• Computers and Concrete
• /
• v.19 no.4
• /
• pp.435-442
• /
• 2017

In recent years, the use of supplementary cementing materials, especially in addition to concrete, has been the subject of many researches. Rice husk ash (RHA) is one of these materials that in this research, is added to the roller compacted concrete as one of the pozzolanic materials. This paper evaluates how different contents of RHA added to the roller compacted concrete pavement specimens, can influence on the strength and permeability. The results are compared to the control samples and determined optimal level of RHA replacement. As it was expected, RHA as supplementary cementitious materials, improved mechanical properties of roller compacted concrete pavement (RCCP). Also, the application of adaptive neuro-fuzzy inference system (ANFIS) in predicting the permeability and compressive strength is investigated. The obtained results shows that the predicted value by this model is in good agreement with the experimental, which shows the proposed ANFIS model is a useful, reliable, fast and cheap tool to predict the permeability and compressive strength. A mean relative error percentage (MRE %) less than 1.1% is obtained for the proposed ANFIS model. Also, the test results and performed modeling show that the optimal value for obtaining the maximum compressive strength and minimum permeability is offered by substituting 9% and 18% of the cement by RHA, respectively.

• Geomechanics and Engineering
• /
• v.20 no.1
• /
• pp.75-86
• /
• 2020

This study aims to simulate the stabilization process of fibrous peat samples using end-bearing Cement Deep Mixing (CDM) columns by three area improvement ratios of 13.1% (TS-2), 19.6% (TS-3) and 26.2% (TS-3). It also focuses on the determination of approximate stress distribution between CDM columns and untreated fibrous peat soil. First, fibrous peat samples were mechanically stabilized using CDM columns of different area improvement ratio. Further, the ultimate bearing capacity of a rectangular foundation rested on the stabilized peat was calculated in stress-controlled condition. Then, this process was simulated via a FEM-based model using Plaxis 3-D foundation and the numerical modelling results were compared with experimental findings. In the numerical modelling stage, the behaviour of fibrous peat was simulated based on hardening soil (HS) model and Mohr-Coulomb (MC) model, while embedded pile element was utilized for CDM columns. The results indicated that in case of untreated peat HS model could predict the behaviour of fibrous peat better than MC model. The comparison between experimental and numerical investigations showed that the stress distribution between soil (S) and CDM columns (C) were 81%C-19%S (TS-2), 83%C-17%S (TS-3) and 89%C-11%S (TS-4), respectively. This implies that when the area improvement ratio is increased, the share of the CDM columns from final load was increased. Finally, the calculated bearing capacity factors were compared with results on the account of empirical design methods.

• Smart Structures and Systems
• /
• v.26 no.4
• /
• pp.403-418
• /
• 2020

In recent years, the use of Fiber Reinforced Polymers (FRPs) as one of the most common ways to increase the strength of concrete samples, has been introduced. Evaluation of the final strength of these specimens is performed with different experimental methods. In this research, due to the variety of models, the low accuracy and impact of different parameters, the use of new intelligence methods is considered. Therefore, using artificial intelligent-based models, a new solution for evaluating the bond strength of FRP is presented in this paper. 150 experimental samples were collected from previous studies, and then two new hybrid models of Imperialist Competitive Algorithm (ICA)-Artificial Neural Network (ANN) and Artificial Bee Colony (ABC)-ANN were developed. These models were evaluated using different performance indices and then, a comparison was made between the developed models. The results showed that the ICA-ANN model's ability to predict the bond strength of FRP is higher than the ABC-ANN model. Finally, to demonstrate the capabilities of this new model, a comparison was made between the five experimental models and the results were presented for all data. This comparison showed that the new model could offer better performance. It is concluded that the proposed hybrid models can be utilized in the field of this study as a suitable substitute for empirical models.

