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Die folgende Bibliografie enthält alle in dieser Datenbank indizierten Veröffentlichungen, die mit diesem Namen als Autor, Herausgeber oder anderweitig Beitragenden verbunden sind.

  1. McMillan, Lauren / Fayaz, Jawad / Varga, Liz (2023): Flow forecasting for leakage burst prediction in water distribution systems using long short_term memory neural networks and Kalman filtering. In: Sustainable Cities and Society, v. 99 (Dezember 2023).

    https://doi.org/10.1016/j.scs.2023.104934

  2. Fayaz, Jawad / Torres‐Rodas, Pablo / Medalla, Miguel / Naeim, Farzad (2023): Assessment of ground motion amplitude scaling using interpretable Gaussian process regression: Application to steel moment frames. In: Earthquake Engineering and Structural Dynamics, v. 52, n. 8 (März 2023).

    https://doi.org/10.1002/eqe.3810

  3. Fayaz, Jawad / Medalla, Miguel / Torres-Rodas, Pablo / Galasso, Carmine (2023): A recurrent-neural-network-based generalized ground-motion model for the Chilean subduction seismic environment. In: Structural Safety, v. 100 (Januar 2023).

    https://doi.org/10.1016/j.strusafe.2022.102282

  4. Otárola, Kenneth / Fayaz, Jawad / Galasso, Carmine (2022): Fragility and vulnerability analysis of deteriorating ordinary bridges using simulated ground‐motion sequences. In: Earthquake Engineering and Structural Dynamics, v. 51, n. 13 (25 Oktober 2022).

    https://doi.org/10.1002/eqe.3720

  5. Fayaz, Jawad / Galasso, Carmine (2022): A generalized ground-motion model for consistent mainshock–aftershock intensity measures using successive recurrent neural networks. In: Bulletin of Earthquake Engineering, v. 20, n. 12 (August 2022).

    https://doi.org/10.1007/s10518-022-01432-w

  6. Tubaldi, Enrico / Turchetti, Francesca / Ozer, Ekin / Fayaz, Jawad / Gehl, Pierre / Galasso, Carmine (2022): A Bayesian network‐based probabilistic framework for updating aftershock risk of bridges. In: Earthquake Engineering and Structural Dynamics, v. 51, n. 10 (August 2022).

    https://doi.org/10.1002/eqe.3698

  7. Fayaz, Jawad / Galasso, Carmine (2023): A deep neural network framework for real‐time on‐site estimation of acceleration response spectra of seismic ground motions. In: Computer-Aided Civil and Infrastructure Engineering, v. 38, n. 1 (Februar 2023).

    https://doi.org/10.1111/mice.12830

  8. Fayaz, Jawad / Zareian, Farzin (2021): An efficient algorithm to simulate site‐based ground motions that match a target spectrum. In: Earthquake Engineering and Structural Dynamics, v. 50, n. 13 (25 Oktober 2021).

    https://doi.org/10.1002/eqe.3521

  9. Fayaz, Jawad / Rezaeian, Sanaz / Zareian, Farzin (2021): Evaluation of simulated ground motions using probabilistic seismic demand analysis: CyberShake (ver. 15.12) simulations for Ordinary Standard Bridges. In: Soil Dynamics and Earthquake Engineering, v. 141 (Februar 2021).

    https://doi.org/10.1016/j.soildyn.2020.106533

  10. Fayaz, Jawad / Xiang, Yijun / Zareian, Farzin (2021): Generalized ground motion prediction model using hybrid recurrent neural network. In: Earthquake Engineering and Structural Dynamics, v. 50, n. 6 (Mai 2021).

    https://doi.org/10.1002/eqe.3410

  11. Fayaz, Jawad / Dabaghi, Mayssa / Zareian, Farzin (2020): Utilization of Site-Based Simulated Ground Motions for Hazard-Targeted Seismic Demand Estimation: Application for Ordinary Bridges in Southern California. In: Journal of Bridge Engineering (ASCE), v. 25, n. 11 (November 2020).

    https://doi.org/10.1061/(asce)be.1943-5592.0001634

  12. Fayaz, Jawad / Medalla, Miguel / Zareian, Farzin (2020): Sensitivity of the response of Box-Girder Seat-type bridges to the duration of ground motions arising from crustal and subduction earthquakes. In: Engineering Structures, v. 219 (September 2020).

    https://doi.org/10.1016/j.engstruct.2020.110845

  13. Fayaz, Jawad / Zareian, Farzin (2019): Reliability Analysis of Steel SMRF and SCBF Structures Considering the Vertical Component of Near-Fault Ground Motions. In: Journal of Structural Engineering (ASCE), v. 145, n. 7 (Juli 2019).

    https://doi.org/10.1061/(asce)st.1943-541x.0002330

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