• Ecology and Resilient Infrastructure
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• v.5 no.3
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• pp.125-133
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• 2018

Artificial neural network (ANN) is a powerful model to predict time series data and have been frequently adopted to predict groundwater level (GWL). Many researchers have also tried to improve the performance of ANN prediction for GWL in many ways. Dummies are usually used in ANN as input to reflect the seasonal effect on predicted results, which is necessary for improving the predicting performance of ANN. In this study, the effect of Dummy on the prediction performance was analyzed qualitatively and quantitatively using several graphical methods, correlation coefficient and performance index. It was observed that results predicted using dummies for ANN model indicated worse performance than those without dummies.

• Earthquakes and Structures
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• v.8 no.5
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• pp.1171-1190
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• 2015

Enhancement of strength and ductility is the main reason for the extensive use of FRP jackets to provide external confinement to reinforced concrete columns especially in seismic areas. Therefore, numerous researches have been carried out in order to provide a better description of the behavior of FRP-confined concrete for practical design purposes. This study presents a new approach to obtain strength enhancement of CFRP (carbon fiber reinforced polymer) confined concrete cylinders by applying artificial neural networks (ANNs). The proposed ANN model is based on experimental results collected from literature. It represents the ultimate strength of concrete cylinders after CFRP confinement which is also given in explicit form in terms of geometrical and mechanical parameters. The accuracy of the proposed ANN model is quite satisfactory when compared to experimental results. Moreover, the results of the proposed ANN model are compared with five important theoretical models proposed by researchers so far and considered to be in good agreement.

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.4
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• pp.73-81
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• 2012

In this study, the settlement data of 32 center cored rockfill dams (total 39 monitored data) were collected and analyzed to develop the method to predict the crest settlement of a CCRD after impounding by using the internal settlement data occurred during construction. An artificial neural network (ANN) modeling was used in developing the method, which was considered to be a more reliable approach since in the ANN model dam height, core width, and core type were all considered as input variables in deriving the crest settlement, whereas in conventional methods, such as Clements's method, only dam height is used as a variable. The ANN analysis results showed a good agreement with the measured data, compared to those by the conventional methods using regression analysis. In addition, a simple procedure to use the ANN model for engineers in practice was provided by proposing the equations used for given input values.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2005.04a
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• pp.348-351
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• 2005

In this study, solute breakthrough curves through the unsaturated zone were predicted using artificial neural network (ANN) by numerical tests and laboratory experiments. In the numerical tests, applicability of ANN model to prediction of breakthrough curves was evaluated using synthetic data generated by HYDRUS-2D. An appropriate strategy of ANN application and input data form were recommended. The ANN model was validated by laboratory experiments comparing with HYDRUS-2D simulations. The results show that the ANN model can be an effective method for forecasting solute breakthrough curves through the unsaturated zone when hydraulic data are available.

• Journal of the Korean Association of Geographic Information Studies
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• v.8 no.2
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• pp.125-135
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• 2005

Generation and transport of nonpoint source pollution, especially sediment-associated pollutants, are profoundly influenced by hydrologic features of runoff. In order to identify pollutant export rates, hence, clear knowledge of rainfall-runoff relationship is a pre-requisition. In this study, performance of AnnAGNPS model was assessed based on the ability of the model to predict rainfall-runoff relationship. Three catchments, each under different nearly single land use, were simulated. From the results, it was found that the model was likely to produce better predictions for larger catchments than smaller catchments. Because of using the daily time scale, the model could not account for short durations less than 24 hours, especially high intensity events with multiple peak flow that significantly contribute to the generation and transport of pollutants. Since CN information for regional areas has not been built up, a careful selection of CN is needed to achieve accurate prediction of runoff volume. Storm distribution also found to be considered as an important calibration parameter for the hydrologic simulation.

• Bulletin of the Korean Chemical Society
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• v.33 no.2
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• pp.613-619
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• 2012

We applied several machine learning methods for developing QSAR models for prediction of acute toxicity to fathead minnow. The multiple linear regression (MLR) and artificial neural network (ANN) method were applied to predict 96 h $LC_{50}$ (median lethal concentration) of 555 chemical compounds. Molecular descriptors based on 2D chemical structure were calculated by PreADMET program. The recursive partitioning (RP) model was used for grouping of mode of actions as reactive or narcosis, followed by MLR method of chemicals within the same mode of action. The MLR, ANN, and two RP-MLR models possessed correlation coefficients ($R^2$) as 0.553, 0.618, 0.632, and 0.605 on test set, respectively. The consensus model of ANN and two RP-MLR models was used as the best model on training set and showed good predictivity ($R^2$=0.663) on the test set.

