• Smart Structures and Systems
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• v.22 no.5
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• pp.561-574
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• 2018

In this paper, for efficiently reducing the computational cost of the model updating during the optimization process of damage detection, the structural response is evaluated using properly trained surrogate model. Furthermore, in practice uncertainties in the FE model parameters and modelling errors are inevitable. Hence, an efficient approach based on Monte Carlo simulation is proposed to take into account the effect of uncertainties in developing a surrogate model. The probability of damage existence (PDE) is calculated based on the probability density function of the existence of undamaged and damaged states. The current work builds a framework for Probability Based Damage Detection (PBDD) of structures based on the best combination of metaheuristic optimization algorithm and surrogate models. To reach this goal, three popular metamodeling techniques including Cascade Feed Forward Neural Network (CFNN), Least Square Support Vector Machines (LS-SVMs) and Kriging are constructed, trained and tested in order to inspect features and faults of each algorithm. Furthermore, three wellknown optimization algorithms including Ideal Gas Molecular Movement (IGMM), Particle Swarm Optimization (PSO) and Bat Algorithm (BA) are utilized and the comparative results are presented accordingly. Furthermore, efficient schemes are implemented on these algorithms to improve their performance in handling problems with a large number of variables. By considering various indices for measuring the accuracy and computational time of PBDD process, the results indicate that combination of LS-SVM surrogate model by IGMM optimization algorithm have better performance in predicting the of damage compared with other methods.

• Journal of the Korea Society of Computer and Information
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• v.4 no.1
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• pp.62-67
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• 1999

The neural network is a static network that consists of a number of layer: input layer, output layer and one or more hidden layer connected in a feed forward way. The popularity of neural network appear to be its ability of learning and approximation capability. The Elman Neural Network proposed the J. Elman, is a type of recurrent network. Is has the feedback links from hidden layer to context layer. So Elman Neural Network is the better performance than the neural network. In this paper. we propose the Modified Elman Neural Network. The structure of a MENN is based on the basic ENN. The recurrency of the network is due to the feedback links from the output layer and the hidden layer to the context layer. In order to certify the usefulness of the proposed method, the MENN apply to the X-Y cartesian tracking system. Simulation shows that the proposed MENN method is better performance than the multi layer neural network and ENN.

• Bulletin of the Korean Chemical Society
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• v.26 no.12
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• pp.2007-2016
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• 2005

An artificial neural network (ANN) is successfully presented for prediction acidity constant (pKa) of various benzoic acids and phenols with diverse chemical structures using a nonlinear quantitative structure-property relationship. A three-layered feed forward ANN with back-propagation of error was generated using six molecular descriptors appearing in the multi-parameter linear regression (MLR) model. The polarizability term $(\pi_1)$, most positive charge of acidic hydrogen atom $(q^+)$, molecular weight (MW), most negative charge of the acidic oxygen atom $(q^-)$, the hydrogen-bond accepting ability $(\epsilon_B)$ and partial charge weighted topological electronic (PCWTE) descriptors are inputs and its output is pKa. It was found that properly selected and trained neural network with 205 compounds could fairly represent dependence of the acidity constant on molecular descriptors. For evaluation of the predictive power of the generated ANN, an optimized network was applied for prediction pKa values of 37 compounds in the prediction set, which were not used in the optimization procedure. Squared correlation coefficient $(R^2)$ and root mean square error (RMSE) of 0.9147 and 0.9388 for prediction set by the MLR model should be compared with the values of 0.9939 and 0.2575 by the ANN model. These improvements are due to the fact that acidity constant of benzoic acids and phenols in water shows nonlinear correlations with the molecular descriptors.

• The Korean Journal of Ecology
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• v.27 no.4
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• pp.245-256
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• 2004

The simple biosphere model 2 (SiB2), which is one of the land surface models, simulates the exchange of momentum, energy and mass such as water vapor and carbon dioxide between atmosphere and biosphere, and includes the biochemical sub-model for representation of stomatal conductance and photosynthetical activities. Throughout the SiB2 simulation, the significant information not only to understand of water and carbon budget but also to make an analysis of interaction such as feed-back and-forward between environment and vegetation is given. Using revised SiB2-Paddy, one sample study which is the evaluation of the runoff in Chaophraya river basin according to land use/cover change is presented in this review. Hence, SiB2 is available in order to ecological studied, if revised SiB2 for realistic simulation about soil respiration, computing leaf area index, vegetation competition and soil moisture is improved.

