Islamic Azad University at Tafresh

www.iautb.ac.ir
Tafresh, Iran

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Zahrai S.M.,University of Tehran | Mirghaderi S.R.,University of Tehran | Saleh A.,Islamic Azad University at Tafresh
Steel and Composite Structures | Year: 2017

Experimental and numerical studies of a newly developed Reduced Beam Section (RBS) connection, called Tubular Web RBS connection (TW-RBS) have been recently conducted. This paper presents experimental and numerical results of extending the plastic hinge length on the beam flange to increase energy dissipation of a proposed version of the TW-RBS connection with two pipes, (TW-RBS(II)), made by replacing a part of flat web with two steel tubular web at the desirable location of the beam plastic hinge. Two deep-beam specimens with two pipes are prepared and tested under cyclic loads. Obtained results reveal that the TW-RBS(II) like its type I, increases story drift capacity up to 6% in deep beam much more than that stipulated by the current seismic codes. Based on test results, the proposed TW-RBS(II) helps to dissipate imposed energy up to 30% more than that of the TW-RBS(I) specimens at the same story drift and also reduces demands at the beam-to-column connection up to 30% by increasing plastic hinge length on the beam flange. The TW-RBS(II) specimens are finally simulated using finite element method showing good agreement with experimental results. Copyright © 2017 Techno-Press, Ltd.


Kheirkhah A.,Bu - Ali Sina University | Azadeh A.,University of Tehran | Saberi M.,Islamic Azad University at Tafresh | Azaron A.,University College Dublin | And 2 more authors.
Computers and Industrial Engineering | Year: 2013

Due to various seasonal and monthly changes in electricity consumption and difficulties in modeling it with the conventional methods, a novel algorithm is proposed in this paper. This study presents an approach that uses Artificial Neural Network (ANN), Principal Component Analysis (PCA), Data Envelopment Analysis (DEA) and ANOVA methods to estimate and predict electricity demand for seasonal and monthly changes in electricity consumption. Pre-processing and post-processing techniques in the data mining field are used in the present study. We analyze the impact of the data pre-processing and post-processing on the ANN performance and a 680 ANN-MLP is constructed for this purpose. DEA is used to compare the constructed ANN models as well as ANN learning algorithm performance. The average, minimum, maximum and standard deviation of mean absolute percentage error (MAPE) of each constructed ANN are used as the DEA inputs. The DEA helps the user to use an appropriate ANN model as an acceptable forecasting tool. In the other words, various error calculation methods are used to find a robust ANN learning algorithm. Moreover, PCA is used as an input selection method, and a preferred time series model is chosen from the linear (ARIMA) and nonlinear models. After selecting the preferred ARIMA model, the Mcleod-Li test is applied to determine the nonlinearity condition. Once the nonlinearity condition is satisfied, the preferred nonlinear model is selected and compared with the preferred ARIMA model, and the best time series model is selected. Then, a new algorithm is developed for the time series estimation; in each case an ANN or conventional time series model is selected for the estimation and prediction. To show the applicability and superiority of the proposed ANN-PCA-DEA-ANOVA algorithm, the data regarding the Iranian electricity consumption from April 1992 to February 2004 are used. The results show that the proposed algorithm provides an accurate solution for the problem of estimating electricity consumption. © 2012 Elsevier Ltd. All rights reserved.


Kazem A.,Tafresh University | Sharifi E.,Tafresh University | Hussain F.K.,University of Technology, Sydney | Saberi M.,Islamic Azad University at Tafresh | Hussain O.K.,Curtin University Australia
Applied Soft Computing Journal | Year: 2013

Due to the inherent non-linearity and non-stationary characteristics of financial stock market price time series, conventional modeling techniques such as the Box-Jenkins autoregressive integrated moving average (ARIMA) are not adequate for stock market price forecasting. In this paper, a forecasting model based on chaotic mapping, firefly algorithm, and support vector regression (SVR) is proposed to predict stock market price. The forecasting model has three stages. In the first stage, a delay coordinate embedding method is used to reconstruct unseen phase space dynamics. In the second stage, a chaotic firefly algorithm is employed to optimize SVR hyperparameters. Finally in the third stage, the optimized SVR is used to forecast stock market price. The significance of the proposed algorithm is 3-fold. First, it integrates both chaos theory and the firefly algorithm to optimize SVR hyperparameters, whereas previous studies employ a genetic algorithm (GA) to optimize these parameters. Second, it uses a delay coordinate embedding method to reconstruct phase space dynamics. Third, it has high prediction accuracy due to its implementation of structural risk minimization (SRM). To show the applicability and superiority of the proposed algorithm, we selected the three most challenging stock market time series data from NASDAQ historical quotes, namely Intel, National Bank shares and Microsoft daily closed (last) stock price, and applied the proposed algorithm to these data. Compared with genetic algorithm-based SVR (SVR-GA), chaotic genetic algorithm-based SVR (SVR-CGA), firefly-based SVR (SVR-FA), artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS), the proposed model performs best based on two error measures, namely mean squared error (MSE) and mean absolute percent error (MAPE). Copyright © 2012 Published by Elsevier B.V. All rights reserved.