• Computers and Concrete
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• v.8 no.3
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• pp.279-292
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• 2011

The main purpose of this study includes investigation of the rheological properties of fresh self consolidating cement paste containing chemical and mineral additives using Artificial Neural Network (ANN) model. In order to develop the model, 200 different mixes are cast in the laboratory as a part of an extensive experimental research program. The data used in the ANN model are arranged in a format of fourteen input parameters covering water-binder ratio, four different mineral additives (calcium carbonate, metakaolin, silica fume, and limestone), five different superplasticizers based on the poly carboxylate and naphthalene and four different Viscosity Modified Admixtures (VMAs). Two common output parameters including the mini slump value and flow cone time are chosen for measuring the rheological properties of fresh self consolidating cement paste. Having validated the model, the influence of effective parameters on the rheological properties of fresh self consolidating cement paste is investigated based on the ANN model outputs. The output results of the model are then compared with the results of previous studies performed by other researchers. Ultimately, the analysis of the model outputs determines the optimal percentage of additives which has a strong influence on the rheological properties of fresh self consolidating cement paste. The proposed ANN model shows that metakaolin and silica fume affect the rheological properties in the same manner. In addition, for providing the suitable rheological properties, the ANN model introduces the optimal percentage of metakaolin, silica fume, calcium carbonate and limestone as 15, 15, 20 and 20% by cement weight, respectively.

• Korean Journal of Construction Engineering and Management
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• v.19 no.4
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• pp.82-92
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• 2018

Considering that earlier stage of construction project has a great influence on the possibility of lowering of environmental load, it is important to build and utilize system that can support effective decision making at the initial stage of the project. In this study, we constructed an environmental load estimation model that can be used at the early stage of the project using basic design factors. The model was constructed by using the artificial neural network to estimate environmental load by applying to planning stage (ANN-1), basic design stage (ANN-2). The result of test, shows that average of absolute measuring efficiency and standard deviation of ANN-1 and ANN-2 were 11.19% / 5.30% and 9.59% / 3.09% each. This result indicates that the model using the input variables extended with the project progress has high reliability and it is considered to be effective in decision support at the initial design stage of the project.

• Journal of the Korean Geotechnical Society
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• v.19 no.6
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• pp.343-350
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• 2003

The range of applications of artificial neural networks(Am) in many branches of geotechnical engineering is growing rapidly. This study was undertaken to develop an analysis model representing ultrasonically enhanced soil flushing by the use of ANN. Input data for the model-development were obtained by laboratory study, and used for training and verification. Analyses involved various ranges of momentum, loaming rate, activation function, hidden layer, and nodes. Results of the analyses were used to obtain the optimum conditions for establishing and verifying the model. The coefficient of correlation between the measured and the predicted data using the developed model was relatively high. It shows potential application of ANN to ultrasonically enhanced soil flushing which is not easy to build up a mathematical model.

• Bulletin of the Korean Chemical Society
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• v.26 no.1
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• pp.139-145
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• 2005

Artificial neural networks (ANNs), for a first time, were successfully developed for the modeling and prediction of solvent effects on rate constant of [2+2] cycloaddition reaction of diethyl azodicarboxylate with ethyl vinyl ether in various solvents with diverse chemical structures using quantitative structure-activity relationship. The most positive charge of hydrogen atom (q$^+$), dipole moment ($\mu$), the Hildebrand solubility parameter (${\delta}_H^2$) and total charges in molecule (q$_t$) are inputs and output of ANN is log k$_2$ . For evaluation of the predictive power of the generated ANN, the optimized network with 68 various solvents as training set was used to predict log k$_2$ of the reaction in 16 solvents in the prediction set. The results obtained using ANN was compared with the experimental values as well as with those obtained using multi-parameter linear regression (MLR) model and showed superiority of the ANN model over the regression model. Mean square error (MSE) of 0.0806 for the prediction set by MLR model should be compared with the value of 0.0275 for ANN model. These improvements are due to the fact that the reaction rate constant shows non-linear correlations with the descriptors.