WITHDRAWN: Use of geo-grid reinforcement and stone column for strengthening of mat foundation base

Jain, Sanjaya Kumar; Nusari, Mohammed; Acharya, Indra Prasad

doi:10.1016/j.matpr.2020.10.757

Cited by 2 publications

(1 citation statement)

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“…Additional knowledge of how ballast and georgic behave in a railway application may aid in the advancement of useful design techniques. In terms of cost and the environment, such an application may have an effect on future train design and track restoration [9]. To sustain the repeated stress caused by train passes, ballast acts as a foundation to absorb energy, drain easily, and withstand pressures acting both vertically and laterally (Selig and Waters).…”

Section: Introductionmentioning

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: Introductionmentioning

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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Numerical Investigation of Dynamic Stress Distribution in a Railway Embankment Reinforced by Geogrid Based Weak Soil Formation Using Hybrid RNN-EHO

Balasubramani¹,

Venkatakrishnaiah²,

Raju³

2022

Advancements in Smart Computing and Information Security

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WITHDRAWN: Use of geo-grid reinforcement and stone column for strengthening of mat foundation base

Cited by 2 publications

References 28 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

Numerical Investigation of Dynamic Stress Distribution in a Railway Embankment Reinforced by Geogrid Based Weak Soil Formation Using Hybrid RNN-EHO

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