Prediction of compression index using artificial neural network

Abstract

Over the decades, a number of empirical correlations have been proposed to relate the Compression Index of normally consolidated soils to other soil parameters, such as the natural water content, liquid limit, plasticity index and void ratio. In this article too it has been attempted to establish a correlation between compression index and physical properties for the clayey soils of Mazandaran region. Due to the multiple effects of various parameters, Artificial Neural Network (ANN) has been adapted for predicting the compression index from more simply determined index properties. In order to develop the ANN model, four hundred consolidation tests for soils sampled at 125 construction sites in the province of Mazandaran, in the north of Iran were collected and 90% of these were used to train the prediction model and the other 10% were used to test it. A comparison was carried out between the experimentally measured compression indexes with the predictions. Furthermore, the predictions of a number of previously proposed empirical correlations were obtained using the available data and it has been shown that an improvement of 1-4% with respect to the other correlations has been achieved.

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

“…Regarding to consolidation parameters, ANNs technique was used to investigate the possibility to predict compression index from other simple soil properties such as: Atterberg limits, compaction parameters, specific gravity, void ratio, and water content (Ozer et al, 2008;Park and Lee, 2011;Kalantary and Kordnaeij 2012;Al-Taie et al, 2017). It was found that ANNs provided good predictions to labs results.…”

Prediction of Compression Index of Soft Soils from the Brazilian Coast Using Artificial Neural Networks and Empirical Correlations

“…Many researchers have published empirical correlations estimating C C from soil index properties around the world (e.g. Terzaghi & Peck, 1967;Azzouz et al, 1976;Ozer et al, 2008;Kalantary & Kordnaeij, 2012;McCabe et al, 2014;and Kootahi & Moradi, 2016) and for Brazilian soft soils (Futai et al, 2008;Coutinho & Bello, 2014;Baroni & Almeida, 2017). However, empirical correlations may not be applied to soils elsewhere without consideration of soil origin, and the multiplicity of existing empirical correlations indicates the need of evaluation criteria for their use.…”

“…Due to their learning capacity, ANNs are less influenced by the natural variability of C C and therefore are a potential tool in estimating the parameter. The use of ANN for the C C prediction is presented in some studies (Ozer et al, 2008;Park & Lee, 2011;Kalantary & Kordnaeij, 2012;Kurnaz et al, 2016) and all of them presented satisfactory results.…”

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

“…machines, artificial neural network, adaptive neuro-fuzzy system, etc.-can be a good alternative to traditional regression methods. In recent years, CI methods have been used in several geotechnical investigations [31][32][33][34][35][36][37][38][39][40][41][42].…”