• Journal of Biosystems Engineering
• /
• v.42 no.2
• /
• pp.75-79
• /
• 2017

The use of tractors to carry out agricultural work has played an important role in mechanizing the agricultural sector. A repairable mechanical system (such as an agricultural tractor) is subject to deterioration or failure. In this study, a regression model was used to predict the failure rate of a John Deere 3140 tractor. The machine failure pattern was carefully studied, and key factors affecting the failure rate were identified in five regions of the Khouzestan province. Through a questionnaire, data was obtained from farm records. This data was grouped into six sub-groups, according to the annual use hours (AUH) and the manner in which the tractors were stored. Results showed that AUH and storage policies affected failure rate slightly. With an increase in the age of the tractors, the failure rate in the tractors used for 1050-2000 hours annually and stored outdoors was higher than those used for 200-1000 hours annually and stored in sheds. When the tractors were of the same age, the slope of the curve in the 200-1000 annual use hours increased gradually and then rapidly, but failure rate in the 1050-2000 annual use hours was high from the beginning, and subsequent increase in this value was almost uniform. As a result, it can be said that with an increase in the annual use hours, the failure and breakdown rate in John Deere 3140 tractors rapidly increases, but maintenance conditions only slightly affect the failure and breakdown rate.

• Korean Journal of Veterinary Research
• /
• v.40 no.2
• /
• pp.373-381
• /
• 2000

Serial ultrasonographic examinations were performed on pregnant Korean Jindo bitches. Measurements of inner chorionic cavity diameter and fetal head diameter were made from pregnancy day 15 to parturition. These measurements were converted retrospectively based on the day of parturition (day 0). The data of inner chorionic cavity diameter obtained from day -42 to day -25 and fetal head diameter obtained from day -24 to day -1 were used to prediction of parturition day. Formulas for the prediction of parturition day using the method of least squares were derived. These formulas were then used to predict parturition dates based on single measurements of inner chorionic cavity diameter or fetal head diameter in 17 additional pregnant Korean Jindo bitches. Predicted date of parturition was then compared to actual whelping date. In the prediction of parturition based on inner chorionic cavity diameter, 7 of 10 bitches were coincided prediction date and actual whelping date, and the prediction was accurate to within 1 day in 3 of 10 bitches. The prediction of parturition based on fetal head diameter was accurate to within 1 day in 6 of 7 bitches and within 2 days in 1 of 7 bitches. In conclusion, the ultrasound measurement of inner chorionic cavity diameter and fetal head diameter are practical and accurate tool in the prediction of parturition.

• Journal of Digital Convergence
• /
• v.12 no.3
• /
• pp.237-242
• /
• 2014

Recently, wind energy is expanding to combination of computing to forecast of wind power generation as well as intelligent of wind powerturbine. Wind power is rise and fall depending on weather conditions and difficult to predict the output for efficient power production. Wind power is need to reliably linked technology in order to efficient power generation. In this paper, distributed power generation forecasts to enhance the predicted and actual power generation in order to minimize the difference between the power of distributed power short-term prediction model is designed. The proposed model for prediction of short-term combining the physical models and statistical models were produced in a physical model of the predicted value predicted by the lattice points within the branch prediction to extract the value of a physical model by applying the estimated value of a statistical model for estimating power generation final gas phase produces a predicted value. Also, the proposed model in real-time National Weather Service forecast for medium-term and real-time observations used as input data to perform the short-term prediction models.

• Journal of Digital Convergence
• /
• v.12 no.3
• /
• pp.211-218
• /
• 2014

Recently, wind energy is expanding to combination of computing to forecast of wind power generation as well as intelligent of wind powerturbine. Wind power is rise and fall depending on weather conditions and difficult to predict the output for efficient power production. Wind power is need to reliably linked technology in order to efficient power generation. In this paper, distributed power generation forecasts to enhance the predicted and actual power generation in order to minimize the difference between the power of distributed power short-term prediction model is designed. The proposed model for prediction of short-term combining the physical models and statistical models were produced in a physical model of the predicted value predicted by the lattice points within the branch prediction to extract the value of a physical model by applying the estimated value of a statistical model for estimating power generation final gas phase produces a predicted value. Also, the proposed model in real-time National Weather Service forecast for medium-term and real-time observations used as input data to perform the short-term prediction models.

