• International Journal of Highway Engineering
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• v.13 no.4
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• pp.29-40
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• 2011

Warm-Mix Asphalt(WMA) technologies have been developed since 15 years ago, which are internationally and domestically concerned with energy-saving and environmental-friendly technologies in public and private sectors of highway engineering. The performance of asphalt mass is traditionally evaluated by the penetration, viscosity, or Superpave tests. This research, however, is focused more on the properties depending on asphalt film thicknesses instead of evaluating asphalt mass behaviors by those tests. For this approach, a new testing protocol and analysis method are described by the bonding properties on each film thickness. This testing method and analysis tool are borrowed from those of DSR Moisture Damage test and applied by using ARES. The analysis results indicate that there is a limit film thickness between $200{\mu}m$ and $400{\mu}m$, which causes significant changes of the properties. In addition, the results show that the property changes of Hot-Mix Asphalt(HMA) and WMA on the limit film thickness are also different. Therefore, it is suggested that the properties on thin film thicknesses between $200{\mu}m$ and $400{\mu}m$ should be considered in order to evaluate WMA properly.

• International Journal of Highway Engineering
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• v.16 no.3
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• pp.43-50
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• 2014

PURPOSES : The dynamic modulus can be determined by applying the various theories from the Impact Resonance Testing(IRT) Method. The objective of this paper is to determine the best theory to produce the dynamic modulus that has the lowest error as the dynamic modulus data obtained from these theories(Complex Wave equation Resonance Method related to either the transmissibility loss or not, Dynamic Stiffness Resonance Method) compared to the results for dynamic modulus determined by using the Universal Testing Machine. The ultimate object is to develop the predictive model for the dynamic modulus of a Linear Visco-Elastic specimen by using the Complex Wave equation Resonance Method(CWRM) came up for an existing study(S. O. Oyadiji; 1985) and the Optimization. METHODS : At the destructive test which uses the Universal Testing Machine, the dynamic modulus results along with the frequency can be used for determining the sigmoidal master curve function related to the reduced frequency by applying Time-Temperature Superposition Principle. RESULTS : The constant to be solved from Eq. (11) is a value of 14.13. The reduced dynamic modulus obtained from the IRT considering the loss factor related to the impact transmissibility has RMSE of 367.7MPa, MPE of 3.7%. When the predictive dynamic modulus model was applied to determine the master curve, the predictive model has RMSE of 583.5MPa, MPE of 3.5% compared to the destructive test results for the dynamic modulus. CONCLUSIONS : Because we considered that the results obtained from the destructive test had the most highest source credibility in this study, the dynamic modulus data obtained respectively from DSRM, CWRM were compared to the results obtained from the destructive test by using th IRT. At the result, the reduced dynamic modulus derived from DSRM has the most lowest error.

• Microbiology and Biotechnology Letters
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• v.30 no.1
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• pp.8-14
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• 2002

Pseudomonas sp. S-47 is capable of degrading 4-chlorobenzoate to produce 5-chloro-2-hydroxymuconic semialdehyde (5C-2HMS) by the enzymes encoding by xylXYZLTE cluster. In this study, the resulting 5C-2HMS was confirmed to be transformed to 5-chloro-2-hydroxymuconic acid (5C-2HMA) by 5C-2HMS dehydrogenase. The xylG gene encoding 5C-2HMS dehydrogenase was cloned from the chromosomal DNA of strain S-47. The nucleotide sequence of xylG showed to be composed of 1,600 base pairs with ATG initiation and TGA termination codons. A deduced amino acid sequence of the 5C-2HMS dehydrogenase (XylG) exhibited 98％, 93％, and 89％ identity with those of the dehydrogenases from P. putida mt-2, P. putida G7, and Pseudomonas sp. CF600, respectively.

• International Journal of Highway Engineering
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• v.11 no.1
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• pp.1-12
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• 2009

A new mix design procedure for cold in-place recycling using foamed asphalt (CIR-foam) has been developed for Iowa Department of Transportation. Some strengths and weaknesses of the new mix design parameters were considered and modified to improve the laboratory test procedure. Based on the critical mixture parameters identified, a new mix design procedure was developed and validated to establish the properties of the CIR-foam mixtures. As part of the validation effort to evaluate a new CIR-foam mix design procedure, dynamic moduli of CIR-foam mixtures made of seven different reclaimed asphalt pavement (RAP) materials collected throughout the state of Iowa were measured and their master curves were constructed. The main objectives of this study are to provide: 1) standardized testing procedure for measuring the dynamic modulus of CIR-foam mixtures using new simple performance testing (SPT) equipment; 2) analysis procedure for constructing the master curves for a wide range of RAP materials; and 3) impacts of RAP material characteristics on the dynamic modulus. Dynamic moduli were measured at three different temperatures and six different loading frequencies and they were consistent among different RAP sources. Master curves were then constructed for the CIR-foam mixtures using seven different RAP materials. Based upon the observation of the constructed master curves, dynamic moduli of CIR-foam mixtures were less sensitive to the loading frequencies than HMA mixtures. It can be concluded that at the low temperature, the dynamic modulus is affected by the amount of fines in the RAP materials whereas, at the high temperature, the dynamic modulus is influenced by the residual binder characteristics.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.580-586
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• 2021

The CAT methodology is a numerical analysis technique using CAE. Recently, a methodology of applying artificial intelligence techniques to a simulation has been studied. A previous study compared the deformation results according to the injection molding process using a machine learning technique. Although MLP has excellent prediction performance, it lacks an explanation of the decision process and is like a black box. In this study, data was generated using Autodesk Moldflow 2018, an injection molding analysis software. Several Machine Learning Algorithms models were developed using RapidMiner version 9.5, a machine learning platform software, and the root mean square error was compared. The decision-tree showed better prediction performance than other machine learning techniques with the RMSE values. The classification criterion can be increased according to the Maximal Depth that determines the size of the Decision-tree, but the complexity also increases. The simulation showed that by selecting an intermediate value that satisfies the constraint based on the changed position, there was 7.7% improvement compared to the previous simulation.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5D
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• pp.783-796
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• 2006

Rut-resistance pavement is adopted to prevent pavement from plastic deformation since 1998. The objective of this paper is to investigate performance and economic efficiency between rut-resistance pavement and conventional hot-mix asphalt(HMA) on national highway. The pavement deterioration models incorporated in HDM-4 have been calibrated and adapted to local road conditions based on observed pavement rut-depth data. Based on calibration result of HDM-4, the economic evaluation including road agency cost and user cost is performed for 34 road pavement sections. Furthermore, we presented optimal timing for maintenance and performance levels subject to different budget. We found that rut-resistance pavement is performing better than conventional hot-mix asphalt in most road sections. Furthermore, we confirmed that the application of HDM-4 is useful for pavement project planning and evaluation. More investigation is needed to enlarge the scope of the pavement data and to explore more deeply socio-environmental cost and delay cost.