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One of the important problems in seismic rehabilitation studies of existing structures is opportune decision making about ending or continuance of various stages rehabilitation in order to save time and cost. About that we can use decision maker systems to solve this problem and to give more rational assessment about that problem. This paper presents a procedure based on Fuzzy Logic that classifies structures into qualitative seismic hazard categories. The purpose of this study is to get a model that can speed existing structures seismic rehabilitation primary studies and also to prompt decision making about continuance of study process. In order to account real world data, in addition to expert’s knowledge, groups of school seismic rehabilitation data of different cities of Iran have been used for modeling. In order to reduce the input space and increase generalization ability of the system, a feature selection method has been applied to the data. Among available parameters of data, significant parameters have been selected by Decision Tree Learning method. Then, Fuzzy Membership Functions corresponding to these parameters have been defined. Appropriate defining of these functions, we can insinuate factors such as uncertainty on that parameter in computations also. Afterwards, the Fuzzy System has been designed by conditional regulations. It is worth to say that these regulations are optimizedcompletely. In order to ease the process of risk assessment based on this model, software named “Rapid Seismic Risk Evaluation” (RSRE) has been developed. Thus, we have a model that by inputting 7 entrance parameters of a structure (both structural and geotechnical parameters corresponding to existing structure), generates its seismic risk level. The proposed procedure has advantages among the rest we can recount the possibility of modeling uncertainties, inputting structural information qualitative and high speed of risk analysis process. It is clear that using Fuzzy Logic not only lead to more simple formation, but also speed the rate of risk analysis process intensely, that this case is one of the most important advantages of the proposed method. In order to scrutiny of the designed model, various controls have been done. These controls have been tested on different data. Outcome results are representative high accuracy of designed model. Finally, in order to survey the efficiency of proposed procedure, the designed model has been applied to some of Tehran and its suburb school structures and outcome results have been compared with main data real results. Outcome results are representative good efficiency of the method. We should notice that using Fuzzy Concluder Systems lead to speed structure risk analysis and so decision making about various stages of structure rehabilitation is performed with more rate than previous. Thus, use of procedure that proposed in this paper, can has suitable applications in rapid seismic risk evaluation of studied structures in first stage of rehabilitation process.

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In this paper, a new hybrid intelligent method is presented for detecting the bearing faults in the various rotating speeds. The vibration signals are collected in four conditions, including the normal state, the faulty inner race, the faulty outer race, and the faulty bearing element. Firstly, twenty-two statistical features in the time domain and four frequency features, three Wavelet packet decomposition (WPD), and the first five intrinsic mode functions obtained by the empirical mode decomposition (EMD) are extracted from the original signal; finally, the feature vector for each signal sample has 424 features. However, in the high dimensional feature matrix, there may exist the insensitive features to the presence of defects. Therefore, in this study, the compensation distance evaluation technique (CDET) is used to select the optimal features. Then, the selected features are used as the inputs of the support vector machine (SVM) classifier to diagnose the bearing conditions. In the CDET method, there is a threshold indicator that plays a decisive role in choosing the desired attributes. Also, the SVM method has some parameters that need to be set during the fault detection process. Therefore, the particle swarm optimization (PSO) algorithm is used to determine the optimal threshold in the CDET method and the optimal SVM parameters, so that the prediction error of the bearing conditions and the number of the selected features are minimized. The obtained results demonstrate that the selected features are well able to differentiate between different bearing conditions at various speeds. Comparing the results of this paper with other fault detection methods indicates the ability of the proposed method.

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In this study, an evaluation model is developed to assess the credibility of the loan applicants. The proposed model is a multicriteria decision making (MCDM) problem consisting of numerous criteria by integrating analytic hierarchy process (AHP) and genetic algorithm (GA). In the case of apparent consensus for several measures, the research clearly indicates that both quantitative and qualitative information must be employed in evaluating loan applicants. The AHP approach is widely used for MCDM in various scopes. In 2008 Lin et al proposed the adaptive AHP approach (A³)in order to decrease the number of steps for checking the inconsistency in the AHP model. The study presents a MCDM model by developing the new adaptive AHP approach (N_A³) already proposed by Herrera-Viedma in 2004. The proposed model has led to fewer calculations, and less complexity. The model was applied to 200 clients in order to show its efficiency and applicability. A brief look at the implementation of the model showed that it is significantly valid in selecting clients with respect to the known criteria, besides decision making regarding the determination of the assessment factors.
 
 
 
 

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Mines and their related-industries are able to affect their surrounding environment, not only by their activities, but also after being abandoned. Among their different harmful effects, under water and surface water contaminations, and soil contamination can be mentioned. In order to manage these environmental effects, it is necessary to use reasonable methods for modelling heavy metal concentration in soil. This study aims to present a framework for modelling heavy metal soil contamination based on spectroscopy and statistical models. For this purpose, the spectral curves of the 53 soil samples, derived from an abandoned mine and its surrounding areas in New South Wales, Australia, were collected using a spectroradiometer in visible to short wavelength infrared (SWIR) wavelengths. Calculating the second derivative of the collected spectral data, random forest feature selection method (RFFS) was used to determine the most important spectral data for modelling heavy metal concentrations including lead, silver, cadmium and mercury. Then, the modelling techniques including multiple linear regression, random forest regression, and support vector regression (SVR) were applied on the selected spectral data. The results indicated that SWIR wavelengths are the most important spectral data for modelling heavy metal concentrations. Moreover, the non-linear machine learning methods, especially random forest with RMSE of 0.8 ppm and R² of 0.51 for lead and RMSE of 9.4 ppm and R² of 0.46 for cadmium performed better than multiple linear regression.