Construction and field measurement of high-speed railway test embankment built on Indian expansive soil “Black Cotton Soil”

Watanabe, Kenji; Nakajima, Shigeru; Fujiwara, Torajiro; Yoshii, Kyoichiro; Rao, Gyandshwar Kumar

doi:10.1016/j.sandf.2020.08.008

Cited by 9 publications

(3 citation statements)

References 3 publications

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“…The stiffness and strength characteristics of the subgrade and formation were evaluated at various testing phases (the formation layer was replaced prior to each test). Unconfined compression tests were used to determine strength, and a circular plate load test was used to determine stiffness under the Losenhausen piston.These values were contrasted with findings from studies utilising light falling deflectometers, dynamic penetration tests, and pocket penetrometers [17]. The stiffness data from the plate load test was believed to be the most reliable, and the Young's modulus was computed using:…”

Section: Measuring Subgrade Stiffnessmentioning

confidence: 99%

Performance of Artificial Neural Network and Modified Gravitational Search Algorithm Models to Predict Vibration Response of Geocell Reinforced Based Weak Sand

2023

Data Analytics and Artificial Intelligence

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The use of a fast evolving artificial intelligence technology (AIT) to forecast the vibration response of maritime soil based on geocells is explored in this research. The vibration response is represented by an indicator called peak particle velocity (PPV). For the purpose of predicting PPV, the artificial intelligence techniques Artificial Neural Network (ANN) and Modified Gravitational Search Algorithm (MGSA) are employed. To create the dataset for the model, a number of field vibration tests were first performed over the geocell-reinforced beds. PPV variation was investigated by varying the test variables—footing embedment, dynamic load, infill material modulus, width, and depth of geocell mattress placement—during the test performed. The various statistical indicators were determined in order to evaluate the prediction performance of a constructed model. Plate load results on geocell-reinforced foundation beds have been used to validate the proposed hybrid ANN-MGSA model. High accuracy and consistency were found when the findings of the ANN-EHO, JSA, MOA, and RNN method were compared, particularly at predicted and actual resolution levels. A parametric sensitivity has also been examined in order to better understand the behaviour of geocell-reinforced structures

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Section: Measuring Subgrade Stiffnessmentioning

confidence: 99%

Performance of Artificial Neural Network and Modified Gravitational Search Algorithm Models to Predict Vibration Response of Geocell Reinforced Based Weak Sand

2023

Data Analytics and Artificial Intelligence

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“…When moistened, these soils have a propensity to expand and contract in opposite directions. As a result, they are also referred to as "vertisols," "expansive," "swell-shrink," "swelling," "black cotton," and "calamitous" soils [1][2][3][4][5][6][7][8] and occasionally as "engineering cancer" [9]. The term "expansive" refers to soil's propensity to expand and contract in volume in response to seasonal moisture fluctuations, endangering the usefulness of pavement infrastructure [10].…”

Section: Introductionmentioning

confidence: 99%

Predictive modelling of strength characteristics of stabilized medium expansive soils

Haleem Ullah Khan,

Irshad Ahmad

2024

World J. Adv. Eng. Technol. Sci.

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This research explores the use of Artificial Neural Network (ANN) modeling to predict the Unconfined Compression Strength (UCS) of stabilized medium expansive soil, aiming to optimize the parameters influencing soil stabilization. A database comprising 240 data points was compiled from laboratory tests involving seven input parameters: Stabilizers content (SCBA & WMD), Curing Period (CP), Liquid Limit (L.L), Plasticity Index (PI), Specific Gravity (Gs), and Free Swell (FS). ANN modeling, employing Levenberg-Marquardt algorithms, demonstrated successful UCS prediction. Increasing the number of neurons improved model accuracy, with optimum results achieved with 14 neurons. With 14 neurons for LMA, the R and R² value reaches 0.99, 0.98 Moreover, plots between experimental and predicted values shows strong correlation as majority of predicted UCS is closed to line of fit. Also error shows large count of data points closed to line of zero error. Sensitivity analysis highlighted SCBA, WMD, CP, L.L, and P.I as significant contributors to UCS prediction, emphasizing their importance in soil stabilization. The study underscores the effectiveness of ANN modeling in predicting soil strength and recommends 4% SCBA and 20% WMD for optimal stabilization. Overall, this research presents a comprehensive approach to predicting UCS in stabilized expansive soil, offering insights into parameter optimization and model enhancement for future geotechnical applications.

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“…[7] cement-mixed gravelly soil (CGS) slab improvement with geosynthetics and a cohesive non-swelling soil (CNS) layer to reduce water content fluctuations. [8] Soil stabilization techniques are used to improve the characteristics of expansive soil, with chemical stabilization being a suitable method. Calcined clay is used as an admixture to study its effects on soil properties.…”

Section: Introductionmentioning

confidence: 99%

Reviewing the enhancement of expansive soil through different waste material blending

Manjushree.V.Gaikwad,

Sourabh Kumar Singh,

Nagesh. T. Suryavanshi

et al. 2024

World J. Adv. Eng. Technol. Sci.

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This review paper examines the efficacy of blending various materials for the enhancement of expansive soil behavior. Expansive soils, characterized by volumetric changes in response to moisture fluctuations, present significant challenges in engineering projects. The review explores the utilization of waste additives such as quarry dust, sawdust ash, glass powder, stone powder, marble dust, Lime and Polypropylene Fiber, plastic waste strips and waste brick powder Oil Shale Ash, cement kiln dust as well as innovative solutions including organic compounds and nano-materials. The effectiveness of each material blend in mitigating issues such as swelling, shrinkage, and cracking is analyzed. The study underscores the importance of material selection and optimal blending techniques in achieving desired improvements in soil properties. Additionally, the paper highlights the need for continued research and innovation to develop sustainable solutions for managing expansive soils in construction and geotechnical engineering endeavors.

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Construction and field measurement of high-speed railway test embankment built on Indian expansive soil “Black Cotton Soil”

Cited by 9 publications

References 3 publications

Performance of Artificial Neural Network and Modified Gravitational Search Algorithm Models to Predict Vibration Response of Geocell Reinforced Based Weak Sand

Performance of Artificial Neural Network and Modified Gravitational Search Algorithm Models to Predict Vibration Response of Geocell Reinforced Based Weak Sand

Predictive modelling of strength characteristics of stabilized medium expansive soils

Reviewing the enhancement of expansive soil through different waste material blending

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