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S. P. Challagulla

The following bibliography contains all publications indexed in this database that are linked with this name as either author, editor or any other kind of contributor.

  1. 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. In: Journal of Building Pathology and Rehabilitation, v. 9, n. 1 (3 November 2023).

    https://doi.org/10.1007/s41024-024-00410-w

  2. 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. In: Soil Dynamics and Earthquake Engineering, v. 177 (February 2024).

    https://doi.org/10.1016/j.soildyn.2023.108427

  3. 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. In: Journal of Building Pathology and Rehabilitation, v. 8, n. 2 (12 June 2023).

    https://doi.org/10.1007/s41024-023-00323-0

  4. Challagulla, S. P. / Bhargav, N. C. / Parimi, Chandu (2022): Evaluation of damping modification factors for floor response spectra via machine learning model. In: Structures, v. 39 (May 2022).

    https://doi.org/10.1016/j.istruc.2022.03.071

  5. Challagulla, S. P. / Parimi, C. / Farsangi, Ehsan Noroozinejad (2022): Effect of Flexibly Attached Secondary Systems on Dynamic Behavior of Light Structures. In: Practice Periodical on Structural Design and Construction, v. 27, n. 1 (February 2022).

    https://doi.org/10.1061/(asce)sc.1943-5576.0000634

  6. 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. In: International Journal of Structural Engineering, v. 11, n. 2 ( 2021).

    https://doi.org/10.1504/ijstructe.2021.10034438

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