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Superior Modeling of Hard Rock TBM Performance Using Novel Predictive Analytics Methodologies

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Authors Superior Modeling of Hard Rock TBM Performance Using Novel Predictive Analytics Methodologies

Masoud Zare Naghadehi - University of Nevada, Reno, USA
  Masoud Samaei - University of Tabriz, Tabriz, Iran
  Masoud Ranjbarnia - University of Tabriz, Tabriz, Iran

Abstract:

In the past decades, TBM performance analysis takes paramount importance among several project management and underground space technology problems on account of its impact on cost estimation, time planning, efficiency improvement and other relative issues. The main aim of this paper is to propose new superior equations for TBM performance prediction in hard rock conditions. The Gene Expression Programming (GEP), and models through the Imperialistic Competitive Algorithm (ICA), are developed in this study, as novel predictive analytics methodologies. The proposed models of this study improve the accuracy of predictive equations developed through a database of TBM performance in one of the most complex tunneling projects in the world.

Keywords:

Performance prediction, Tunnel boring machine (TBM), Predictive modeling, Gene expression programming (GEP), Imperialist competitive algorithm (ICA).

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https://www.civilica.com/Paper-SECM03-SECM03_146.html
COI code: SECM03_146

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Zare Naghadehi, Masoud; Masoud Samaei & Masoud Ranjbarnia, 2019, Superior Modeling of Hard Rock TBM Performance Using Novel Predictive Analytics Methodologies, rd International Conference on Applied Researches in Structural Engineering and Construction Management (secm2019), تهران, دانشگاه صنعتي شريف, https://www.civilica.com/Paper-SECM03-SECM03_146.htmlInside the text, wherever referred to or an achievement of this article is mentioned, after mentioning the article, inside the parental, the following specifications are written.
First Time: (Zare Naghadehi, Masoud; Masoud Samaei & Masoud Ranjbarnia, 2019)
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