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| 1 | Calculation of maximum surface settlement induced by EPB shield tunnelling and introducing most effective parameter显示文摘This study aims to predict ground surface settlement due to shallow tunneling and introduce the most affecting parameters on this phenomenon.Based on data collected from Shanghai LRT Line 2 project undertaken by TBM-EPB method,this research has considered the tunnel's geometric,strength,and operational factors as the dependent variables.At first,multiple regression(MR) method was used to propose equations based on various parameters.The results indicated the dependency of surface settlement on many parameters so that the interactions among different parameters make it impossible to use MR method as it leads to equations of poor accuracy.As such,adaptive neuro-fuzzy inference system(ANFIS),was used to evaluate its capabilities in terms of predicting surface settlement.Among generated ANFIS models,the model with all input parameters considered produced the best prediction,so as its associated R^2 in the test phase was obtained to be 0.957.The equations and models in which operational factors were taken into consideration gave better prediction results indicating larger relative effect of such factors.For sensitivity analysis of ANFIS model,cosine amplitude method(CAM) was employed; among other dependent variables,fill factor of grouting(n) and grouting pressure(P) were identified as the most affecting parameters. | Sayed Rahim Moeinossadat Kaveh Ahangari Kourosh Shahriar | 2016 | Journal of Central South University2016，23，12： | 6 |
| 2 | Predicting crest settlement in concrete face rockfill dams using adaptive neuro-fuzzy inference system and gene expression programming intelligent methods显示文摘This paper deals with the estimation of crest settlement in a concrete face rockfill dam (CFRD), utilizing intelligent methods. Following completion of dam construction, considerable movements of the crest and the body of the dam can develop during the first impoundment of the reservoir. Although there is vast experience worldwide in CFRD design and construction, few accurate experimental relationships are available to predict the settlement in CFRD. The goal is to advance the development of intelligent methods to estimate the subsidence of dams at the design stage. Due to dam zonification and uncertainties in material properties, these methods appear to be the appropriate choice. In this study, the crest settlement behavior of CFRDs is analyzed based on compiled data of 24 CFRDs constructed during recent years around the world, along with the utilization of gene expression programming (GEP) and adaptive neuro-fuzzy inference system (ANFIS) methods. In addition, dam height (H), shape factor (S f ), and time (t, time after first operation) are also assessed, being considered major factors in predicting the settlement behavior. From the relationships proposed, the values of R 2 for both equations of GEP (with and without constant) were 0.9603 and 0.9734, and for the three approaches of ANFIS (grid partitioning (GP), subtractive clustering method (SCM), and fuzzy c-means clustering (FCM)) were 0.9693, 0.8657, and 0.8848, respectively. The obtained results indicate that the overall behavior evaluated by this approach is consistent with the measured data of other CFRDs. | Danial BEHNIA Kaveh AHANGARI Ali NOORZAD Sayed Rahim MOEINOSSADAT | 2013 | Journal of Zhejiang University-Science A(Applied Physics & Engineering)2013，14，8： | 6 |
| 3 | Modeling of shear wave velocity in limestone by soft computing methods显示文摘The main purpose of current study is development of an intelligent model for estimation of shear wave velocity in limestone. Shear wave velocity is one of the most important rock dynamic parameters.Because rocks have complicated structure, direct determination of this parameter takes time, spends expenditure and requires accuracy. On the other hand, there are no precise equations for indirect determination of it; most of them are empirical. By using data sets of several dams of Iran and neuro-genetic,adaptive neuro-fuzzy inference system(ANFIS), and gene expression programming(GEP) methods, models are rendered for prediction of shear wave velocity in limestone. Totally, 516 sets of data has been used for modeling. From these data sets, 413 ones have been utilized for building the intelligent model, and 103 have been used for their performance evaluation. Compressional wave velocity(V_ p), density(γ)and porosity(n), were considered as input parameters. Respectively, the amount of R for neuro-genetic and ANFIS networks was 0.959 and 0.963. In addition, by using GEP, three equations are obtained; the best of them has 0.958 R. ANFIS shows the best prediction results, whereas GEP indicates proper equations. Because these equations have accuracy, they could be used for prediction of shear wave velocity for limestone in the future. | Behnia Danial Ahangari Kaveh Moeinossadat Sayed Rahim | 2017 | International Journal of Mining Science and Technology2017，27，3： | 2 |
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