Prediction of shear strength parameters of soils using artificial neural networks and multivariate regression methods

Khanlari, Gholamreza; Heidari, Mojtaba; Momeni, Aliakbar; Abdilor, Yasin

doi:10.1016/j.enggeo.2011.12.006

Cited by 78 publications

(25 citation statements)

References 22 publications

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“…[3,4] The activation energy (E at,η ) ( Table 3) ranged from 84.57 to 92.53 kJ/mol for the honeys cipó uva and multi-centrewest, respectively, similar to the values reported in the literature. [8,11,12,42,43] The effect of TSS concentration on E at,η may be described properly (p < 0.05) by means of the power law (Eq. 8) and exponential equations (Eq.…”

Section: Influence Of Temperature and Total Soluble Solids Concentrationmentioning

confidence: 99%

Rheological properties of selected Brazilian honeys as a function of temperature and soluble solid concentration

Silva

Filho

Resende

2017

International Journal of Food Properties

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Steady shear (η) and complex viscosities (η*) are important rheological properties of honeys. In this study, the effects of temperature and total soluble solids (TSS) concentration on these properties in 40 different Brazilian honeys were investigated. All honeys showed liquid-like behaviour at the temperatures and mechanical spectra tested and, except for the orange blossom and multisoutheast samples, respected the Cox-Merz rule. It was found that η varied between 147.3 Pa s and 0.35 Pa s, and η* between 151.33 Pa s and 0.42 Pa s, at 10°C and 60°C, respectively. Four experimental models (Arrhenius, Williams-Landel-Ferry (WLF), Vogel-Tamman-Fulcher (VTF), and power law) were used to evaluate the effect of temperature on η and η*. The Arrhenius model was the most appropriate for estimation of η of all honeys and η* of some. WLF was the most appropriate for predicting η* of the orange blossom, multisoutheast, and multi-southern honeys. Increase in temperature and decrease in TSS concentration lowered these values. The effect of TSS concentration on η and η* at different temperatures could be appropriately described by the power law and exponential models. Simplified models were proposed to determine the η and η* from the combined effect of both factors, which presented R 2 of 0.9540 and 0.9334, and RMSE of 8.00 and 10.44, respectively. Thus, all models obtained in this study provide important tools towards the proper industrialisation of honeys and honey-based products.

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Section: Influence Of Temperature and Total Soluble Solids Concentrationmentioning

confidence: 99%

Rheological properties of selected Brazilian honeys as a function of temperature and soluble solid concentration

Silva

Filho

Resende

2017

International Journal of Food Properties

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“…They concluded that the ANN model outperforms the other models. Khanlari, Heidari, Momeni, and Abdilor (2012) investigated the shear strength parameters of soils by utilizing ANN and multivariate regression (MR) methods. They concluded that these methods can be used for estimation of shear strength parameters of soils.…”

Section: Literature Reviewmentioning

confidence: 99%

Determination of Ultimate Bearing Capacity of Shallow Foundations Using LSSVM Algorithm

Xue

Chen

2019

Journal of Civil Engineering and Management

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Accurate determination of the ultimate bearing capacity (UBC) of shallow foundations is vital for the safety of structures and buildings. Due to the inherent spatial variability characteristics of soil properties, some new approaches are needed to accurately determine the UBC of shallow foundations. The objective of this study is to develop a hybrid least squares support vector machine (LSSVM) and an improved particle swarm optimization (IPSO) algorithm for determining the UBC of shallow foundations. To validate the hybrid IPSO-LSSVM model, a comparison of the predictions was carried out among different models and theoretical methods. Three statistical indexes, namely the root-mean-square error (RMSE), the mean absolute error (MAE) and the correlation coefficient (R) were employed to measure and evaluate the performance of these models. The results showed that the developed hybrid IPSO-LSSVM model can be used for determining the UBC of shallow foundations with high accuracy.

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“…Multivariate regression has been shown to suitable model for various earth sciences studies. Over the past few years, multivariate regression analysis was used widely in different earth sciences for predicting various objectives (e.g., Noori et al, 2010 ;Khanlari et al, 2012;Mokhtari, 2014;Habibi et al, 2014;Granian et al, 2015). Therefore, multivariate linear regression has been used to obtain the relationship between IP, Rs and copper grade in the Abassabad copper mine.…”

Section: Multivariate Regression Analyses (Mra)mentioning

confidence: 99%

Mineral resource estimation using a combination of drilling and IP&Rs data using statistical and cokriging methods

Mostafaei

Ramazi

2018

Bulletin of the Mineral Research and Exploration

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The aim of this research is the mineral resource estimation using a combination of drilling and IP-Rs data. Therefore the approach of this paper is to study the correlation of induced polarization (IP) and Electrical resistivity (Rs) data with drilling data in order to grade estimation and mineral resource estimation. Reducing the boreholes number and optimization of the boreholes location is another aim of this research. The Abassabad copper mine located in Miami-Sabzevar mineralization belt northeast Iran was chosen as a case study. Within the borehole locations, geophysical profiles were designed and surveyed. After IP-Rs data inversion, 2D sections were prepared. The 3D block models of IP-Rs were constructed by geostatistical methods. The correlation between IP-Rs and drilling data were examined by statistical and geostatistical methods using regression, multivariate regression analysis, and cokriging. Based on the mentioned methods copper grade was estimated and the 3D block models of Cu grade were constructed. Obtained models were checked and compared with real Cu model compiled according to drilling data which was done after geophysical measurements. Results showed that the regression between IP data and Cu grade was more appropriate with least error. Rs data are not suitable for Cu estimation, due to changing intervals which led to increasing estimation error. Based on the suggestions of this paper, we could reduce the number of boreholes to 30% of the initial number and optimize the boreholes locations.

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Prediction of shear strength parameters of soils using artificial neural networks and multivariate regression methods

Cited by 78 publications

References 22 publications

Rheological properties of selected Brazilian honeys as a function of temperature and soluble solid concentration

Rheological properties of selected Brazilian honeys as a function of temperature and soluble solid concentration

Determination of Ultimate Bearing Capacity of Shallow Foundations Using LSSVM Algorithm

Mineral resource estimation using a combination of drilling and IP&Rs data using statistical and cokriging methods

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