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Hossein Moayedi

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. Moayedi, Hossein / Hayati, Sajad (2018): Applicability of a CPT-Based Neural Network Solution in Predicting Load-Settlement Responses of Bored Pile. In: International Journal of Geomechanics, v. 18, n. 6 (June 2018).

    https://doi.org/10.1061/(asce)gm.1943-5622.0001125

  2. Nazir, Ramli / Moayedi, Hossein / Mosallanezhad, Mansour / Tourtiz, Alireza (2015): Appraisal of reliable skin friction variation in a bored pile. In: Proceedings of the Institution of Civil Engineers - Geotechnical Engineering, v. 168, n. 1 (February 2015).

    https://doi.org/10.1680/geng.13.00140

  3. Qiao, Weibiao / Moayedi, Hossein / Foong, Loke Kok (2020): Nature-inspired hybrid techniques of IWO, DA, ES, GA, and ICA, validated through a k-fold validation process predicting monthly natural gas consumption. In: Energy and Buildings, v. 217 (June 2020).

    https://doi.org/10.1016/j.enbuild.2020.110023

  4. Guo, Zhanjun / Moayedi, Hossein / Foong, Loke Kok / Bahiraei, Mehdi (2020): Optimal modification of heating, ventilation, and air conditioning system performances in residential buildings using the integration of metaheuristic optimization and neural computing. In: Energy and Buildings, v. 214 (May 2020).

    https://doi.org/10.1016/j.enbuild.2020.109866

  5. Moayedi, Hossein / Mu'azu, Mohammed Abdullahi / Foong, Loke Kok (2020): Novel swarm-based approach for predicting the cooling load of residential buildings based on social behavior of elephant herds. In: Energy and Buildings, v. 206 (January 2020).

    https://doi.org/10.1016/j.enbuild.2019.109579

  6. Zhang, Xiliang / Nguyen, Hoang / Bui, Xuan-Nam / Anh Le, Hong / Nguyen-Thoi, Trung / Moayedi, Hossein / Mahesh, Vinyas (2020): Evaluating and Predicting the Stability of Roadways in Tunnelling and Underground Space Using Artificial Neural Network-Based Particle Swarm Optimization. In: Tunnelling and Underground Space Technology, v. 103 (September 2020).

    https://doi.org/10.1016/j.tust.2020.103517

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