Explainable Machine learning on New Zealand strong motion for PGV and PGA SN Somala, S Chanda, K Karthikeyan, S Mangalathu Structures 34, 4977-4985, 2021 | 22 | 2021 |
Duration prediction of Chilean strong motion data using machine learning S Chanda, MC Raghucharan, KSKK Reddy, V Chaudhari, SN Somala Journal of South American Earth Sciences 109, 103253, 2021 | 10 | 2021 |
Spectral acceleration prediction for strike, dip, and rake: a multi-layered perceptron approach SN Somala, S Chanda, MC Raghucharan, E Rogozhin Journal of Seismology 25, 1339-1346, 2021 | 3 | 2021 |
Single-component/single-station–based machine learning for estimating magnitude and location of an earthquake: A support vector machine approach S Chanda, SN Somala Pure and Applied Geophysics 178 (6), 1959-1976, 2021 | 3 | 2021 |
Explainable XGBoost–SHAP Machine-Learning Model for Prediction of Ground Motion Duration in New Zealand SN Somala, S Chanda, M AlHamaydeh, S Mangalathu Natural Hazards Review 25 (2), 04024005, 2024 | | 2024 |
Estimation of the location of the earthquake from single station recordings using Physics-Informed Neural Networks (PINNs) S Chanda, SN Somala AGU Fall Meeting Abstracts 2022, INV44A-03, 2022 | | 2022 |
Focal Mechanism Influence with Azimuth Using Near-Field Simulated Ground Motion: Application to a Multispan Continuous Concrete Single-Frame Box-Girder Bridge SN Somala, S Mangalathu, S Chanda, KSK Karthik Reddy, R Parla Journal of Bridge Engineering 27 (6), 04022034, 2022 | | 2022 |
Uncertainty Quantification of Structural Response Due to Earthquake Loading S Chanda, SN Somala Proceedings of SECON’21: Structural Engineering and Construction Management …, 2022 | | 2022 |
Machine learning algorithms applied to engineering seismology and earthquake engineering S Chanda, SN Somala Indian Institute of Technology Hyderabad., 2021 | | 2021 |
Seismic Hazard of Garhwal Region, Himalaya EA Rogozhin, SN Somala, OO Erteleva, FF Aptikaev, S Chanda Izvestiya, Atmospheric and Oceanic Physics 56, 1315-1325, 2020 | | 2020 |