Estimating compaction parameters of fine- and coarse-grained soils by means of artificial neural networks

Isik, Fatih; Özden, Gürkan

doi:10.1007/s12665-012-2057-5

Cited by 55 publications

(21 citation statements)

References 38 publications

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“…Artificial neural network (ANN) is a well-known branch of soft computing (Alavi et al 2010). This technique has been successfully employed to solve problems in civil engineering field (e.g., Kayadelen et al 2009;Günaydın 2009;Kolay et al 2010;Das et al 2010;Yilmaz 2010a, b; Akgun and Türk 2010; Kaunda et al 2010;Das et al 2011a, b, c;Mert et al 2011;Mollahasani et al 2011;Yilmaz et al 2012;Sattari et al 2012;Tasdemir et al 2013;Seker 2012, 2013;Isik and Ozden 2013;Alkhasawneh et al 2014;Wu et al 2013;Maiti and Tiwari 2014;Park et al 2013;Ceryan et al 2013;Manouchehrian et al 2014). Besides, ANN has been used to predict the bearing capacity of shallow foundations resting on soil layers (Soleimanbeigi and Hataf 2005;Padmini et al 2008;Kuo et al 2009;Kalinli et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Explicit formulation of bearing capacity of shallow foundations on rock masses using artificial neural networks: application and supplementary studies

et al. 2014

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A major concern in the design of foundations is to achieve a precise estimation of bearing capacity of the underlying soil or rock mass. The present study proposes a new design equation for the prediction of the bearing capacity of shallow foundations on rock masses utilizing artificial neural network (ANN). The bearing capacity is formulated in terms of rock mass rating, unconfined compressive strength of rock, ratio of joint spacing to foundation width, and angle of internal friction for the rock mass. Further, a conventional calculation procedure is proposed based on the fixed connection weights and bias factors of the best ANN structure. A comprehensive database of rock socket, centrifuge rock socket, plate load, and large-scaled footing load test results is used for the model development. Sensitivity and parametric analyses are conducted and discussed. The results clearly demonstrate the acceptable performance of the derived model for estimating the bearing capacity of shallow foundations. The proposed prediction equation has a notably better performance than the traditional equations.

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Section: Introductionmentioning

confidence: 99%

Explicit formulation of bearing capacity of shallow foundations on rock masses using artificial neural networks: application and supplementary studies

et al. 2014

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“…That is one of the most important and most complicated parts of designing neural networks, as there is no single theory or accepted rule for determining the optimal network architecture [59][60][61]. The number of hidden layers and their neurons is mostly determined by trial and error [62,63].…”

Section: Multi-layer Perceptron (Mlp)mentioning

confidence: 99%

“…When the function of the network is complete, network outputs are post-processed so that data can be converted into non-normalized units [28]. For ANN modeling, data are divided randomly into three categories of training, testing, and validation [60]. The network is trained by the rst category of data.…”

Section: Data Preparation and Normalizationmentioning

confidence: 99%

Predicting Shear Wave Velocity of Soil Using Multiple Linear Regression Analysis and Artificial Neural Networks

et al. 2017

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Abstract. This paper investigates the correlation between shear wave velocity and some of the index parameters of soils, including Standard Penetration Test blow counts (SPT), FineContent (FC), soil moisture (W ), Liquid Limit (LL), and Depth (D). The study attempts to show the application of arti cial neural networks and multiple regression analysis to the prediction of the shear wave velocity (V S ) value of soils. New prediction equations are suggested to correlate VS with the mentioned parameters based on a dataset collected from Mashhad city in the north east of Iran. The results suggest that, in the case of ANN method use, highly accurate correlations in the estimation of VS are acquired. The predicted values using ANN method are checked against the real values of V S to evaluate the performance of this method. The minimum correlation coe cient obtained in ANN method is higher than the maximum correlation coe cient obtained from the MLR. In addition, the value of estimation error in the ANN method is much less than that in the MLR method, indicating the role of higher con dence coe cient of the ANN in estimating VS of soil.

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“…For repeated measurements, the machine learning methods accounted for the spatial structure of errors, but the temporal structure error was neglected by assuming independent ground measurements between field campaigns. In contrast, certain methods that can handle mixed effects can be considered in future analyses [74,75]. Furthermore, HJ-CCD images and derived VIs were used in this study, and even though satisfactory results were obtained, the comparatively narrow spectral range still limited the more powerful VIs (e.g., reduced simple ratio (RSR) and cellulose absorption index (CAI)) used.…”

Section: Model Comparison and Study Limitationsmentioning

confidence: 99%

Predicting Grassland Leaf Area Index in the Meadow Steppes of Northern China: A Comparative Study of Regression Approaches and Hybrid Geostatistical Methods

Wang

Tang

et al. 2016

Remote Sensing

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Leaf area index (LAI) is a key parameter used to describe vegetation structures and is widely used in ecosystem biophysical process and vegetation productivity models. Many algorithms have been developed for the estimation of LAI based on remote sensing images. Our goal was to produce accurate and timely predictions of grassland LAI for the meadow steppes of northern China. Here, we compare the predictive power of regression approaches and hybrid geostatistical methods using Chinese Huanjing (HJ) satellite charge coupled device (CCD) data. The regression methods evaluated include partial least squares regression (PLSR), artificial neural networks (ANNs) and random forests (RFs). The two hybrid geostatistical methods were regression kriging (RK) and random forests residuals kriging (RFRK). The predictions were validated for different grassland types and different growing stages, and their performances were also examined by adding several groups of vegetation indices (VIs). The two hybrid geostatistical models (RK and RFRK) yielded the most accurate predictions (root mean squared error (RMSE) = 0.21 m 2 /m 2 and 0.23 m 2 /m 2 for RK and RFRK, respectively), followed by the RF model (RMSE = 0.27 m 2 /m 2 ), which was the most accurate among the regression models. These three models also exhibited the best temporal performance across the duration of the growing season. The PLSR and ANN models were less accurate (RMSE = 0.33 m 2 /m 2 and 0.35 m 2 /m 2 for ANN and PLSR, respectively), and the PLSR model performed the worst (exhibiting varied temporal performance and unreliable prediction accuracy that was susceptible to ground conditions). By adding VIs to the predictor variables, the predictions of the PLSR and ANN models were obviously improved (RMSE improved from 0.35 m 2 /m 2 to 0.28 m 2 /m 2 for PLSR and from 0.33 m 2 /m 2 to 0.28 m 2 /m 2 for ANN); the RF and RFRK models did not generate more accurate predictions and the performance of the RK model declined (RMSE decreased from 0.21 m 2 /m 2 to 0.32 m 2 /m 2 ).

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Estimating compaction parameters of fine- and coarse-grained soils by means of artificial neural networks

Cited by 55 publications

References 38 publications

Explicit formulation of bearing capacity of shallow foundations on rock masses using artificial neural networks: application and supplementary studies

Explicit formulation of bearing capacity of shallow foundations on rock masses using artificial neural networks: application and supplementary studies

Predicting Shear Wave Velocity of Soil Using Multiple Linear Regression Analysis and Artificial Neural Networks

Predicting Grassland Leaf Area Index in the Meadow Steppes of Northern China: A Comparative Study of Regression Approaches and Hybrid Geostatistical Methods

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