• International Journal of Highway Engineering
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• v.19 no.6
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• pp.83-95
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• 2017

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.

• Journal of the Korean Institute of Landscape Architecture
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• v.34 no.1 s.114
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• pp.21-36
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• 2006

This study was undertaken to analyze the effects of pavement thermal properties and water retention characteristics on the surface temperature of the gray permeable cement concrete pavement during the summer. Following is a summary of major results. 1) The hourly surface temperature of pavement could be well predicted with a heat transfer model program that incorporated the input data of major meteorological variables including solar radiation, atmospheric temperature, dew point, wind velocity, cloudiness and the evaporation rate of the pavements predicted by the time domain reflectometry (TDR) method. 2) When the albedo was changed to 0.5 from an arbitrary starting condition of 0.3, holding other variables constant, the peak surface temperature of the pavement dropped by 11.5%. When heat capacity was changed to $2.5\;kJm^{-3}K^{-1}\;from\;1.5\;kJm^{-3}K^{-1}$, surface temperature dropped by 8.0%. When daily evaporation was changed to 1 mm from 2 mm, temperature dropped by 5.7%. When heat conductivity was changed to $2.5\;Wm^{-1}K^{-1}\;from\;1.5\;Wm^{-1}K^{-1}$, the peak surface temperature of the pavement fell by 1.2%. The peak pavement surface temperature under the arbitrary basic condition was $24.46^{\circ}C$ (12 a.m.). 3) It accordingly became evident that the pavement surface temperature can be most effectively lowered by using materials with a high albedo, a high heat capacity or a high evaporation at the pavement surface. The glare situation, however, is intensified by raising of the albedo, moreover if reflected light is absorbed into surrounding physical masses, it is changed into heat. It accordingly became evident that raising the heat capacity and the evaporative capacity may be the moot acceptable measures to improve the thermal characteristics of the pavement. 4) The sensitivity of the surface temperature to major meteorological variables was as follows. When the daily average temperature changed to $0^{\circ}C\;from\;15^{\circ}C$, holding all other variables constant, the peak surface temperature of the pavement decreased by 56.1 %. When the global solar radiation changed to $200\;Wm^{-2}\;from\;600\;Wm^{-2}$, the temperature of the pavement decreased by 23.4%. When the wind velocity changed to $8\;ms^{-1}\;from\;4\;ms^{-1}$, the temperature decreased by 1.4%. When the cloudiness level changed to 1.0 from 0.5, the peak surface temperature decreased by 0.7%. The peak pavement surface temperature under the arbitrary basic conditions was $24.46^{\circ}C$ (12 a.m.)

• Coupled systems mechanics
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• v.11 no.2
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• pp.107-119
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• 2022

The aim of the work presented in this paper is development of numerical model for prediction of temperature distribution in pavement according to the measured meteorological parameters, with introduction of non-linear heat transfer coefficient which is a function of temerature difference between the air and the pavement. Developed model calculates heat radiated from the pavement back in the air, which is an important part of the heat trasfer process in the open air surfaces. Temperature of the pavement surface, heat radiation together with many meteorological parameters were measured in series during two years in order to validate the model and calibrate model parameters. Special finite element method for temperature heat transfer towards the soil together with the time integration scheme are used to solve the governing equation. It is proved that non-linear heat transfer coefficient, which is a function of time and temperature difference between the air and the pavement, is required to decribe this phenomena. Proposed model includes heat tranfer coefficient callibration for specific climate region, through the iterative inverse procedure.

• Journal of Korean Society of Transportation
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• v.29 no.2
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• pp.123-131
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• 2011

This study proposes a model for road surface temperature prediction on basis of the heat-energy balance equation between atmosphere and road surface. The overall model is consisted of two types of modules: 1) Canopy 1 is used to describe heat transfer between soil surface and atmosphere; and 2) Canopy 2 can reflect the characteristics of pavement type. Input data used in the model run is obtained from the Korea Meteorological For model validation, the observed and predicted surface temperature data are compared using data collected on MoonEui Bridge along CheongWon-Sangju Expressway, and the comparison is made on winter and other seasons separately. Analysis results show that average difference between two temperatures lies within ${\pm}2^{\circ}C$ which is considered as appropriate from a micrometeorology point of view. The model proposed in this paper can be adopted as a useful tool in practical applications for winter maintenance. This study being a fundamental research is anticipated to be a starting point for further development of robust surface road temperature prediction algorithms.

• International Journal of Highway Engineering
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• v.3 no.1 s.7
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• pp.165-176
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• 2001

Joints in jointed concrete Pavement are designed to control against randomly occurred cracks within slabs, which may be caused by temperature or moisture variation. The advantage of these artificial cracks (joints) over naturally occurred cracks are easy access of protections, such as installation of joint seal and load transfer mechanism. The potential benefits of joint seals are to prevent infiltration of surface water through the joint into underlying soil and intrusion of incompressible materials (debris, fine size aggregate) in to the joint, which may prevent weakening of underlying soils and spallings due to excessive compressive stress, respectively. For the adequate design of joint seal, horizontal variation of joint widths (horizontal joint movements) are essential inputs. Based on long-term in-situ joint movement data of sixteen jointed concrete pavement sections in Long Term Performance Pavement Seasonal Monitoring Program (LTPP SMP), it was indicated that considerable Portion of joints showed no horizontal movements with change in temperature. This Phenomenon is called 'Joint Freezing'. Possible cause for joint freezing is that designed penetrated cracks do not occur at a joint. In this study, a model for the prediction of the ratio of freezing joints in a particular pavement sections is proposed. In addition, possible effects of joint freezing against pavement performance are addressed.

• Journal of Korean Society of Transportation
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• v.32 no.6
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• pp.686-693
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• 2014

This study presents a model that enables to predict road surface temperature using neural network theory. Historical road surface temperature data were collected from Road Weather Information System. They used for the calibration of the model. The neural network was designed to predict surface temperature after 1-hour, 2-hour, and 3-hour from now. The developed model was performed on Cheongwon-Sangju highway to test. As a result, the standard deviation of the difference of the predicted and observed was $1.27^{\circ}C$, $0.55^{\circ}C$ and $1.43^{\circ}C$, respectively. Also, comparing the predicted surface temperature and the actual data, R2 was found to be 0.985, 0.923, and 0.903, respectively. It can be concluded that the explanatory power of the model seems to be high.