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Mokhtar Mohammadi ORCID

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. Mahmoodzadeh, Arsalan / Nejati, Hamid Reza / Mohammadi, Mokhtar / Hashim Ibrahim, Hawkar / Khishe, Mohammad / Rashidi, Shima / Hussein Mohammed, Adil (2022): Developing six hybrid machine learning models based on gaussian process regression and meta-heuristic optimization algorithms for prediction of duration and cost of road tunnels construction. Dans: Tunnelling and Underground Space Technology, v. 130 (décembre 2022).

    https://doi.org/10.1016/j.tust.2022.104759

  2. Wang, Le / Khishe, Mohammad / Mohammadi, Mokhtar / Mahmoodzadeh, Arsalan (2022): Extreme learning machine evolved by fuzzified hunger games search for energy and individual thermal comfort optimization. Dans: Journal of Building Engineering, v. 60 (novembre 2022).

    https://doi.org/10.1016/j.jobe.2022.105187

  3. Mahmoodzadeh, Arsalan / Nejati, Hamid Reza / Mohammadi, Mokhtar (2022): Optimized machine learning modelling for predicting the construction cost and duration of tunnelling projects. Dans: Automation in Construction, v. 139 (juillet 2022).

    https://doi.org/10.1016/j.autcon.2022.104305

  4. Mahmoodzadeh, Arsalan / Mohammadi, Mokhtar / Nariman Abdulhamid, Sazan / Nejati, Hamid Reza / M Gharrib Noori, Krikar / Hashim Ibrahim, Hawkar / Farid Hama Ali, Hunar (2021): Predicting construction time and cost of tunnels using Markov chain model considering opinions of experts. Dans: Tunnelling and Underground Space Technology, v. 116 (octobre 2021).

    https://doi.org/10.1016/j.tust.2021.104109

  5. Mahmoodzadeh, Arsalan / Mohammadi, Mokhtar / Hashim Ibrahim, Hawkar / Nariman Abdulhamid, Sazan / Farid Hama Ali, Hunar / Mohammed Hasan, Ahmed / Khishe, Mohammad / Mahmud, Hoger (2021): Machine learning forecasting models of disc cutters life of tunnel boring machine. Dans: Automation in Construction, v. 128 (août 2021).

    https://doi.org/10.1016/j.autcon.2021.103779

  6. Mahmoodzadeh, Arsalan / Mohammadi, Mokhtar / M Gharrib Noori, Krikar / Khishe, Mohammad / Hashim Ibrahim, Hawkar / Farid Hama Ali, Hunar / Nariman Abdulhamid, Sazan (2021): Presenting the best prediction model of water inflow into drill and blast tunnels among several machine learning techniques. Dans: Automation in Construction, v. 127 (juillet 2021).

    https://doi.org/10.1016/j.autcon.2021.103719

  7. Mahmoodzadeh, Arsalan / Mohammadi, Mokhtar / Nariman Abdulhamid, Sazan / Hashim Ibrahim, Hawkar / Farid Hama Ali, Hunar / Ghafoor Salim, Sirwan (2021): Dynamic reduction of time and cost uncertainties in tunneling projects. Dans: Tunnelling and Underground Space Technology, v. 109 (mars 2021).

    https://doi.org/10.1016/j.tust.2020.103774

  8. Mahmoodzadeh, Arsalan / Mohammadi, Mokhtar / Hashim Ibrahim, Hawkar / Gharrib Noori, Krikar M. / Nariman Abdulhamid, Sazan / Farid Hama Ali, Hunar (2021): Forecasting sidewall displacement of underground caverns using machine learning techniques. Dans: Automation in Construction, v. 123 (mars 2021).

    https://doi.org/10.1016/j.autcon.2020.103530

  9. Mahmoodzadeh, Arsalan / Mohammadi, Mokhtar / Daraei, Ako / Farid Hama Ali, Hunar / Kameran Al-Salihi, Nawzad / Mohammed Dler Omer, Rebaz (2020): Forecasting maximum surface settlement caused by urban tunneling. Dans: Automation in Construction, v. 120 (décembre 2020).

    https://doi.org/10.1016/j.autcon.2020.103375

  10. Mahmoodzadeh, Arsalan / Mohammadi, Mokhtar / Daraei, Ako / Faraj, Rabar H. / Mohammed Dler Omer, Rebaz / Sherwani, Aryan Far H. (2020): Decision-making in tunneling using artificial intelligence tools. Dans: Tunnelling and Underground Space Technology, v. 103 (septembre 2020).

    https://doi.org/10.1016/j.tust.2020.103514

  11. Mahmoodzadeh, Arsalan / Mohammadi, Mokhtar / Daraei, Ako / Rashid, Tarik A. / Sherwani, Aryan Far H. / Faraj, Rabar H. / Darwesh, Aso M. (2019): Updating ground conditions and time-cost scatter-gram in tunnels during excavation. Dans: Automation in Construction, v. 105 (septembre 2019).

    https://doi.org/10.1016/j.autcon.2019.04.017

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