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Mohamed T. Elnabwy ORCID

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

  1. Elnabwy, Mohamed T. / Khalaf, Diaa / Mlybari, Ehab A. / Elbeltagi, Emad: An integrated machine learning approach for evaluating critical success factors influencing project portfolio management adoption in the construction industry. In: Engineering, Construction and Architectural Management.

    https://doi.org/10.1108/ecam-05-2024-0537

  2. Salem, Mohamed / Al-Sabah, Ruqaya S. / Elnabwy, Mohamed T. / Elbeltagi, Emad / Tantawy, Mohamed (2024): Critical Success Factors for the Widespread Adoption of Virtual Alternative Dispute Resolution (VADR) in the Construction Industry: A Structural Equation Modeling Analysis. In: Buildings, v. 14, n. 9 (25 August 2024).

    https://doi.org/10.3390/buildings14093033

  3. Yamany, Mohamed S. / Elbaz, Mohamed M. / Abdelaty, Ahmed / Elnabwy, Mohamed T. (2024): Leveraging convolutional neural networks for efficient classification of heavy construction equipment. In: Asian Journal of Civil Engineering, v. 25, n. 8 (24 September 2024).

    https://doi.org/10.1007/s42107-024-01159-w

  4. Shaker, Rana Ahmed / Elbeltagi, Emad / Motawa, Ibrahim / Elmasoudi, Islam / Elnabwy, Mohamed T. (2024): Social network analysis for identifying the significant drivers of off-site construction adoption in Egypt. In: Built Environment Project and Asset Management, v. 14, n. 4 (3 Juli 2024).

    https://doi.org/10.1108/bepam-10-2023-0188

  5. Elnabwy, Mohamed T. / Elbeltagi, Emad / El Banna, Mahmoud M. / Alshahri, Abdullah H. / Hu, Jong Wan / Choi, Byoung Gil / Kwon, Yong Hee / Kaloop, Mosbeh R. (2024): Harbor Sedimentation Management Using Numerical Modeling and Exploratory Data Analysis. In: Advances in Civil Engineering, v. 2024 (Januar 2024).

    https://doi.org/10.1155/2024/1209460

  6. Abdellatief, Mohamed / Hassan, Youssef M. / Elnabwy, Mohamed T. / Wong, Leong Sing / Chin, Ren Jie / Mo, Kim Hung (2024): Investigation of machine learning models in predicting compressive strength for ultra-high-performance geopolymer concrete: A comparative study. In: Construction and Building Materials, v. 436 (Juli 2024).

    https://doi.org/10.1016/j.conbuildmat.2024.136884

  7. Kaloop, Mosbeh R. / Elbeltagi, Emad / Elnabwy, Mohamed T. (2015): Bridge Monitoring with Wavelet Principal Component and Spectrum Analysis Based on GPS Measurements: Case Study of the Mansoura Bridge in Egypt. In: Journal of Performance of Constructed Facilities (ASCE), v. 29, n. 3 (Juni 2015).

    https://doi.org/10.1061/(asce)cf.1943-5509.0000559

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