• Journal of Biosystems Engineering
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• v.41 no.1
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• pp.12-20
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• 2016

Purpose: Pepper prices have risen continuously because of a decrease in cultivation area; therefore, mechanical harvesting systems for peppers should be developed to reduce cost, time, and labor during harvest. In this study, a screw type picking head for a self-propelled pepper harvester was developed, and the optimal working conditions were evaluated considering helix types, winding directions of helix, and rotational speeds of the helix. Methods: The screw type was selected for the picking head after analyzing previous studies, and the device consisted of helices and a feed chain mechanism for conveying pepper branches. A double helix and a triple helix were manufactured, and rotational speeds of 200, 300, and 400 rpm were tested. The device was controlled by a variable speed (VS) motor and an inverter. Both the forward and reverse directions were tested for the winding and rotating directions of the helix. An experiment crop (cultivar: Longgreenmat) was cultivated in a plastic greenhouse. The test results were analyzed using the SAS program with ANOVA to examine the relationship between each factor and the performance of the picking head. Results: The results of the double and triple helix tests in the reverse direction showed gross harvest efficiency levels of 60-95%, mechanical damage rates of 8-20%, and net marketable portion rates of 50-80%. The dividing ratio was highest at a rotational speed of 400 rpm. Gross harvest efficiency was influenced by the types of helix and rotational speed. Net marketable portion was influenced by rotational speed but not influenced by the type of helix. Mechanical damage was not influenced by the type of helix or rotational speed. Conclusions: Best gross harvest efficiency was obtained at a rotational speed of 400 rpm; however, operating the device at that speed resulted in vibration, which should be reduced.

• International Journal of Concrete Structures and Materials
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• v.7 no.3
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• pp.225-238
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• 2013

In this study, a two-layer feed-forward neural network was constructed and applied to determine a mapping associating mix design and testing factors of cement-nano silica (NS)-rice husk ash ternary blended concrete samples with their performance in conductance to the water absorption properties. To generate data for the neural network model (NNM), a total of 174 field cores from 58 different mixes at three ages were tested in the laboratory for each of percentage, velocity and coefficient of water absorption and mix volumetric properties. The significant factors (six items) that affect the permeability properties of ternary blended concrete were identified by experimental studies which were: (1) percentage of cement; (2) content of rice husk ash; (3) percentage of 15 nm of $SiO_2$ particles; (4) content of NS particles with average size of 80 nm; (5) effect of curing medium and (6) curing time. The mentioned significant factors were then used to define the domain of a neural network which was trained based on the Levenberg-Marquardt back propagation algorithm using Matlab software. Excellent agreement was observed between simulation and laboratory data. It is believed that the novel developed NNM with three outputs will be a useful tool in the study of the permeability properties of ternary blended concrete and its maintenance.

• Journal of the Korean Society of Industry Convergence
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• v.20 no.1
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• pp.34-48
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• 2017

This study proposes a new approach to control a trajectory control of vertical type articulated robot arm with six revolution joints by computed torque method for manufacturing process automation. The proposed control scheme takes advantage of the properties of the fuzzy controllers. The proposed method is suitable to control of the trajectory and path control in cartesian space for vertical type articulated robot manipulator for forging manufacturing process automation. The results is illustrated that the proposed fuzzy computed torque controller is more stable and robust than the conventional computed torque controller. This study is included with an analytical methodology of inverse kinematic computation for 6 DOF manipulators. And an intelligent PID based on feed forward fuzzy control structure is applied to control the working path control with disturbances caused by uncertainty parameters of the manipulator dynamic model. Lastly, the validity of proposed is verified by simulations and experiments.

