TBM performance prediction developing a hybrid ANFIS-PNN predictive model optimized by imperialism competitive algorithm

Harandizadeh, H; Armaghani, DJ; Asteris, PG; Gandomi, AH

TBM performance prediction developing a hybrid ANFIS-PNN predictive model optimized by imperialism competitive algorithm

Harandizadeh, H Armaghani, DJ Asteris, PG Gandomi, AH

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Publisher:: Springer
Publication Type:: Journal Article
Citation:: Neural Computing and Applications, 2021, 33, (23), pp. 16149-16179
Issue Date:: 2021-12-01

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Field	Value	Language
dc.contributor.author	Harandizadeh, H
dc.contributor.author	Armaghani, DJ
dc.contributor.author	Asteris, PG
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2022-03-31T03:36:59Z
dc.date.available	2022-03-31T03:36:59Z
dc.date.issued	2021-12-01
dc.identifier.citation	Neural Computing and Applications, 2021, 33, (23), pp. 16149-16179
dc.identifier.issn	0941-0643
dc.identifier.issn	1433-3058
dc.identifier.uri	http://hdl.handle.net/10453/155758
dc.description.abstract	A reliable and accurate prediction of the tunnel boring machine (TBM) performance can assist in minimizing the relevant risks of high capital costs and in scheduling tunneling projects. This research aims to develop a novel hybrid intelligent system, i.e., adaptive neuro-fuzzy inference system (ANFIS)-polynomial neural network (PNN) optimized by the imperialism competitive algorithm (ICA), ANFIS-PNN-ICA for prediction of TBM performance. In fact, the role of ICA in this hybrid system is to optimize the membership functions obtained by ANFIS-PNN model for receiving a higher level of performance prediction. Based on previously published works, seven parameters including the rock quality designation, the rock mass rating, Brazilian tensile strength, rock mass weathering, the uniaxial compressive strength, revolution per minute and thrust force were set as inputs to predict TBM performance. Together with the ANFIS-PNN-ICA model, two single model of PNN and ANFIS were also constructed for comparison purposes. These models were designed conducting several parametric studies on their most important parameters and then, their performance capacities were assessed through the use of several performance indices, e.g., correlation coefficient (R). R values of (0.9642, 0.9654 and 1), (0.9482, 0.9671 and 0.9778) and (0.9652, 0.9642, 0.9898) were obtained for training, testing and all datasets of PNN, ANFIS and ANFIS-PNN-ICA models, respectively. These results revealed that the greater prediction capacity can be provided by the ANFIS-PNN-ICA predictive model compared to ANFIS and PNN models and this hybrid intelligent model can be introduced as an accurate, powerful and applicable technique in the field of TBM performance prediction.
dc.language	English
dc.publisher	Springer
dc.relation.ispartof	Neural Computing and Applications
dc.relation.isbasedon	10.1007/s00521-021-06217-x
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering, 1702 Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	TBM performance prediction developing a hybrid ANFIS-PNN predictive model optimized by imperialism competitive algorithm
dc.type	Journal Article
utslib.citation.volume	33
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1702 Cognitive Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-03-31T03:36:54Z
pubs.issue	23
pubs.publication-status	Published
pubs.volume	33
utslib.citation.issue	23

Abstract:

A reliable and accurate prediction of the tunnel boring machine (TBM) performance can assist in minimizing the relevant risks of high capital costs and in scheduling tunneling projects. This research aims to develop a novel hybrid intelligent system, i.e., adaptive neuro-fuzzy inference system (ANFIS)-polynomial neural network (PNN) optimized by the imperialism competitive algorithm (ICA), ANFIS-PNN-ICA for prediction of TBM performance. In fact, the role of ICA in this hybrid system is to optimize the membership functions obtained by ANFIS-PNN model for receiving a higher level of performance prediction. Based on previously published works, seven parameters including the rock quality designation, the rock mass rating, Brazilian tensile strength, rock mass weathering, the uniaxial compressive strength, revolution per minute and thrust force were set as inputs to predict TBM performance. Together with the ANFIS-PNN-ICA model, two single model of PNN and ANFIS were also constructed for comparison purposes. These models were designed conducting several parametric studies on their most important parameters and then, their performance capacities were assessed through the use of several performance indices, e.g., correlation coefficient (R). R values of (0.9642, 0.9654 and 1), (0.9482, 0.9671 and 0.9778) and (0.9652, 0.9642, 0.9898) were obtained for training, testing and all datasets of PNN, ANFIS and ANFIS-PNN-ICA models, respectively. These results revealed that the greater prediction capacity can be provided by the ANFIS-PNN-ICA predictive model compared to ANFIS and PNN models and this hybrid intelligent model can be introduced as an accurate, powerful and applicable technique in the field of TBM performance prediction.

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