Predicting tunnel boring machine performance through a new model based on the group method of data handling View Full Text


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Article Info

DATE

2018-07-28

AUTHORS

Mohammadreza Koopialipoor, Sayed Sepehr Nikouei, Aminaton Marto, Ahmad Fahimifar, Danial Jahed Armaghani, Edy Tonnizam Mohamad

ABSTRACT

The tunnel boring machine (TBM), developed within the past few decades, is designed to make the process of tunnel excavation safer and more economical. The use of TBMs in civil and mining construction projects is controlled by several factors including economic considerations and schedule deadlines. Hence, improved methods for estimating TBM performance are important for future projects. This paper presents a new model based on the group method of data handling (GMDH) for predicting the penetration rate (PR) of a TBM. In order to achieve this aim, after investigation of the most effective parameters of PR, rock quality designation, uniaxial compressive strength, rock mass rating, Brazilian tensile strength, weathering zone, thrust force per cutter and revolutions per minute were selected and measured to estimate TBM PR. A database composed of 209 datasets was prepared according to the mentioned model inputs and output. Then, based on the most influential factors of GMDH, a series of parametric investigations were carried out on the established database. In the following, five different datasets with different sets of training and testing were selected and used to construct GMDH models. Aside from that, five multiple regression (MR) models/equations were also proposed to predict TBM PR for comparison purposes. After that, a ranking system was used in order to evaluate the obtained results. As a result, performance prediction results of [i.e. coefficient of determination (R2) = 0.946 and 0.924, root mean square error (RMSE) = 0.141 and 0.169 for training and testing datasets, respectively] demonstrated a high accuracy level of GMDH model in estimating TBM PR. Although both methods are applicable for estimation of PR, GMDH is able to provide a higher degree of accuracy and can be introduced as a new model in this field. More... »

PAGES

3799-3813

References to SciGraph publications

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  • 2015-05-20. Application of fuzzy inference system for prediction of rock fragmentation induced by blasting in ARABIAN JOURNAL OF GEOSCIENCES
  • 2016-04-27. Risk assessment and prediction of rock fragmentation produced by blasting operation: a rock engineering system in ENVIRONMENTAL EARTH SCIENCES
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    31 schema:description The tunnel boring machine (TBM), developed within the past few decades, is designed to make the process of tunnel excavation safer and more economical. The use of TBMs in civil and mining construction projects is controlled by several factors including economic considerations and schedule deadlines. Hence, improved methods for estimating TBM performance are important for future projects. This paper presents a new model based on the group method of data handling (GMDH) for predicting the penetration rate (PR) of a TBM. In order to achieve this aim, after investigation of the most effective parameters of PR, rock quality designation, uniaxial compressive strength, rock mass rating, Brazilian tensile strength, weathering zone, thrust force per cutter and revolutions per minute were selected and measured to estimate TBM PR. A database composed of 209 datasets was prepared according to the mentioned model inputs and output. Then, based on the most influential factors of GMDH, a series of parametric investigations were carried out on the established database. In the following, five different datasets with different sets of training and testing were selected and used to construct GMDH models. Aside from that, five multiple regression (MR) models/equations were also proposed to predict TBM PR for comparison purposes. After that, a ranking system was used in order to evaluate the obtained results. As a result, performance prediction results of [i.e. coefficient of determination (R2) = 0.946 and 0.924, root mean square error (RMSE) = 0.141 and 0.169 for training and testing datasets, respectively] demonstrated a high accuracy level of GMDH model in estimating TBM PR. Although both methods are applicable for estimation of PR, GMDH is able to provide a higher degree of accuracy and can be introduced as a new model in this field.
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