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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.

  1. Naser, M. Z. / Kodur, V. K. R. (2017): Comparative fire behavior of composite girders under flexural and shear loading. Dans: Thin-Walled Structures, v. 116 (juillet 2017).

    https://doi.org/10.1016/j.tws.2017.03.003

  2. Naser, M. Z. (2024): Causal diagrams for civil and structural engineers. Dans: Structures, v. 63 (mai 2024).

    https://doi.org/10.1016/j.istruc.2024.106398

  3. Naser, M. Z. / Ross, Brandon / Ogle, Jennifer / Kodur, Venkatesh / Hawileh, Rami / Abdalla, Jamal / Thai, Huu-Tai (2024): Evaluating the Performance of Artificial Intelligence Chatbots and Large Language Models in the FE and PE Structural Exams. Dans: Practice Periodical on Structural Design and Construction, v. 29, n. 2 (mai 2024).

    https://doi.org/10.1061/ppscfx.sceng-1369

  4. Hostetter, Haley / Naser, M. Z. / Randall, Kristina / Murray-Tuite, Pamela (2024): Evacuation preparedness and intellectual disability: Insights from a university fire drill. Dans: Journal of Building Engineering, v. 84 (mai 2024).

    https://doi.org/10.1016/j.jobe.2024.108578

  5. Assad, Maha / Hawileh, Rami / Karaki, Ghada / Abdalla, Jamal / Naser, M. Z. (2023): Assessment of critical parameters affecting the behaviour of bearing reinforced concrete walls under fire exposure. Dans: Journal of Structural Fire Engineering, v. 15, n. 3 (novembre 2023).

    https://doi.org/10.1108/jsfe-07-2023-0029

  6. Hostetter, Haley / Naser, M. Z. (2023): Architectural and Structural Engineering of Nineteenth- and Twentieth-Century Mental Health Institutions and Psychiatric Hospitals with Respect to Fire Causes and Mitigation Strategies. Dans: Journal of Architectural Engineering (ASCE), v. 29, n. 4 (décembre 2023).

    https://doi.org/10.1061/jaeied.aeeng-1643

  7. Naser, M. Z. / Ozyuksel Ciftcioglu, Aybike (2023): Causal discovery and inference for evaluating fire resistance of structural members through causal learning and domain knowledge. Dans: Structural Concrete, v. 24, n. 3 (avril 2023).

    https://doi.org/10.1002/suco.202200525

  8. Guzmán-Torres, J. A. / Domínguez-Mota, F. J. / Martinez-Molina, W. / Naser, M. Z. / Tinoco-Guerrero, G. / Tinoco-Ruíz, J. G. (2023): Damage detection on steel-reinforced concrete produced by corrosion via YOLOv3: A detailed guide. Dans: Frontiers in Built Environment, v. 9 (février 2023).

    https://doi.org/10.3389/fbuil.2023.1144606

  9. Zhou, Huanting / Li, Huaidong / Qin, Han / Liang, Tianfu / Naser, M. Z. (2023): Examining fire response of unilaterally concrete-reinforced web prestressed composite beams with corrugated webs. Dans: Engineering Structures, v. 274 (janvier 2023).

    https://doi.org/10.1016/j.engstruct.2022.115194

  10. Naser, M. Z. (2022): CLEMSON: An Automated Machine-Learning Virtual Assistant for Accelerated, Simulation-Free, Transparent, Reduced-Order, and Inference-Based Reconstruction of Fire Response of Structural Members. Dans: Journal of Structural Engineering (ASCE), v. 148, n. 9 (septembre 2022).

    https://doi.org/10.1061/(asce)st.1943-541x.0003399

  11. Hostetter, Haley / Naser, M. Z. (2022): Characterizing disability in fire evacuation: A progressive review. Dans: Journal of Building Engineering, v. 53 (août 2022).

    https://doi.org/10.1016/j.jobe.2022.104573

  12. Diana Andrushia, A. / Anand, N. / Neebha, T. Mary / Naser, M. Z. / Lublóy, Éva (2022): Autonomous detection of concrete damage under fire conditions. Dans: Automation in Construction, v. 140 (août 2022).

    https://doi.org/10.1016/j.autcon.2022.104364

  13. Mathews, Mervin Ealiyas / Kiran, Tattukolla / Anand, N. / Lublóy, Éva / Naser, M. Z. / Prince Arulraj, G. (2022): Effect of protective coating on axial resistance and residual capacity of self-compacting concrete columns exposed to standard fire. Dans: Engineering Structures, v. 264 (août 2022).

    https://doi.org/10.1016/j.engstruct.2022.114444

  14. Naser, M. Z. (2022): A Faculty’s Perspective on Infusing Artificial Intelligence into Civil Engineering Education. Dans: Journal of Civil Engineering Education, v. 148, n. 4 (octobre 2022).