Azadeh A.,University of Tehran | Saberi M.,Islamic Azad University at Tafresh | Rouzbahman M.,University of Toronto | Saberi Z.,Amirkabir University of Technology
Journal of Loss Prevention in the Process Industries | Year: 2013

This study presents an intelligent algorithm based on Adaptive Neuro-Fuzzy Inference System (ANFIS) and statistical methods for measuring job stress in noisy and complex petrochemical plants. Job stress is evaluated against health, safety, environment and ergonomics (HSEE) program in the integrated algorithm. The algorithm is composed of seventeen distinct steps. To achieve the objectives of this study, standard questionnaires with respect to HSEE are completed by operators. The average results for each category of HSEE are used as inputs and job stress is used as output for the algorithm. Moreover, operators' stress level with respect to HSEE is evaluated by the algorithm. Finally, operators with weak stress level are identified. The advantage and superiority of the intelligent algorithm are shown by error analysis in contrast with conventional regression approaches. This is the first study that introduces an integrated intelligent algorithm for assessment and improvement of job stress and HSEE in noisy, complex and uncertain environment. © 2012 Elsevier Ltd.


Azadeh A.,University of Tehran | Saberi M.,Islamic Azad University at Tafresh | Asadzadeh S.M.,University of Tehran | Hussain O.K.,Islamic Azad University at Tafresh | Saberi Z.,Amirkabir University of Technology
International Journal of Electrical Power and Energy Systems | Year: 2013

This paper presents an adaptive-network-based fuzzy inference system (ANFIS)-fuzzy data envelopment analysis (FDEA) algorithm for improvement of long-term natural gas (NG) consumption forecasting and analysis. Two types of ANFIS (Types 1 and 2) have been proposed to forecast annual NG demand. For each type, several ANFIS models have been constructed and tested in order to find the best ANFIS for NG consumption. Two parameters have been considered in construction and examination of plausible ANFIS models (Type 1). Six different membership functions and several linguistic variables are considered in building ANFIS. Also different value of cluster radius has been used to construct ANFIS (Type 2) models. The proposed models consist of two input variables, namely, Gross Domestic Product (GDP) and Population. All trained ANFIS are then compared with respect to mean absolute percentage error (MAPE), Root mean square normalized error (RMSE) and correlation coefficient (R) using data envelopment analysis (DEA). To meet the best performance of the intelligent based approaches, data are pre-processed (scaled) and finally our outputs are post-processed (returned to its original scale). FDEA is used to examine the behavior of gas consumption. To show the applicability and superiority of the ANFIS-FDEA algorithm, actual NG consumption in six Southern America countries from 1980 to 2007 is considered. NG consumption is then forecasted up to 2015. The ANFIS-FDEA algorithm is capable of dealing both complexity and uncertainty as well several other unique features discussed in this paper. © 2012 Elsevier Ltd. All rights reserved.


Azadeh A.,University of Tehran | Saberi M.,Islamic Azad University at Tafresh | Kazem A.,Tafresh University | Ebrahimipour V.,University of Tehran | And 2 more authors.
Applied Soft Computing Journal | Year: 2013

Fault detection and diagnosis have an effective role for the safe operation and long life of systems. Condition monitoring is an appropriate way of the maintenance technique that is applicable in the fault diagnosis of rotating machinery faults. A unique flexible algorithm is proposed for classifying the condition of centrifugal pump based on support vector machine hyper-parameters optimization and artificial neural networks (ANNs) which are composed of eight distinct steps. Artificial neural networks (ANNs), support vector classification with genetic algorithm (SVC-GA) and support vector classification with particle swarm optimization (SVC-PSO) algorithm have been considered in a flexible algorithm to perform accurate classification in the manufacturing area. SVC-GA, SVC-PSO and ANN have been used together due to their importance and capabilities in classifying domain. Also, the superiority of the proposed hybrid algorithm (SVC with GA and PSO) is shown by comparing its results with SVC performance. Two types of faults through six features, flow, temperature, suction pressure, discharge pressure, velocity, and vibration, have been classified with proposed integrated algorithm. To test the robustness of the efficiency results of the proposed method, the ability of proposed flexible algorithm in dealing with noisy and corrupted data is analyzed. © 2012 Elsevier B.V. All rights reserved.


