Prediction of UCS and CBR of microsilica-lime stabilized sulfate silty sand using ANN and EPR models; application to the deep soil mixing

Ghorbani, Ali; Hasanzadehshooiili, Hadi

doi:10.1016/j.sandf.2017.11.002

Cited by 79 publications

(23 citation statements)

References 43 publications

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“…ANNs are broadly applied in engineering [22][23][24][25][26][27][28][29]. Also, over the last decades, ANNs have appeared as efficient meta-modelling methods applicable to a wide range of sciences, including material science and structural engineering [30][31][32].…”

Section: Methodsmentioning

confidence: 99%

Neural Prediction of Tunnels’ Support Pressure in Elasto-Plastic, Strain-Softening Rock Mass

2018

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Featured Application: This work can be conjunctly used with the support characteristic curve of circular tunnels to find the optimum time of the installation of the support system in a way to restrict the displacements to a specific value. The approach described in this manuscript facilitates the design of circular tunnels for elasto-plastic, strain-softening rock masses obeying both Mohr-Coulomb and Hoek-Brown strength criteria. Abstract:The prediction of the support pressure (P i ) and the development of the ground reaction curve (GRC) are crucial elements of the convergence-confinement procedure used to design underground structures. In this paper, two different types of artificial neural networks (ANNs) are used to predict the P i of circular tunnels in elasto-plastic, strain-softening rock mass. The developed ANNs consider the stress state, the radial displacement of tunnel and the material softening behavior. Among these parameters, strain softening is the parameter of the deterioration of the material's strength in the plastic zone. The analysis also presents separate solutions for the Mohr-Coulomb and Hoek-Brown strength criteria. In this regard, multi-layer perceptron (MLP) and radial basis function (RBF) ANNs were successfully applied. MLP with the architectures of 15-5-10-1 for the Mohr-Coulomb criteria and 17-5-15-1 for the Hoek-Brown criteria appeared optimum for the prediction of the P i . On the other hand, the RBF networks with the architectures of 15-5-1 for the Mohr-Coulomb criterion and 17-3-12-1 for the Hoek-Brown criterion were found to be the optimum for the prediction of the P i .

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Section: Methodsmentioning

confidence: 99%

Neural Prediction of Tunnels’ Support Pressure in Elasto-Plastic, Strain-Softening Rock Mass

2018

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“…Recently, a series of UCS and California bearing ratio (CBR) tests were carried out on stablized sulfate silty sand with microsilica and lime. According to obtained results, Ghorbani and Hasanzadehshooiili [38] were developed Back Propagation Articial Neural Network (BP-ANN) and Evolutionary Polynomial Regression (EPR) models to predict the UCS values for sulfate silty sand stabilized with different lime and microsilica. Since no study has yet been found to determine the relationship between the mechanical properties of deep mixing columns implemented in salty beds, this study attempts to find out and appropriate equations for compressive strength and elasticity of DSM columns involving salt percentage, age and microsilica additive percentage.…”

Section: Regression Equationsmentioning

confidence: 99%

Laboratory Investigation on the Effect of Microsilica Additive on the Mechanical Behavior of Deep Soil Mixing Columns in Saline Dry Sand

Esmaeili

Astaraki

Yaghouti

et al. 2021

Period. Polytech. Civil Eng.

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Since loose and salty subgrades consider as problematic barriers while constructing new transportation infrastructures such as railway tracks and roads are required, the current study aims to find a solution to stabilize these kinds of subgrades using the deep soil mixing (DSM) technique and micro silica additive. In the present study a series of experimental DSM columns were executed in a salty sand-filled chamber utilizing a laboratory scale DSM apparatuses. In the first step, by adding three salt percentages of 5, 10 and 20 into the original sand, four different sandy subgrades with a relative density of 70% were prepared. Considering three percentages of 10, 15 and 20 for micro silica additive, the water-to-cement ratio of 1, salt percentages of 0, 5, 10 and 20 totally 150 sand-cement columns were constructed in the lab environment. In continuation, unconfined compression strength (UCS) and elasticity modulus of all capped DSM columns have been determined and interpreted using scanning electron microscope (SEM) images at three ages of 7,14 and 28 days. The results indicated that increasing the salinity of subgrade soil from 0 to 20% resulted in a falling UCS and Young module by 28 and 21% for 28-days specimens. Furthermore, as a solution, adding micro silica in cement-water grout up to 15% resulted in enhancing mechanical characteristics of the DSM columns. So that adding 15% microsilica caused a 21 and 42% increase in UCS and elasticity modulus of 28-days samples respectively, executed in subgrade with 20% salt.

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“…In this method, type of functions, number of terms, range of exponents, number of generations, etc. are constraints to control the target [39][40][41].…”

Section: Evolutionary Polynomial Regression (Epr) Is a New Hybrid Regmentioning

confidence: 99%

Evaluation of Strength behaviour of Cement-RHA Stabilized and Polypropylene Fiber Reinforced Clay-Sand Mixtures

Ghorbani

Salimzadehshooiili

2018

Civ Eng J

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In this paper, regarding the high availability of rice husk ash (RHA) in Guilan province, also, to decrease the geo-environmental issues caused by dumping RHA in the environment, different clay-sand mixtures are stabilized using the combination of cement and RHA. Polypropylene (PP) fibers are also used to decrease the growth of tensile cracks and increase the overall strength of samples. As the main scope, effect of sand content (in different conditions: with and without presence of RHA) on the compressive strength of stabilized and reinforced samples is investigated. In this regard, 28 day cured clay-sand samples are prepared and unconfined compressive strength (UCS) tests are conducted and the results are compared. It is obtained that with addition of 20% sand to the clay samples, their UCS increases in both cases of non-RHA and RHA-stabilized samples. Moreover, such behavior has been observed with the length of studied PP fibers. As the second scope, based on the conducted UCS tests on the 7-, 28- and 90- day cured clay samples, compressive strength of non-RHA samples are almost completely achieved in a 28-day curing period, while samples containing RHA continue to strengthening after such a period toward a 90-day curing period. Next, a simple relationship for the prediction of UCS of cement-RHA stabilized and PP reinforced clay is presented based on the evolutionary polynomial regression (EPR) technique. This relationship can be efficiently applied by construction engineers to obtain the appropriate mixture design for the stabilization of clay with cement, RHA and PP fibers.

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Prediction of UCS and CBR of microsilica-lime stabilized sulfate silty sand using ANN and EPR models; application to the deep soil mixing

Cited by 79 publications

References 43 publications

Neural Prediction of Tunnels’ Support Pressure in Elasto-Plastic, Strain-Softening Rock Mass

Neural Prediction of Tunnels’ Support Pressure in Elasto-Plastic, Strain-Softening Rock Mass

Laboratory Investigation on the Effect of Microsilica Additive on the Mechanical Behavior of Deep Soil Mixing Columns in Saline Dry Sand

Evaluation of Strength behaviour of Cement-RHA Stabilized and Polypropylene Fiber Reinforced Clay-Sand Mixtures

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