Prediction of recompression index using GMDH-type neural network based on geotechnical soil properties

Kordnaeij, Afshin; Kalantary, Farzin; Kordtabar, Behrouz; MolaAbasi, Hossein

doi:10.1016/j.sandf.2015.10.001

Cited by 71 publications

(24 citation statements)

References 21 publications

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“…It was found that ANNs provided good predictions to labs results. Kordnaeij et al, (2015) stated that the Atterberg limits, water content, void ratio, dry unit weight and specific gravity of soil can be used as input parameters of to predict Cr via ANN with the help of lab results of soil collected from various places in Iran. They found that ANN provided more accurate predictions than other methods.…”

Section: Applications Of Artificial Neural Networkmentioning

confidence: 99%

Application of Artificial Neural Networks to Predict Soil Recompression Index and Recompression Ratio

Al-Taie

Al-Bayati

2021

Kufa J. Eng.

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Overconsolidated soils are widely encountered in practice where settlement calculations are crucial. The recompression index (Cr) and the recompression ratio (Cr) are considered as one of the most important parameters used in settlement calculations. To achieve this purpose, expensive and time-consuming laboratory tests are usually conducted using undisturbed specimens to obtain the values of these parameters. Various equations derived from regression analysis were proposed to predict consolidation parameters from the physical properties of a soil. In this paper, however, an artificial neural network model (ANN) is proposed to predict Cr and Cr using natural water content, initial void ratio, total unit weight and effective overburden pressure. The proposed ANN model achieved good agreement with the results of one hundred seventy-nine standard one-dimensional consolidation tests collected from previous geotechnical investigations in Baghdad.

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Section: Applications Of Artificial Neural Networkmentioning

confidence: 99%

Application of Artificial Neural Networks to Predict Soil Recompression Index and Recompression Ratio

Al-Taie

Al-Bayati

2021

Kufa J. Eng.

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“…Table 5 shows the capabilities of the proposed models in predicting the K s of clayey soils statistically. In this table, MAPE, RMSE, MAD and R 2 are, respectively, the mean absolute percent error, root mean squared error, mean absolute deviation and absolute fraction of variance, and their corresponding equations are as following [48].…”

Section: Estimation Of K S By Gmdhmentioning

confidence: 99%

“…Genetic algorithms have been used frequently in the GMDH modeling for each neuron, searching its optimal set of connections with the preceding layer. Various studies have been conducted on applying the genetic algorithms optimized GMDHtype NN in geotechnical applications in recent years [46][47][48][49][50][51][52][53].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Subgrade Reaction Modulus of Clayey Soils using Group Method of Data Handling

et al. 2018

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Settlement-based designs for foundations, using subgrade reaction modulus (K s ), is an important technique in geotechnical engineering. Plate load test (PLT) is one of the most commonly applied tests to directly obtain the K s of soils. As the determination of the K s from PLT-especially at depths-is both timeconsuming and costly, it is necessary to develop models that can handle easily measurable characteristics. The suitability of the Group Method of Data Handling (GMDH) polynomial neural network to estimate the K s of clayey soils has been investigated in the present research. In order to develop the GMDH models, 123 data sets from Qazvin, Iran, have been applied. The predictability of the derived equations has been compared with other available equations for clayey soils. The results demonstrated that an improvement in predicting the K s has been achieved. A sensitivity analysis on the best GMDH-based equation shows that the liquid limit (LL) of soils is the most influential parameter on the proposed GMDH model to predict the K s of the clayey soils.

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“…In this eld, Kalantary et al [29], Ardalan et al [30], Mola-Abasi et al [31], and Kordnaeij et al [32] applied polynomial models to predict undrain shear strength of clays, pile bearing capacity, liquefaction induced lateral displacement, shear wave velocity, shear wave velocity, and recompression index of consolidation based on geotechnical soil properties, respectively. Thus, this approach can be used in empirical correlation of zeolitecement-sand mixture's UCS.…”

Section: Modelling Using Polynomial Modelsmentioning

confidence: 99%

Polynomial Models Controlling Strength of Zeolite-Cement-Sand Mixtures

MolaAbasi¹,

Shooshpasha²

2017

Scientia Iranica

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Abstract. The improvement of local soils with cement and zeolite can provide great bene ts, including strengthening slopes in slope stability problems and stabilizing problematic soils to prevent soil liquefaction. Recently, dosage methodologies have been developed for improved soils based on a rational criterion as it exists in concrete technology. The present study aims to quantify the in uence of the amount of cement, zeolite, porosity, and curing time in the evaluation of Uncon ned Compressive Strength (UCS) of zeolitecemented sand mixtures. A program of uncon ned compression tests considering di erent voids ratios, cement contents, zeolite contents, and curing times was performed in this paper. The results show that UCS values of samples substantially increase with increasing zeolite content to an optimum value of 30% after 28 days of curing time. The rate of improvement is approximately between 20 to 80% and 20% to 60% for 28 and 90 days curing times, respectively. Moreover, the polynomial models are shown to be the appropriate ones to estimate UCS values of zeolite-cemented mixtures. Additionally, the sensitivity analysis reveals the in uence of parameters and the contribution of each coe cient to the polynomial model. Cement and zeolite contents are related more strongly among relative density and curing time.

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Prediction of recompression index using GMDH-type neural network based on geotechnical soil properties

Cited by 71 publications

References 21 publications

Application of Artificial Neural Networks to Predict Soil Recompression Index and Recompression Ratio

Application of Artificial Neural Networks to Predict Soil Recompression Index and Recompression Ratio

Prediction of Subgrade Reaction Modulus of Clayey Soils using Group Method of Data Handling

Polynomial Models Controlling Strength of Zeolite-Cement-Sand Mixtures

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