An evolutionary approach for modeling of shear strength of RC deep beams

Gandomi, Amir H.; Yun, Gun Jin; Alavi, Amir H.

doi:10.1617/s11527-013-0039-z

Cited by 200 publications

(58 citation statements)

References 23 publications

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“…Two types of models were developed. Model 1 included four inputs (LL, PL, PI, CF and DPI) and Table 3 Sensitivity analysis of inputs for Model 1 as per Gandomi et al (2013).…”

Section: Resultsmentioning

confidence: 99%

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Prediction of the residual strength of clay using functional networks

Khan

Suman

Pavani

et al. 2016

Geoscience Frontiers

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a b s t r a c tLandslides are common natural hazards occurring in most parts of the world and have considerable adverse economic effects. Residual shear strength of clay is one of the most important factors in the determination of stability of slopes or landslides. This effect is more pronounced in sensitive clays which show large changes in shear strength from peak to residual states. This study analyses the prediction of the residual strength of clay based on a new prediction model, functional networks (FN) using data available in the literature. The performance of FN was compared with support vector machine (SVM) and artificial neural network (ANN) based on statistical parameters like correlation coefficient (R), NashSutcliff coefficient of efficiency (E), absolute average error (AAE), maximum average error (MAE) and root mean square error (RMSE). Based on R and E parameters, FN is found to be a better prediction tool than ANN for the given data. However, the R and E values for FN are less than SVM. A prediction equation is presented that can be used by practicing geotechnical engineers. A sensitivity analysis is carried out to ascertain the importance of various inputs in the prediction of the output. Ó 2015, China University of Geosciences (Beijing) and Peking University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).

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“…Two types of models were developed. Model 1 included four inputs (LL, PL, PI, CF and DPI) and Table 3 Sensitivity analysis of inputs for Model 1 as per Gandomi et al (2013).…”

Section: Resultsmentioning

confidence: 99%

“…In this study, the sensitivity analysis is done according to Gandomi et al (2013). To calculate the sensitivity of the output with respect to any input, the respective input is varied and other parameters are kept equal to their values in accordance with Eqs.…”

Section: Sensitivity Analysismentioning

confidence: 99%

Prediction of the residual strength of clay using functional networks

Khan

Suman

Pavani

et al. 2016

Geoscience Frontiers

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“…Performance of the GMDH, MARS, and the SVR models are demonstrated through their standard statistical errors such as the correlation coefficient (R), root-mean-square-error (RMSE), mean absolute error (MAE), the mean absolute percentage error (MAPE), adjusted coefficient of efficiency (E), and Scatter index (SI). The value of R, RMSE, MAE, MAPE, E, and SI are determined from the following relates given as [66,67]:…”

Section: Model Performance Assessmentmentioning

confidence: 99%

Anticipate Manning’s Coefficient in Meandering Compound Channels

2018

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Estimating Manning's roughness coefficient (n) is one of the essential factors in predicting the discharge in a stream. Present research work is focused on prediction of Manning's n in meandering compound channels by using the Group Method of Data Handling Neural Network (GMDH-NN) approach. The width ratio (α), relative depth (β), sinuosity (s), Channel bed slope (S o ), and meander belt width ratio (ω) are specified as input parameters for the development of the model. The performance of GMDH-NN is evaluated with two different machine learning techniques, namely the support vector regression (SVR) and multivariate adaptive regression spline (MARS) with various statistical measures. Results indicate that the proposed GMDH-NN model predicts the Manning's n satisfactorily as compared to the MARS and SVR model. This GMDH-NN approach can be useful for practical implementation as the prediction of Manning's coefficient and subsequently discharge through Manning's equation in the compound meandering channels are found to be quite adequate.Hydrology 2018, 5, 47 2 of 21 hydraulic parameters. Formulation for Manning's roughness showing the gradient effect in channels having longitudinal slopes greater than 0.002 was derived by Jarrett [7]. Due to variation in flow depth, geometry and slope, the distinction of roughness coefficient in a straight channel as compared to a meandering channel were discussed by Arcement and Schneider [8]. Yen [9] proposed Manning's n for simple uniform flows taking account for a geometric measure, such as unevenness of the edge. Further, SCS method was linearized by James [10] for a meandering channel, named the Linearized SCS (LSCS) and recommended the Manning's roughness value for the different sinuosity channels. Shiono et al. [11] developed a model to predict discharge considering the Manning's n in the case of longitudinal slope and the meander effect of the channel. Jena [12] proposed an equation to calculate the n by considering the effect of channel width, longitudinal channel slope, and the flow depth of the channel. The variations of wetted area, wetted perimeter, and the velocity data on Manning's roughness coefficient by considering the flow simulation and sediment transport in irrigated channels was investigated by Mailapalli et al. [13]. Khatua et al. [14-16] formulated a mathematical equation for roughness coefficients by varying the sinuosity and geometry of the meandering compound channel. Xia et al. [17] and Barati [18-20] carried out the experiments for predicting discharge by taking care of the effect of bed roughness. Dash et al. [21] modeled the Manning's roughness coefficient by considering the aspect ratio, viscosity, slope of the bed, and sinuosity. Pradhan et al. [22] proposed an empirical formulation of predicting Manning's n by dimensional analysis for the compound meandering channel, which affects relative depth, width ratio, longitudinal channel slope, and sinuosity.In the recent years, ML techniques have been successfully implemented for predicting complex phenom...

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“…The main reason behind performing the analysis is to perform a check whether the physics obtained from the models matches with those from the actual understanding about the three experimental studies. Description of the performed sensitivity and parametric analysis is given as follows: The SA percentage of the output (number of days before maintenance is required) to each input variable is determined using the following formulas [38]: Table 9 Multi-objective error of the two models for overall vibration study. Idle run (g-peak) Turning (g-peak) Facing (g-peak)…”

Section: Testing the Robustness Of The Proposed Modelsmentioning

confidence: 99%