• Advances in Computational Design
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• v.7 no.3
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• pp.253-279
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• 2022

This study investigated and predicted the Marshall stability of glass-fiber asphalt mix, carbon-fiber asphalt mix and glass-carbon-fiber asphalt (hybrid) mix by using machine learning techniques such as Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Forest(RF), The data was obtained from the experiments and the research articles. Assessment of results indicated that performance of the Artificial Neural Network (ANN) based model outperformed applied models in training and testing datasets with values of indices as; coefficient of correlation (CC) 0.8492 and 0.8234, mean absolute error (MAE) 2.0999 and 2.5408, root mean squared error (RMSE) 2.8541 and 3.3165, relative absolute error (RAE) 48.16% and 54.05%, relative squared error (RRSE) 53.14% and 57.39%, Willmott's index (WI) 0.7490 and 0.7011, Scattering index (SI) 0.4134 and 0.3702 and BIAS 0.3020 and 0.4300 for both training and testing stages respectively. The Taylor diagram also confirms that the ANN-based model outperforms the other models. Results of sensitivity analysis show that Carbon fiber has a major influence in predicting the Marshall stability. However, the carbon fiber (CF) followed by glass-carbon fiber (50GF:50CF) and the optimal combination CF + (50GF:50CF) are found to be most sensitive in predicting the Marshall stability of fibrous asphalt concrete.

• 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.

• 한국기계연구소 소보
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• s.9
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• pp.29-39
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• 1982

• Magazine of the Korea Institute for Structural Maintenance and Inspection
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• v.17 no.1
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• pp.30-42
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• 2013

• Magazine of the Korea Concrete Institute
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• v.11 no.4
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• pp.46-52
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• 1999

• Proceedings of the KAIS Fall Conference
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• 2000.04a
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• pp.8-8
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• 2000

• Journal of Drive and Control
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• v.16 no.4
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• pp.74-79
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• 2019

In this study, an edible fresh corn harvest testing machine was designed and manufactured. And harvesting performance was analyzed through the field test. The testing machine is of the tractor attached type. It is connected to the tractor PTO shaft to transfer power to the each part of the harvesting machine. And it harvests fresh corn by one row through the processes of cutting, stem crushing, detaching, and collecting. The performance test was performed at PTO speed (540, 750, 1050 rpm, respectively), working speed (0.1, 0.15, 0.2 m/s, respectively), and cropping cultivation (row spacing·hill spacing 70·25 cm, 70·40 cm, 90·30 cm, respectively). The performance test was repeated three times in the 15 m section. The detachment loss ratio, uncollected crop ratio, damage ratio, and harvest ratio were analyzed. As a result of the performance test, it was analyzed that the PTO speed 540 rpm, running speed of 0.1 m/s, and row spacing·hill spacing 70·40 cm were the optimal condition.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.778-804
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• 2017

Software today has become an inseparable part of our life. In order to achieve the ever demanding needs of customers, it has to rapidly evolve and include a number of changes. In this paper, our aim is to study the relationship of object oriented metrics with change proneness attribute of a class. Prediction models based on this study can help us in identifying change prone classes of a software. We can then focus our efforts on these change prone classes during testing to yield a better quality software. Previously, researchers have used statistical methods for predicting change prone classes. But machine learning methods are rarely used for identification of change prone classes. In our study, we evaluate and compare the performances of ten machine learning methods with the statistical method. This evaluation is based on two open source software systems developed in Java language. We also validated the developed prediction models using other software data set in the same domain (3D modelling). The performance of the predicted models was evaluated using receiver operating characteristic analysis. The results indicate that the machine learning methods are at par with the statistical method for prediction of change prone classes. Another analysis showed that the models constructed for a software can also be used to predict change prone nature of classes of another software in the same domain. This study would help developers in performing effective regression testing at low cost and effort. It will also help the developers to design an effective model that results in less change prone classes, hence better maintenance.

• Journal of the Korean Society for Nondestructive Testing
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• v.30 no.2
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• pp.121-125
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• 2010

The infrared thermography technology rather than traditional nondestructive methods has benefits with non-contact and non-destructive testings in measuring for the fault diagnosis of the rotating machine. In this work, condition monitoring measurements using this advantage of thermography were proposed. From this study, the novel approach for the damage detection of a rotating machine was conducted based on the spectrum analysis. As results, by adopting the ball bearing used in the rotating machine applied extensively, an spectrum analysis with thermal imaging experiment was performed. Also, as analysing the temperature characteristics obtained from the infrared thermography for in-situ rotating ball bearing under the lubrication condition, it was concluded that infrared thermography for condition monitoring in the rotating machine at real time could be utilized in many industrial fields.

• Progress in Medical Physics
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• v.21 no.1
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• pp.78-85
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• 2010

The compatibility with GammaMed-12i brachytherapy machine for an Ir-192 encapsulated source (IRRS20, KAERI, Korea) manufactured by Korea atomic energy research institute (KAERI) has been investigated. As a mechanical testing of compatibility, precise measurement of step movement with channels, measurement of curvature of radius for wire, and emergency return testing were performed. Periodic measurements of air kerma strength for 45 days were carried out to evaluate decay characteristics of Ir-192 radioisotope and comparison of dose distributions in phantom between KAERI and old sources previously used were performed by film dosimetry. KAERI source has a good compatibility with GammaMed12i machine as a result of mechanical testing. There are in good agreement with calculated values in activity characteristics and there were small differences in dose distributions around the source in comparison between KAERI and old source.