Azadeh A.,University of Tehran | Saberi M.,Islamic Azad University at Tafresh | Anvari M.,Iran University of Science and Technology
Computers and Industrial Engineering | Year: 2011

Efficiency frontier analysis has been an important approach of evaluating firms' performance in private and public sectors. There have been many efficiency frontier analysis methods reported in the literature. However, the assumptions made for each of these methods are restrictive. Each of these methodologies has its strength as well as major limitations. This study proposes two non-parametric efficiency frontier analysis sub-algorithms based on (1) Artificial Neural Network (ANN) technique and (2) ANN and Fuzzy C-Means for measuring efficiency as a complementary tool for the common techniques of the efficiency studies in the previous studies. Normal probability plot is used to find the outliers and select from these two methods. The proposed computational algorithms are able to find a stochastic frontier based on a set of input-output observational data and do not require explicit assumptions about the functional structure of the stochastic frontier. In these algorithms, for calculating the efficiency scores, a similar approach to econometric methods has been used. Moreover, the effect of the return to scale of decision-making unit (DMU) on its efficiency is included and the unit used for the correction is selected by notice of its scale (under constant return to scale assumption). Also in the second algorithm, for increasing DMUs' homogeneousness, Fuzzy C-Means method is used to cluster DMUs. Two examples using real data are presented for illustrative purposes. First example which deals with power generation sector shows the superiority of Algorithm 2 while the second example dealing auto industries of various developed countries shows the superiority of Algorithm 1. Overall, we find that the proposed integrated algorithm based on ANN, Fuzzy C-Means and Normalization approach provides more robust results and identifies more efficient units than the conventional methods since better performance patterns are explored. © 2010 Elsevier Ltd. All rights reserved.


Kaveh A.,Iran University of Science and Technology | Rahami H.,University of Tehran | Mehanpour H.,Islamic Azad University at Tafresh
Advances in Engineering Software | Year: 2012

The aim of this paper is two fold. In the first part, static analysis of structures with repeated patterns is presented. These structures are comprised of submodels each having different repeated pattern. As an example, considering a structure with two different repeated patterns, the nodal numbering is performed in such a manner that the resulting stiffness matrix of the structure contains two block diagonal matrices. Thus their inversion can easily be performed using regular matrices requiring smaller amount of computational time. In the second part, the modal analysis, free vibration and eigen-frequencies of such structures are studied. Here as well the stiffness and mass matrices are transformed into two block matrices forms and using dynamic condensation and the matrix inversion which is involved in this condensation, the eigensolution is performed on matrices of lower dimensions. The presented examples consist of 2D and 3D structures in which in some stories the stiffnesses are changed due to the addition of some members taking the structures out of regularity. Apart from these, the power transition towers often having additional bracings in some levels are investigated. Other applications correspond to calculating the buckling loads and natural frequencies of regular plates driven to irregular forms by having different support conditions and some added parts. © 2012 Elsevier Ltd. All rights reserved.


Azadeh A.,University of Tehran | Asadzadeh S.M.,University of Tehran | Saberi M.,Islamic Azad University at Tafresh | Nadimi V.,Tafresh University | And 2 more authors.
Applied Energy | Year: 2011

This paper presents an adaptive network-based fuzzy inference system-stochastic frontier analysis (ANFIS-SFA) approach for long-term natural gas (NG) consumption prediction and analysis of the behavior of NG consumption. The proposed models consist of input variables of Gross Domestic Product (GDP) and population (POP). Six distinct models based on different inputs are defined. All of trained ANFIS are then compared with respect to mean absolute percentage error (MAPE). To meet the best performance of the intelligent based approaches, data are pre-processed (scaled) and finally the outputs are post-processed (returned to its original scale). To show the applicability and superiority of the integrated ANFIS-SFA approach, gas consumption in four Middle Eastern countries i.e. Bahrain, Saudi Arabia, Syria, and United Arab Emirates is forecasted and analyzed based on the data of the time period 1980-2007. With the aid of autoregressive model, GDP and population are projected for the period 2008-2015. These projected data are used as the input of ANFIS model to predict the gas consumption in the selected countries for 2008-2015. SFA is then used to examine the behavior of gas consumption in the past and also to make insights for the forthcoming years. The ANFIS-SFA approach is capable of dealing with complexity, uncertainty, and randomness as well as several other unique features discussed in this paper. © 2011 Elsevier Ltd.


Azadeh A.,University of Tehran | Seraj O.,University of Tehran | Asadzadeh S.M.,University of Tehran | Saberi M.,Islamic Azad University at Tafresh
Applied Soft Computing Journal | Year: 2012

This study introduces an integrated fuzzy regression (FR) data envelopment analysis (DEA) algorithm for oil consumption estimation and optimization with uncertain and ambiguous data. This is quite important as oil consumption estimations deals with several uncertainties due to social, economic factors. Furthermore, DEA is integrated with FR because there is no clear cut as to which FR approach is superior for oil consumption estimation. The standard indicators used in this paper are population, cost of crude oil, gross domestic production (GDP) and annual oil production. Fifteen popular and most cited FR models are considered in the algorithm. Each FR model has different approach and advantages. The input data is divided into train and test data. The FR models have been tuned for all their parameters according to the train data, and the best coefficients are identified. Center of Average Method for defuzzification output process is applied. For determining the rate of error of FR models estimations, the rate of defuzzified output of each model is compared with its actual rate consumption in test data. The efficiency of 15 FR models is examined by the output-oriented Data Envelopment Analysis (DEA) model without inputs by considering three types of relative error: RMSE, MAE and MAPE. The applicability and superiority of the proposed algorithm is shown for monthly oil consumption of Canada, United States, Japan and Australia from 1990 to 2005. © 2012 Elsevier B.V.

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