• The Journal of Fisheries Business Administration
• /
• v.47 no.1
• /
• pp.87-100
• /
• 2016

Suhyup Bank became to be subject to regulation of capital ratio by Basel III which was introduced in order to enhance stability of the financial institution. Accordingly, Suhyup Bank will require recapitalization. It is important to estimate the risk-weighted assets in calculating of Suhyup Bank's recapitalization scale. Therefor, this study aimed to present a scientific model as estimated the risk-weighted assets. Risk-weighted assets are calculated by applying different risk weights for loans, may have a certain relationship with the loans. Results show that the risk-weighted assets is affected by the previous year's risk-weighted assets and influenced the increase in loans during the year. Since the required basic capital adequacy ratio was specified, the risk-weighted assets should be predicted reasonably. Accordingly, on this study it was tried to derive the accounting equation to predict the risk-weighted assets based on management data of a bank since introduction of Basel III. As the risk-weighted assets were weighted differently according to the type of loans, if the accounting equation is derived by using the type of loans, then it would be helpful for the risk management of banks in the long-term. According to this, the increase of loan would be predicted on the basis of past management performance of Suhyup Bank, and for this reason, the future risk-weighted assets of Suhyup Bank were predicted. The result of this study was showed that 98.3% of risk-weighted assets of the previous year, 62.4% of the secured loan changes and 95.1% of the credit loan changes affected risk-weighted assets.

• Publications of The Korean Astronomical Society
• /
• v.30 no.2
• /
• pp.267-268
• /
• 2015

One of the long-standing problems in modern astronomy is the curious division of globular clusters (GCs) into two groups, according to the mean period (<$P_{ab}$>) of type ab RR Lyrae variables. In light of the recent discovery of multiple populations in GCs, we suggest a new model explaining the origin of the Sandage period-shift and the difference in mean period of type ab RR Lyrae variables between the two Oosterhoff groups. In our models, the instability strip in the metal-poor group II clusters, such as M15, is populated by second generation stars (G2) with enhanced helium and CNO abundances, while the RR Lyraes in the relatively metal-rich group I clusters like M3 are mostly produced by first generation stars (G1) without these enhancements. This population shift within the instability strip with metallicity can create the observed period-shift between the two groups, since both helium and CNO abundances play a role in increasing the period of RR Lyrae variables. The presence of more metal-rich clusters having Oosterhoff-intermediate characteristics, such as NGC 1851, as well as of most metal-rich clusters having RR Lyraes with the longest periods (group III) can also be reproduced, as more helium-rich third and later generations of stars (G3) penetrate into the instability strip with further increase in metallicity. Therefore, although there are systems where the suggested population shift cannot be a viable explanation, for the most general cases, our models predict that RR Lyraes are produced mostly by G1, G2, and G3, respectively, for the Oosterhoff groups I, II, and III.

• Journal of Korean Society of Water and Wastewater
• /
• v.20 no.6
• /
• pp.799-811
• /
• 2006

As an alternative measure to replace reactive stance with proactive one, a risk based management scheme has been commonly applied to enhance public satisfaction on water service by providing a higher creditable solution to handle a rehabilitation problem of pipe having high potential risk of leaks. This study intended to examine the feasibility of a simulation model to predict a recurrence probability of pipe leaks. As a branch of the data mining technique, probabilistic neural network (PNN) algorithm was applied to infer the extent of leaking recurrence probability of water network. PNN model could classify the leaking level of each unit segment of the pipe network. Pipe material, diameter, C value, road width, pressure, installation age as input variable and 5 classes by pipe leaking probability as output variable were built in PNN model. The study results indicated that it is important to pay higher attention to the pipe segment with the leak record. By increase the hydraulic pipe pressure to meet the required water demand from each node, simulation results indicated that about 6.9% of total number of pipe would additionally be classified into higher class of recurrence risk than present as the reference year. Consequently, it was convinced that the application of PNN model incorporated with a data base management system of pipe network to manage municipal water distribution network could make a promise to enhance the management efficiency by providing the essential knowledge for decision making rehabilitation of network.