Shear strength estimation of plastic clays with statistical and neural approaches

Göktepe, A. Burak; Altun, Selim; Altıntaş, Gökhan; Tan, Özcan

doi:10.1016/j.buildenv.2007.01.022

Cited by 32 publications

(22 citation statements)

References 16 publications

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“…Our findings agree well with those of other authors: Goktepe et al (2008), for instance, concluded that ANNbased models were more successful in estimating the shear strength of plastic clays with reference to multiple regression-based models. Kaul et al (2005) compared the effectiveness of utilization of regression and ANN models in predicting corn (Zea mays) and soybean (Glycine max) yields and reported that ANN models consistently gave more precise yield predictions than regression models.…”

Section: Comparison Of the Mlr Ann And Anfis Approachessupporting

confidence: 83%

“…Determination of the shear strength of soils in a direct way has been a priority issue for engineers and soil scientists for a long time (Goktepe et al 2008). Many techniques including cone penetrometer, shear vane, torsional shear boxes, direct shear box, and Zhang's method have been developed to directly measure soil shear strength (Rauws and Govers 1988;Zhang et al 2001).…”

Section: Introductionmentioning

confidence: 99%

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Soil shear strength prediction using intelligent systems: artificial neural networks and an adaptive neuro-fuzzy inference system

Besalatpour

Hajabbasi

Ayoubi

et al. 2012

Soil Science and Plant Nutrition

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Surface soil shear strength can be a useful dynamic index for soil erodibility and thus a measure of soil resistance to water erosion. In this study, we evaluated the predictive capabilities of artificial neural networks (ANNs) and an adaptive neuro-fuzzy inference system (ANFIS) in estimating soil shear strength from measured particle size distribution (clay and fine sand), calcium carbonate equivalent (CCE), soil organic matter (SOM), and normalized difference vegetation index (NDVI). The results showed that the ANN model was more feasible in predicting the soil shear strength than the ANFIS model. The root mean square error (RMSE), mean estimation error (MEE), and correlation coefficient (R) between the measured soil shear strength and the estimated values using the ANN model were 0.05, 0.01, and 0.86, respectively. In ANFIS analysis, the RMSE was 0.08 and a lower correlation coefficient of 0.60 was obtained in comparison with the ANN model. Furthermore, the ANN and ANFIS models were more accurate in predicting the soil shear strength than was the conventional regression model. Results indicate that the ANN model might be superior in determining the relationships between index properties and soil shear strength.

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Section: Comparison Of the Mlr Ann And Anfis Approachessupporting

confidence: 83%

Section: Introductionmentioning

confidence: 99%

Soil shear strength prediction using intelligent systems: artificial neural networks and an adaptive neuro-fuzzy inference system

Besalatpour

Hajabbasi

Ayoubi

et al. 2012

Soil Science and Plant Nutrition

View full text Add to dashboard Cite

show abstract

“…The back-propagation algorithm with a gradient search technique (the steepest gradient descent method) would minimize a function equal to the mean square difference between the desired and the actual network outputs, which is widely used for the supervision of neural networks. Since ANN methodology is widely described by hundreds of studies in the past, the reader is advised to check out the literature for further information [20,21]. …”

Section: Artificial Neural Networkmentioning

confidence: 99%

Models for Prediction of Daily Mean Indoor Temperature and Relative Humidity: Education Building in Izmir, Turkey

Özbalta

Sezer

Yıldız

2011

Indoor and Built Environment

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In this research, several models were developed to forecast the daily mean indoor temperature (IT) and relative humidity values in an education building in Izmir, Turkey. The city is located at a hot-humid climatic region. In order to forecast the IT and internal relative humidity (IRH) parameters in the building, a number of artificial neural networks (ANN) models were trained and tested with a dataset including outdoor climatic conditions, day of year and indoor thermal comfort parameters. The indoor thermal comfort parameters, namely, IT and IRH values between 6 June and 21 September 2009 were collected via HOBO data logger. Fraction of variance (R 2 ) and root-mean squared error values calculated by the use of the outputs of different ANN architectures were compared. Moreover, several multiple regression models were developed to question their performance in comparison with those of ANNs. The results showed that an ANN model trained with inconsiderable amount of data was successful in the prediction of IT and IRH parameters in education buildings. It should be emphasized that this model can be benefited in the prediction of indoor thermal comfort conditions, energy requirements, and heating, ventilating and air conditioning system size.

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“…The reason for this is that it is usually not easy to perform the investigations; the most important parameters, such ass cohesion and angle of friction, are very difficult to measure (Goktepe et al 2008). The statistical evaluation of previous field information can provide useful results for new engineering-geologic (or geotechnical) projects.…”

Section: Discussionmentioning

confidence: 99%

Engineering geologic evaluation of overcompacted claystone, new metro line, Budapest

Barsi

Görög

Török

2012

Central European Geology

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The Oligocene clay units in the Budapest area along the new metro (subway) line show different properties than most others. They are denser and of greater strength than most of the unconsolidated ones. This paper provides an overview of their engineering geologic properties using nearly 4700 physical parameter data. These data were obtained from cores representing sampling intervals of the Kiscell Clay, on the Buda side of the Danube River. Seventeen engineering geologic parameters were used in the description of the clay. The parameter analyses show that the clay behaves as a soft rock rather than a soil.

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Shear strength estimation of plastic clays with statistical and neural approaches

Cited by 32 publications

References 16 publications

Soil shear strength prediction using intelligent systems: artificial neural networks and an adaptive neuro-fuzzy inference system

Soil shear strength prediction using intelligent systems: artificial neural networks and an adaptive neuro-fuzzy inference system

Models for Prediction of Daily Mean Indoor Temperature and Relative Humidity: Education Building in Izmir, Turkey

Engineering geologic evaluation of overcompacted claystone, new metro line, Budapest

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