Leveraging machine learning techniques to support a holistic performance-based seismic design of civil structures

Zaker Esteghamati, Mohsen

doi:10.1016/b978-0-12-824073-1.00008-3

Interpretable Machine Learning for the Analysis, Design, Assessment, and Informed Decision Making for Civil Infrastructure 2024

DOI: 10.1016/b978-0-12-824073-1.00008-3

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Leveraging machine learning techniques to support a holistic performance-based seismic design of civil structures

Mohsen Zaker Esteghamati

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Landslide Assessment Classification Using Deep Neural Networks Based on Climate and Geospatial Data

Tynchenko,

Kukartsev,

Tynchenko

et al. 2024

Sustainability

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This study presents a method for classifying landslide triggers and sizes using climate and geospatial data. The landslide data were sourced from the Global Landslide Catalog (GLC), which identifies rainfall-triggered landslide events globally, regardless of size, impact, or location. Compiled from 2007 to 2018 at NASA Goddard Space Flight Center, the GLC includes various mass movements triggered by rainfall and other events. Climatic data for the 10 years preceding each landslide event, including variables such as rainfall amounts, humidity, pressure, and temperature, were integrated with the landslide data. This dataset was then used to classify landslide triggers and sizes using deep neural networks (DNNs) optimized through genetic algorithm (GA)-driven hyperparameter tuning. The optimized DNN models achieved accuracies of 0.67 and 0.82, respectively, in multiclass classification tasks. This research demonstrates the effectiveness of GA to enhance landslide disaster risk management.

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Landslide Assessment Classification Using Deep Neural Networks Based on Climate and Geospatial Data

Tynchenko,

Kukartsev,

Tynchenko

et al. 2024

Sustainability

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Leveraging machine learning techniques to support a holistic performance-based seismic design of civil structures

Cited by 1 publication

References 49 publications

Landslide Assessment Classification Using Deep Neural Networks Based on Climate and Geospatial Data

Landslide Assessment Classification Using Deep Neural Networks Based on Climate and Geospatial Data

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