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Formulation of multi-hazard damage prediction (MhDP) model for tunnelling projects in earthquake and landslide-prone regions: A novel approach with artificial neural networking (ANN)

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Abstract

The two most common natural disasters in the Himalayas are earthquakes and landslides. Disaster-proof auditing is required for ongoing transportation infrastructure projects in this region. The Artificial Neural Networking (ANN) approach is used in the present study to train neural networks with input layers in terms of disaster parameters, structural configuration, and confining medium characteristics. This model will aid in predicting tunnel failure damage states during earthquake and landslide events. The proposed damage indices for various damage states of the portal and lining can be applied to define the co-seismic demand for individual structural elements. Co-seismic design recommendations will be useful in determining transportation infrastructure serviceability in post-disaster conditions.

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Acknowledgements

The earthquake catalogue utilised in this study was provided by the National Center for Seismology (NCS), Ministry of Earth Sciences, New Delhi. The authors appreciate the tunnel data, geotechnical and geological parameters, provided by Northern Railways, Konkan Railway Corporation Limited (KRCL), and Ircon International. The authors thank for the technical and logistical assistance offered by Patel Engineering Limited. The authors are also grateful to the Divisional Commissioner Office of Jammu and Kashmir for granting special permission for fieldwork in Jammu and Kashmir during the COVID-19 pandemic.

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Authors and Affiliations

  1. Department of Civil Engineering, Inha University, Incheon, 22212, Republic of Korea

    Abdullah Ansari

  2. Department of Smart City Engineering, Inha University, Incheon, 22212, Republic of Korea

    Abdullah Ansari

  3. Department of Civil and Urban Engineering, Inje University, Gimhae, 50834, Republic of Korea

    Abdullah Ansari

  4. Department of Civil Engineering, Indian Institute of Technology Delhi, New Delhi, 110 016, India

    Abdullah Ansari, K S Rao & A K Jain

  5. Department of Computer Engineering, Sanjivani College of Engineering, Kopargaon, 423 603, Maharashtra, India

    Anas Ansari

  6. Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei City, 106335, Taiwan

    Anas Ansari

Authors
  1. Abdullah Ansari
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  2. K S Rao
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  3. A K Jain
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  4. Anas Ansari
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Contributions

All authors contributed to the conception, visualisation, methodology, and design aspects of this study. Data collection, data processing, analysis, and materials preparation were performed by Abdullah Ansari and Anas Ansari. Field surveys in Jammu and Kashmir were conducted by Abdullah Ansari and KS Rao. The first draft of the manuscript was written by Abdullah Ansari, and all authors commented on previous versions of the manuscript.

Corresponding author

Correspondence to Abdullah Ansari.

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Communicated by Anand Joshi

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Ansari, A., Rao, K.S., Jain, A.K. et al. Formulation of multi-hazard damage prediction (MhDP) model for tunnelling projects in earthquake and landslide-prone regions: A novel approach with artificial neural networking (ANN). J Earth Syst Sci 132, 164 (2023). https://doi.org/10.1007/s12040-023-02178-y

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  • DOI: https://doi.org/10.1007/s12040-023-02178-y

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