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Probabilistic Slope Stability Analysis on a Heavy-Duty Freight Corridor Using a Soft Computing Technique

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Abstract

This study overviews how to strengthen railway embankments using a geo-grid in the cohesive soil embankment layer and the Bishop method to determine a safety factor using Geo-Studio software. The primary goal of this study is to show the relationship between an embankment’s safety factor with and without a geo-grid. These parameters, such as angle of internal friction, cohesion value, and unit weight for both the subsoil and embankment layer, respectively, pull-out resistance, and tensile capacity for geo-grid, have been used as input in this test. The safety factor has increased continually after altering its features and incorporating a geo-grid into the embankment layer. Based on the Geo-Studio results, the ideal choice was to strengthen the railway embankment by adding a geo-grid to the embankment layer and employing a reliable computational technique to analyse the corridor’s probabilistic slope stability for heavy-duty freight trains. The current method, which has been utilised to undertake a probabilistic study of a high embankment of 12.29 m taken by the Ministry of Indian Railways for a heavy-haul freight corridor, consists of four model analyses: convolutional neural networks (CNN), deep neural networks (DNN), artificial neural networks (ANN), and multiple linear regression (MLR). Performance indicators assessed the models’ performance, such as R2, RMSE, RSR, WI, MAE, NS, and PI. According to the analysis of the results, the CNN model outperformed DNN, ANN, and MLR. CNN is, therefore, a trustworthy soft computing technique for determining the safety of a railway embankment slope.

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The author confirms that the data supporting the findings of this study are available from the corresponding author upon reasonable request from the reader.

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Authors and Affiliations

  1. Civil Engineering Department, National Institute of Technology Patna, Patna, India

    Furquan Ahmad, Pijush Samui & S. S. Mishra

Authors
  1. Furquan Ahmad
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  2. Pijush Samui
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  3. S. S. Mishra
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Contributions

FA: research methodology, resources, software, validation, visualisation, original draft, review, and editing writing; PS: guidance. SSM: guidance.

Corresponding authors

Correspondence to Furquan Ahmad, Pijush Samui or S. S. Mishra.

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Annexure

Annexure

Calculating the reliability index (β) and the failure probability (POF).

  1. 1.1.

    Determination of β and POF

Steps: The following steps can be followed:

  1. a)

    Generation of random values of \(c\) 1, ɸ 1, \(\gamma\) 1, \(c\) 2, ɸ 2, \(\gamma\) 2, PR, and TC

  2. b)

    Calculation of FOS using CNN, DNN, ANN, and MLR.

  3. c)

    Calculate \({\mu }_{{\text{FOS}}}\) and \({\sigma }_{{\text{FOS}}}\).

  4. d)

    Calculation of \(\beta\) and POF as per Eq. (2) and Eq. (5), respectively.

Tables 7, 8 and 9

Table 7 Generated soil parameters and estimated FOS
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Table 8 Normalised soil parameters and estimated FOS
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Table 9 FOS with or without Geogrid
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Ahmad, F., Samui, P. & Mishra, S.S. Probabilistic Slope Stability Analysis on a Heavy-Duty Freight Corridor Using a Soft Computing Technique. Transp. Infrastruct. Geotech. (2023). https://doi.org/10.1007/s40515-023-00365-4

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