider these parameters as key variables to influence payment schedule to the contractors and the present and future quality of the as-constructed mixture. A set of empirically pre-determined pay adjustment schedule for each parameter that was differently developed and being used by the individual agencies is then applied to a given project, in order to judge whether each parameter conforms to the designated specifications and consequently the contractor may either be rewarded or penalized in accordance with the payment schedule. With an improved quality assurance system, the Performance Related Specification, the individual parameters are not utilized as a direct judgment factor; rather, they become independent variables within a performance prediction function which is directly used to predict the performance. The quantified performance based on the prediction model is then applied to evaluate the pavement quality. This paper presents the brief history of the quality assurance in asphalt pavement construction including the Performance Related Specifications, statistical performance models in terms of fatigue and rutting distresses, as an example of the performance prediction models, and envisions the possibilities as to how this Performance Related Specification could be utilized in other infrastructures construction quality assurance.

• 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.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1043-1050
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• 2022

Markov chains and Monte Carlo Simulation were applied to account for the probabilistic nature of pavement deterioration over time using data collected in the field. The primary purpose of this study was to evaluate pavement network performance of Western Australia (WA) by applying the existing pavement management tools relevant to WA road construction networks. Two approaches were used to analyze the pavement networks: evaluating current pavement performance data to assess WA State Road networks and predicting the future states using past and current pavement data. The Markov chains process and Monte Carlo Simulation methods were used to predicting future conditions. The results indicated that Markov chains and Monte Carlo Simulation prediction models perform well compared to pavement performance data from the last four decades. The results also revealed the impact of design, traffic demand, and climate and construction standards on urban pavement performance. This study recommends an appropriate and effective pavement engineering management system for proper pavement design and analysis, preliminary planning, future pavement maintenance and rehabilitation, service life, and sustainable pavement construction functionality.

• International Journal of Highway Engineering
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• v.7 no.4 s.26
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• pp.1-8
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• 2005

Permanent Deformation is one of the most important load-related pavement distresses in asphalt pavements. The Korean Pavement Design Guide currently being developed adopted the mechanistic-empirical approach and needed the pavement distress prediction models. This study intends to develop the model for prediction of permanent deformation in the asphalt layer and estimate the pavement performance. The objectives of this paper are to figure out the factors affecting the permanent deformation and then develop the permanent deformation prediction model for asphalt mixtures. The repeated triaxial load test was Performed on the 19mm dense graded asphalt mixture with variation of temperature and air void. Results from the laboratory tests showed that temperature and air void in asphalt mixtures have significantly influenced on the factors in prediction model. The permanent deformation prediction model for 19m dense grade asphalt mixtures has been developed using the multiple regression approach and validated the proposed permanent deformation prediction model.