    https://doi.org/10.1061/(asce)ei.2643-9115.0000065

  15. Guzmán-Torres, José A. / Naser, M. Z. / Domínguez-Mota, Francisco J. (2022): Effective medium crack classification on laboratory concrete specimens via competitive machine learning. Dans: Structures, v. 37 (mars 2022).

    https://doi.org/10.1016/j.istruc.2022.01.061

  16. Naser, M. Z. / Kodur, V. K. (2022): Explainable machine learning using real, synthetic and augmented fire tests to predict fire resistance and spalling of RC columns. Dans: Engineering Structures, v. 253 (février 2022).

    https://doi.org/10.1016/j.engstruct.2021.113824

  17. Degtyarev, V. V. / Naser, M. Z. (2021): Boosting machines for predicting shear strength of CFS channels with staggered web perforations. Dans: Structures, v. 34 (décembre 2021).

    https://doi.org/10.1016/j.istruc.2021.09.060

  18. Zarringol, Mohammadreza / Thai, Huu-Tai / Naser, M. Z. (2021): Application of machine learning models for designing CFCFST columns. Dans: Journal of Constructional Steel Research, v. 185 (octobre 2021).

    https://doi.org/10.1016/j.jcsr.2021.106856

  19. Naser, M. Z. (2021): An engineer's guide to eXplainable Artificial Intelligence and Interpretable Machine Learning: Navigating causality, forced goodness, and the false perception of inference. Dans: Automation in Construction, v. 129 (septembre 2021).

    https://doi.org/10.1016/j.autcon.2021.103821

  20. Naser, M. Z. / Thai, Son / Thai, Huu-Tai (2021): Evaluating structural response of concrete-filled steel tubular columns through machine learning. Dans: Journal of Building Engineering, v. 34 (février 2021).

    https://doi.org/10.1016/j.jobe.2020.101888

  21. Mhanna, Haya H. / Hawileh, Rami A. / Abuzaid, Wael / Naser, M. Z. / Abdalla, Jamal A. (2020): Experimental Investigation and Modeling of the Thermal Effect on the Mechanical Properties of Polyethylene-Terephthalate FRP Laminates. Dans: Journal of Materials in Civil Engineering (ASCE), v. 32, n. 10 (octobre 2020).

    https://doi.org/10.1061/(asce)mt.1943-5533.0003389

  22. Naser, M. Z. (2020): Autonomous Fire Resistance Evaluation. Dans: Journal of Structural Engineering (ASCE), v. 146, n. 6 (juin 2020).

    https://doi.org/10.1061/(asce)st.1943-541x.0002641

  23. Naser, M. Z. / Kodur, V. K. R. (2016): Factors governing onset of local instabilities in fire exposed steel beams. Dans: Thin-Walled Structures, v. 98 (janvier 2016).

    https://doi.org/10.1016/j.tws.2015.04.005

  24. Kodur, V. K. R. / Naser, M. Z. (2015): Effect of local instability on capacity of steel beams exposed to fire. Dans: Journal of Constructional Steel Research, v. 111 (août 2015).

    https://doi.org/10.1016/j.jcsr.2015.03.015

  25. Kodur, V. K. R. / Naser, M. Z. (2014): Effect of shear on fire response of steel beams. Dans: Journal of Constructional Steel Research, v. 97 (juin 2014).

    https://doi.org/10.1016/j.jcsr.2014.01.018

  26. Kodur, V. K. R. / Naser, M. Z. (2019): Designing steel bridges for fire safety. Dans: Journal of Constructional Steel Research, v. 156 (mai 2019).

    https://doi.org/10.1016/j.jcsr.2019.01.020

  27. Kodur, V. K. R. / Naser, M. Z. (2018): Approach for shear capacity evaluation of fire exposed steel and composite beams. Dans: Journal of Constructional Steel Research, v. 141 (février 2018).

    https://doi.org/10.1016/j.jcsr.2017.11.011

  28. Naser, M. Z. (2019): Autonomous and resilient infrastructure with cognitive and self-deployable load-bearing structural components. Dans: Automation in Construction, v. 99 (mars 2019).

    https://doi.org/10.1016/j.autcon.2018.11.032

  29. Naser, M. Z. / Kodur, V. K. R. (2018): Cognitive infrastructure - a modern concept for resilient performance under extreme events. Dans: Automation in Construction, v. 90 (juin 2018).

    https://doi.org/10.1016/j.autcon.2018.03.004

  30. Naser, M. Z. (2018): Deriving temperature-dependent material models for structural steel through artificial intelligence. Dans: Construction and Building Materials, v. 191 (décembre 2018).

    https://doi.org/10.1016/j.conbuildmat.2018.09.186

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