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Gh. Shafabakhsh

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

  1. Firouzinia, M. / Shafabakhsh, Gh. (2018): Investigation of the effect of nano-silica on thermal sensitivity of HMA using artificial neural network. In: Construction and Building Materials, v. 170 (Mai 2018).

    https://doi.org/10.1016/j.conbuildmat.2018.03.067

  2. Sezavar, R. / Shafabakhsh, Gh. / Mirabdolazimi, S. M. (2019): New model of moisture susceptibility of nano silica-modified asphalt concrete using GMDH algorithm. In: Construction and Building Materials, v. 211 (Juni 2019).

    https://doi.org/10.1016/j.conbuildmat.2019.03.114

  3. Shafabakhsh, Gh. / Mirabdolazimi, S. M. / Sadeghnejad, M. (2014): Evaluation the effect of nano-TiO2 on the rutting and fatigue behavior of asphalt mixtures. In: Construction and Building Materials, v. 54 (März 2014).

    https://doi.org/10.1016/j.conbuildmat.2013.12.064

  4. Shafabakhsh, Gh. / Taghipoor, M. / Sadeghnejad, M. / Tahami, S. A. (2015): Evaluating the effect of additives on improving asphalt mixtures fatigue behavior. In: Construction and Building Materials, v. 90 (August 2015).

    https://doi.org/10.1016/j.conbuildmat.2015.04.046

  5. Jahanian, H. R. / Shafabakhsh, Gh. / Divandari, H. (2017): Performance evaluation of Hot Mix Asphalt (HMA) containing bitumen modified with Gilsonite. In: Construction and Building Materials, v. 131 (Januar 2017).

    https://doi.org/10.1016/j.conbuildmat.2016.11.069

  6. Mirabdolazimi, S. M. / Shafabakhsh, Gh. (2017): Rutting depth prediction of hot mix asphalts modified with forta fiber using artificial neural networks and genetic programming technique. In: Construction and Building Materials, v. 148 (September 2017).

    https://doi.org/10.1016/j.conbuildmat.2017.05.088

  7. Tanakizadeh, A. / Shafabakhsh, Gh. (2018): Viscoelastic characterization of aged asphalt mastics using typical performance grading tests and rheological-micromechanical models. In: Construction and Building Materials, v. 188 (November 2018).

    https://doi.org/10.1016/j.conbuildmat.2018.08.043

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