Staff View: Developing hybrid ELM-ALO, ELM-LSO and ELM-SOA models for predicting advance rate of TBM

Developing hybrid ELM-ALO, ELM-LSO and ELM-SOA models for predicting advance rate of TBM

Accurate prediction of TBM performance is very important for efficient completion of TBM construction tunnel project. This paper aims to predict the advance rate (AR) of tunnel boring machine (TBM) using three hybrid models by combining three swarm intelligence optimization algorithm (Ant Lion Optim...

Full description

Saved in:

Bibliographic Details
Main Authors:	Li, Chuanqi, Zhou, Jian, Tao, Ming, Du, Kun, Wang, Shaofeng, Armaghani, Danial Jahed, Mohamad, Edy Tonnizam
Format:	Article
Published:	Elsevier Ltd 2022
Subjects:	TA Engineering (General). Civil engineering (General)
Online Access:	http://eprints.utm.my/104686/ http://dx.doi.org/10.1016/j.trgeo.2022.100819
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my.utm.104686
record_format	eprints
spelling	my.utm.1046862024-02-25T02:57:20Z http://eprints.utm.my/104686/ Developing hybrid ELM-ALO, ELM-LSO and ELM-SOA models for predicting advance rate of TBM Li, Chuanqi Zhou, Jian Tao, Ming Du, Kun Wang, Shaofeng Armaghani, Danial Jahed Mohamad, Edy Tonnizam TA Engineering (General). Civil engineering (General) Accurate prediction of TBM performance is very important for efficient completion of TBM construction tunnel project. This paper aims to predict the advance rate (AR) of tunnel boring machine (TBM) using three hybrid models by combining three swarm intelligence optimization algorithm (Ant Lion Optimizer (ALO), Loin swarm optimization (LSO) and Seagull optimization algorithm (SOA)) and the Extreme learning machine (ELM) model, namely ELM-ALO, ELM-LSO and ELM-SOA model respectively. The dataset consists of 1, 286 samples from the Pahang Selangor Raw Water Transfer (PSRWT) tunnel project in Malaysia, and containing six parameters (rock quality designation (RQD), uniaxial compressive strength (UCS), rock mass rating (RMR), Brazilian tensile strength (BTS), thrust force per cutter (TFC) and revolution per minutes (RPM)) from rock mass and machines. In order to evaluate the prediction performance of different hybrid models, the root mean square error (RMSE), the mean absolute error (MAE), the mean square error (MSE), the determination coefficient (R2), the sum of square error (SSE), and the variance accounted for (VAF) were adopted as the performance indicators. The results show that ELM-LSO is the best model to predict AR. Sensitivity analysis shows the importance of all considerations to AR. TFC, RPM and RMR are the three most important parameters. But this is not absolute, more parameters need to be taken into account in AR prediction. Meanwhile, the ELM-LSO model proposed in this paper can be used as a new method to predict AR. Elsevier Ltd 2022-09 Article PeerReviewed Li, Chuanqi and Zhou, Jian and Tao, Ming and Du, Kun and Wang, Shaofeng and Armaghani, Danial Jahed and Mohamad, Edy Tonnizam (2022) Developing hybrid ELM-ALO, ELM-LSO and ELM-SOA models for predicting advance rate of TBM. Transportation Geotechnics, 36 (NA). pp. 1-12. ISSN 2214-3912 http://dx.doi.org/10.1016/j.trgeo.2022.100819 DOI:10.1016/j.trgeo.2022.100819
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
topic	TA Engineering (General). Civil engineering (General)
spellingShingle	TA Engineering (General). Civil engineering (General) Li, Chuanqi Zhou, Jian Tao, Ming Du, Kun Wang, Shaofeng Armaghani, Danial Jahed Mohamad, Edy Tonnizam Developing hybrid ELM-ALO, ELM-LSO and ELM-SOA models for predicting advance rate of TBM
description	Accurate prediction of TBM performance is very important for efficient completion of TBM construction tunnel project. This paper aims to predict the advance rate (AR) of tunnel boring machine (TBM) using three hybrid models by combining three swarm intelligence optimization algorithm (Ant Lion Optimizer (ALO), Loin swarm optimization (LSO) and Seagull optimization algorithm (SOA)) and the Extreme learning machine (ELM) model, namely ELM-ALO, ELM-LSO and ELM-SOA model respectively. The dataset consists of 1, 286 samples from the Pahang Selangor Raw Water Transfer (PSRWT) tunnel project in Malaysia, and containing six parameters (rock quality designation (RQD), uniaxial compressive strength (UCS), rock mass rating (RMR), Brazilian tensile strength (BTS), thrust force per cutter (TFC) and revolution per minutes (RPM)) from rock mass and machines. In order to evaluate the prediction performance of different hybrid models, the root mean square error (RMSE), the mean absolute error (MAE), the mean square error (MSE), the determination coefficient (R2), the sum of square error (SSE), and the variance accounted for (VAF) were adopted as the performance indicators. The results show that ELM-LSO is the best model to predict AR. Sensitivity analysis shows the importance of all considerations to AR. TFC, RPM and RMR are the three most important parameters. But this is not absolute, more parameters need to be taken into account in AR prediction. Meanwhile, the ELM-LSO model proposed in this paper can be used as a new method to predict AR.
format	Article
author	Li, Chuanqi Zhou, Jian Tao, Ming Du, Kun Wang, Shaofeng Armaghani, Danial Jahed Mohamad, Edy Tonnizam
author_facet	Li, Chuanqi Zhou, Jian Tao, Ming Du, Kun Wang, Shaofeng Armaghani, Danial Jahed Mohamad, Edy Tonnizam
author_sort	Li, Chuanqi
title	Developing hybrid ELM-ALO, ELM-LSO and ELM-SOA models for predicting advance rate of TBM
title_short	Developing hybrid ELM-ALO, ELM-LSO and ELM-SOA models for predicting advance rate of TBM
title_full	Developing hybrid ELM-ALO, ELM-LSO and ELM-SOA models for predicting advance rate of TBM
title_fullStr	Developing hybrid ELM-ALO, ELM-LSO and ELM-SOA models for predicting advance rate of TBM
title_full_unstemmed	Developing hybrid ELM-ALO, ELM-LSO and ELM-SOA models for predicting advance rate of TBM
title_sort	developing hybrid elm-alo, elm-lso and elm-soa models for predicting advance rate of tbm
publisher	Elsevier Ltd
publishDate	2022
url	http://eprints.utm.my/104686/ http://dx.doi.org/10.1016/j.trgeo.2022.100819
_version_	1792147994766737408
score	13.211869

Developing hybrid ELM-ALO, ELM-LSO and ELM-SOA models for predicting advance rate of TBM

Similar Items