• Computers and Concrete
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• v.12 no.6
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• pp.785-802
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• 2013

Ready-mixed soil material, known as a kind of controlled low-strength material, is a new way of soil cement combination. It can be used as backfill materials. In this paper, artificial neural network and nonlinear regression approach were applied to predict the compressive strength of ready-mixed soil material containing Portland cement, slag, sand, and soil in mixture. The data used for analyzing were obtained from our testing program. In the experiment, we carried out a mix design with three proportions of sand to soil (e.g., 6:4, 5:5, and 4:6). In addition, blast furnace slag partially replaced cement to improve workability, whereas the water-to-binder ratio was fixed. Testing was conducted on samples to estimate its engineering properties as per ASTM such as flowability, strength, and pulse velocity. Based on testing data, the empirical pulse velocity-strength correlation was established by regression method. Next, three topologies of neural network were developed to predict the strength, namely ANN-I, ANN-II, and ANN-III. The first two models are back-propagation feed-forward networks, and the other one is radial basis neural network. The results show that the compressive strength of ready-mixed soil material can be well-predicted from neural networks. Among all currently proposed neural network models, the ANN-I gives the best prediction because it is closest to the actual strength. Moreover, considering combination of pulse velocity and other factors, viz. curing time, and material contents in mixture, the proposed neural networks offer better evaluation than interpolated from pulse velocity only.

• Computers and Concrete
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• v.19 no.1
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• pp.59-68
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• 2017

This study focused on modeling the behavior of the compressive stress using the average strain and ultrasonic test results in concrete. Feed-forward backpropagation artificial neural network (ANN) models were used to compare four types of concrete mixtures with varying water cement ratio (WC), ordinary concrete (ORC) and concrete with short steel fiber-reinforcement (FRC). Sixteen (16) $150mm{\times}150mm{\times}150mm$ concrete cubes were used; each contained eighteen (18) data sets. Ultrasonic test with pitch-catch configuration was conducted at each loading state to record linear and nonlinear test response with multiple step loads. Statistical Spearman's rank correlation was used to reduce the input parameters. Different types of concrete produced similar top five input parameters that had high correlation to compressive stress: average strain (${\varepsilon}$), fundamental harmonic amplitude (A1), $2^{nd}$ harmonic amplitude (A2), $3^{rd}$ harmonic amplitude (A3), and peak to peak amplitude (PPA). Twenty-eight ANN models were trained, validated and tested. A model was chosen for each WC with the highest Pearson correlation coefficient (R) in testing, and the soundness of the behavior for the input parameters in relation to the compressive stress. The ANN model showed increasing WC produced delayed response to stress at initial stages, abruptly responding after 40%. This was due to the presence of more voids for high water cement ratio that activated Contact Acoustic Nonlinearity (CAN) at the latter stage of the loading path. FRC showed slow response to stress than ORC, indicating the resistance of short steel fiber that delayed stress increase against the loading path.

• Structural Engineering and Mechanics
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• v.63 no.4
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• pp.429-438
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• 2017

Structural design has an imperative role in deciding the failure possibility of a Reinforced Concrete (RC) structure. Recent research works achieved the goal of predicting the structural failure of the RC structure with the assistance of machine learning techniques. Previously, the Artificial Neural Network (ANN) has been trained supported by Particle Swarm Optimization (PSO) to classify RC structures with reasonable accuracy. Though, keeping in mind the sensitivity in predicting the structural failure, more accurate models are still absent in the context of Machine Learning. Since the efficiency of multi-objective optimization over single objective optimization techniques is well established. Thus, the motivation of the current work is to employ a Multi-objective Genetic Algorithm (MOGA) to train the Neural Network (NN) based model. In the present work, the NN has been trained with MOGA to minimize the Root Mean Squared Error (RMSE) and Maximum Error (ME) toward optimizing the weight vector of the NN. The model has been tested by using a dataset consisting of 150 RC structure buildings. The proposed NN-MOGA based model has been compared with Multi-layer perceptron-feed-forward network (MLP-FFN) and NN-PSO based models in terms of several performance metrics. Experimental results suggested that the NN-MOGA has outperformed other existing well known classifiers with a reasonable improvement over them. Meanwhile, the proposed NN-MOGA achieved the superior accuracy of 93.33% and F-measure of 94.44%, which is superior to the other classifiers in the present study.