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Base de données et galerie internationale d'ouvrages d'art et du génie civil

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S. P. Challagulla

La bibliographie suivante contient toutes les publications répertoriées dans la base de données qui sont reliées à ce nom en tant qu'auteur, éditeur ou collaborateur.

Latha, A. Madhavi / Lingeshwaran, N. / Challagulla, S. P. / Manne, Mounika (2023): Examining random forests for predicting elastic floor response spectra involving dynamic primary-secondary structure interaction. Dans: Journal of Building Pathology and Rehabilitation, v. 9, n. 1 (3 novembre 2023).
https://doi.org/10.1007/s41024-024-00410-w
Annamdasu, Madhavi Latha / Challagulla, S. P. / Kontoni, Denise-Penelope N. / Rex, J. / Jameel, Mohammed / Vicencio, Felipe (2024): Artificial neural network-based prediction model of elastic floor response spectra incorporating dynamic primary-secondary structure interaction. Dans: Soil Dynamics and Earthquake Engineering, v. 177 (février 2024).
https://doi.org/10.1016/j.soildyn.2023.108427
Prathyusha, M. / Challagulla, S. P. / Achyutha Kumar Reddy, M. (2023): An experimental investigation of thermal characteristics of graphene oxide and multi walled carbon nanotechnology in cementitious composites. Dans: Journal of Building Pathology and Rehabilitation, v. 8, n. 2 (12 juin 2023).
https://doi.org/10.1007/s41024-023-00323-0
Challagulla, S. P. / Bhargav, N. C. / Parimi, Chandu (2022): Evaluation of damping modification factors for floor response spectra via machine learning model. Dans: Structures, v. 39 (mai 2022).
https://doi.org/10.1016/j.istruc.2022.03.071
Challagulla, S. P. / Parimi, C. / Farsangi, Ehsan Noroozinejad (2022): Effect of Flexibly Attached Secondary Systems on Dynamic Behavior of Light Structures. Dans: Practice Periodical on Structural Design and Construction, v. 27, n. 1 (février 2022).
https://doi.org/10.1061/(asce)sc.1943-5576.0000634
Farsangi, Ehsan Noroozinejad / Pradeep, S. / Parimi, Chandu / Challagulla, S. P. (2021): Estimation of dynamic design parameters for buildings with multiple sliding non-structural elements using machine learning. Dans: International Journal of Structural Engineering, v. 11, n. 2 ( 2021).
https://doi.org/10.1504/ijstructe.2021.10034438

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