Evaluation of the compression index of soils using an artificial neural network

Park, Hyun Il; Lee, Seung Rae

doi:10.1016/j.compgeo.2011.02.011

Cited by 111 publications

(37 citation statements)

References 27 publications

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“…The accuracy of the proposed models (see Table 3) for predicting C c is compared with correlations presented earlier in [12][13][14][15][16][17][18][19][20][21][22] (see Table 1), which are shown in Table 4.…”

Section: Modelling Compression Index Using a Polynomial Functionmentioning

confidence: 99%

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Prediction of Compression Index of Saturated Clays (Cc) Using polynomial models

MolaAbasi¹,

Shooshpasha²,

Ebrahimi³

2016

Scientia Iranica

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Abstract. Settlement-based design for shallow foundation realizing consolidation aspect is a major task of geotechnical engineer. Compression index (C c ) from the oedometer test is used to estimate the consolidation settlement of clays. Since the determination of C c from oedometer tests is relatively time-consuming, empirical equations, based on index properties, can be useful for settlement estimation. Empirical correlations have been proposed to relate Cc of clay deposits to other soil parameters. New polynomial models are proposed for correlation. In order to assess the merits of the proposed approach, a database containing 352 data points has been compiled from case histories via geotechnical investigation sites in the province of Mazandaran, along southern shoreline of the Caspian Sea, Iran. We compare our results, involving polynomial tting with earlier results of statistical correlation relations, with other geotechnical soil properties. The predicted values using our model are checked with the measured ones to evaluate the performance of the polynomial model. The results suggest that the newly proposed approach of correlation provides a means for recognizing, more e ciently, the patterns in the data and predicting the Cc, reliably.

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Section: Modelling Compression Index Using a Polynomial Functionmentioning

confidence: 99%

“…liquid limit (LL %), natural water content (! n %), or in-situ void ratio (e 0 ) [12][13][14][15][16][17][18][19]. However, others recommend multiple soil-parameter models [12][13][14][20][21][22][23][24] for the estimation of C c .…”

Section: Introductionmentioning

confidence: 99%

Prediction of Compression Index of Saturated Clays (Cc) Using polynomial models

MolaAbasi¹,

Shooshpasha²,

Ebrahimi³

2016

Scientia Iranica

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“…With AI, a learning mechanism often contributes to constructing the intelligent structure of an estimation model. Among the popular AI methods, ANNs present a robust artificial tool that is widely used to predict Cc [7,[22][23][24][25][26]. AI techniques have been reported to have an acceptable statistical performance in terms of correlation.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Compression Index of Fine-Grained Soils Using a Gene Expression Programming Model

et al. 2019

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In construction projects, estimation of the settlement of fine-grained soils is of critical importance, and yet is a challenging task. The coefficient of consolidation for the compression index (Cc) is a key parameter in modeling the settlement of fine-grained soil layers. However, the estimation of this parameter is costly, time-consuming, and requires skilled technicians. To overcome these drawbacks, we aimed to predict Cc through other soil parameters, i.e., the liquid limit (LL), plastic limit (PL), and initial void ratio (e0). Using these parameters is more convenient and requires substantially less time and cost compared to the conventional tests to estimate Cc. This study presents a novel prediction model for the Cc of fine-grained soils using gene expression programming (GEP). A database consisting of 108 different data points was used to develop the model. A closed-form equation solution was derived to estimate Cc based on LL, PL, and e0. The performance of the developed GEP-based model was evaluated through the coefficient of determination (R 2 ), the root mean squared error (RMSE), and the mean average error (MAE). The proposed model performed better in terms of R 2 , RMSE, and MAE compared to the other models.

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“…Soft computing techniques such as artificial neural networks (ANN) are widely accepted and popular along the conventional statistical methods (e.g., regression) [11][12][13][14][15][16][17][18][19][20][21] . These techniques were successfully applied to different geotechnical problems such as Cc prediction [7,[22][23][24][25][26][27]. However, a major limitation of common soft computing techniques is that no closed-form prediction equation is provided by them.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Prediction Model for Fine-Grained Soils Compression Index Using Gene-Expression Programming

Mohammadzadeh¹,

Kazemi²,

Mosavi³

2019

Preprint

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Appropriate estimation of soil settlement is of significant importance since it directly influences the performance of building and infrastructures that are built on soil. In particular, the settlement of fine-grained soils is critical because of low permeability and continuous settlement with time.Coefficient of consolidation (Cc) is a key parameter to estimate settlement of fine-grained soil layers. However, estimation of this parameter is time consuming, needs skilled technicians, and specific equipment. In this study, Cc was estimated using several soil parameters such as liquid limit (LL), plastic limit (PL), and initial void ratio (e0). Estimating such parameters in laboratory is straight forward and needs substantially less time and cost compared to conventional tests to estimate Cc such as oedometer test. This study presents a novel prediction model for Cc of finegrained soils using gene-expression programming (GEP). GEP is a biologically inspired technique capable of offering closed-form solution for the optimal solution. A database consisted of 108 different data points was used to develop the model. A closed-form equation solution was derived to estimate Cc based on LL, PL, and e0. The performance of developed GEP-based model was evaluated through coefficient of determination (R 2 ), root mean squared error (RMSE), and mean average error (MAE). High R 2 and low error values indicated the descent performance of the model. Furthermore, the model was evaluated using the additional performance measures and met all the suggested criteria. Furthermore, the model had a better performance in terms of R 2 , RMSE, and MAE compared to most of existing models. It is expected that the developed model will decrease the time and cost associate with determining Cc of fine-grained soils.

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Evaluation of the compression index of soils using an artificial neural network

Cited by 111 publications

References 27 publications

Prediction of Compression Index of Saturated Clays (Cc) Using polynomial models

Prediction of Compression Index of Saturated Clays (Cc) Using polynomial models

Prediction of Compression Index of Fine-Grained Soils Using a Gene Expression Programming Model

Evolutionary Prediction Model for Fine-Grained Soils Compression Index Using Gene-Expression Programming

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