Geotechnical Aspects of Various Constructions along the Canal Embankment using Rice Husk Ash as Stabilizer

Nagaraju, T. Vamsi; P.V.V, Satyanarayana

doi:10.1007/978-981-13-0559-7_16

Cited by 13 publications

(7 citation statements)

References 4 publications

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“…Knowledge metrices defined from Equation (6) to Equation ( 8) consist of 𝑔𝑏𝑒𝑠𝑡 hich is the personal staff best, while 𝛼 and 𝛽 are the learning coefficients of board of is Moore-Penrose generalized inverse of H. The detailed version of the ELM is well documented by Huang et al [38] and Huang et al [39].…”

Section: Csomentioning

confidence: 99%

“…Lateritic soils and expansive clays are most common soils found globally. Most road projects on expansive clays often use strong subbase material and subgrade soil containing a montmorillonite mineral, which offers high swelling and shrinkage and is unsuitable for highway or building projects [5,6]. Under wet conditions, they can swell and lose their strength.…”

Section: Introductionmentioning

confidence: 99%

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Predicting California Bearing Ratio of Lateritic Soils Using Hybrid Machine Learning Technique

Nagaraju¹,

Bahrami

Chinta

et al. 2023

Buildings

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The increase in population has made it possible for better, more cost-effective vehicular services, which warrants good roadways. The sub-base that serves as a stress-transmitting media and distributes vehicle weight to resist shear and radial deformation is a critical component of the pavement structures. Developing novel techniques that can assess the sub-base soil’s geotechnical characteristics and performance is an urgent need. Laterite soil abundantly available in the West Godavari area of India was employed for this research. Roads and highways construction takes a chunk of geotechnical investigation, particularly, California bearing ratio (CBR) of subgrade soils. Therefore, there is a need for intelligent tool to predict or analyze the CBR value without time-consuming and cumbersome laboratory tests. An integrated extreme learning machine-cooperation search optimizer (ELM-CSO) approach is used herein to predict the CBR values. The correlation coefficient is utilized as cost functions of the CSO to identify the optimal activation weights of the ELM. The statistical measures are separately considered, and best solutions are reported in this article. Comparisons are provided with the standard ELM to show the superiorities of the proposed integrated approach to predict the CBR values. Further, the impact of each input variable is studied separately, and reduced models are proposed with limited and inadequate input data without loss of prediction accuracy. When 70% training and 30% testing data are applied, the ELM-CSO outperforms the CSO with Pearson correlation coefficient (R), coefficient of determination (R2), and root mean square error (RMSE) values of 0.98, 0.97, and 0.84, respectively. Therefore, based on the prediction findings, the newly built ELM-CSO can be considered an alternative method for predicting real-time engineering issues, including the lateritic soil properties.

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Section: Csomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting California Bearing Ratio of Lateritic Soils Using Hybrid Machine Learning Technique

Nagaraju¹,

Bahrami

Chinta

et al. 2023

Buildings

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show abstract

“…In recent years, many industrial by-products have been gaining importance because the traditional additives cement and lime are energy intensive and emit CO2 [1,2]. Industrial by-products such as fly ash, pond ash, rice husk ash, bagasse ash, corn cob ash, cotton hull ash, ground granulated blast furnace slag, and silica fume are successful in controlling heave and improving strength [3,4,5]. Chemically altered soils have often been used for pavement subgrade material, embankment film material, and foundation material for lightweight structures [3,4].…”

Section: Introductionmentioning

confidence: 99%

“…Industrial by-products such as fly ash, pond ash, rice husk ash, bagasse ash, corn cob ash, cotton hull ash, ground granulated blast furnace slag, and silica fume are successful in controlling heave and improving strength [3,4,5]. Chemically altered soils have often been used for pavement subgrade material, embankment film material, and foundation material for lightweight structures [3,4]. In most of the rural roads in India, altering the properties of locally available materials is desirable to fulfill both strength and economic requirements.…”

Section: Introductionmentioning

confidence: 99%

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Strength and stiffness prediction models of expansive clays blended with sawdust ash

Alisha¹,

Nagaraju²,

Murty³

et al. 2023

IOP Conf. Ser.: Mater. Sci. Eng.

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Strength and stiffness characteristics are major concern for selecting any geomaterial. However, laboratory testing of these characteristics is time associated, laborious, and high cost. So, there is a need of intelligence tools to estimate the strength and stiffness of geomaterial. The impact of sawdust ash on the stiffness and strength properties of combined expanding clays is discussed in this research. The combined expansive clays underwent tests for California bearing ratio (CBR), unconfined compressive strength (UCS), optimal moisture content, maximum dry density, plasticity characteristics (liquid limit and plastic limit), and differential free swell (DFSI). According to test results, adding more sawdust to the blended clays improves their performance. This study also investigates the artificial neural network (ANN) model that considers six input variables to forecast the CBR and UCS of blended clays. The findings demonstrate that the ANN model performs more accurately for the CBR and UCS models. This clever method may help manage the under- or overestimation of additive dosage and reduce project costs.

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Swell-Compressibility Behavior of Geopolymer Blended Expansive Clays

Nagaraju

Sunil

Chaudhary

2021

Lecture Notes in Civil Engineering

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Geotechnical Aspects of Various Constructions along the Canal Embankment using Rice Husk Ash as Stabilizer

Cited by 13 publications

References 4 publications

Predicting California Bearing Ratio of Lateritic Soils Using Hybrid Machine Learning Technique

Predicting California Bearing Ratio of Lateritic Soils Using Hybrid Machine Learning Technique

Strength and stiffness prediction models of expansive clays blended with sawdust ash

Swell-Compressibility Behavior of Geopolymer Blended Expansive Clays

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