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A novel combination of PCA and machine learning techniques to select the most important factors for predicting tunnel construction performance

Numerous studies have reported the effective use of artificial intelligence approaches, particularly artificial neural networks (ANNs)-based models, to tackle tunnelling issues. However, having a high number of model inputs increases the running time and related mistakes of ANNs. The principal compo...

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Main Authors: Wang, Jiangfeng, Mohammed, Ahmed Salih, Macioszek, Elzbieta, Ali, Mujahid, Ulrikh, Dmitrii Vladimirovich, Fang, Qiancheng
Format: Article
Published: MDPI 2022
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.um.edu.my/41674/
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spelling my.um.eprints.416742023-10-27T04:55:23Z http://eprints.um.edu.my/41674/ A novel combination of PCA and machine learning techniques to select the most important factors for predicting tunnel construction performance Wang, Jiangfeng Mohammed, Ahmed Salih Macioszek, Elzbieta Ali, Mujahid Ulrikh, Dmitrii Vladimirovich Fang, Qiancheng TA Engineering (General). Civil engineering (General) Numerous studies have reported the effective use of artificial intelligence approaches, particularly artificial neural networks (ANNs)-based models, to tackle tunnelling issues. However, having a high number of model inputs increases the running time and related mistakes of ANNs. The principal component analysis (PCA) approach was used in this work to select input factors for predicting tunnel boring machine (TBM) performance, specifically advance rate (AR). A reliable and precise forecast of TBM AR is desirable and critical for mitigating risk throughout the tunnel building phase. The developed PCAs (a total of four PCAs) were used with the artificial bee colony (ABC) method to predict TBM AR. To assess the created PCA-ANN-ABC model's capabilities, an imperialist competitive algorithm-ANN and regression-based methods for estimating TBM AR were also suggested. To evaluate the artificial intelligence and statistical models, many statistical evaluation metrics were evaluated and generated, including the coefficient of determination (R-2). The findings indicate that the PCA-ANN-ABC model (with R-2 values of 0.9641 for training and 0.9558 for testing) is capable of predicting AR values with a high degree of accuracy, precision, and flexibility. The modelling approach utilized in this study may be used to other comparable studies involving the solution of engineering challenges. MDPI 2022-07 Article PeerReviewed Wang, Jiangfeng and Mohammed, Ahmed Salih and Macioszek, Elzbieta and Ali, Mujahid and Ulrikh, Dmitrii Vladimirovich and Fang, Qiancheng (2022) A novel combination of PCA and machine learning techniques to select the most important factors for predicting tunnel construction performance. Buildings, 12 (7). ISSN 2075-5309, DOI https://doi.org/10.3390/buildings12070919 <https://doi.org/10.3390/buildings12070919>. 10.3390/buildings12070919
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Wang, Jiangfeng
Mohammed, Ahmed Salih
Macioszek, Elzbieta
Ali, Mujahid
Ulrikh, Dmitrii Vladimirovich
Fang, Qiancheng
A novel combination of PCA and machine learning techniques to select the most important factors for predicting tunnel construction performance
description Numerous studies have reported the effective use of artificial intelligence approaches, particularly artificial neural networks (ANNs)-based models, to tackle tunnelling issues. However, having a high number of model inputs increases the running time and related mistakes of ANNs. The principal component analysis (PCA) approach was used in this work to select input factors for predicting tunnel boring machine (TBM) performance, specifically advance rate (AR). A reliable and precise forecast of TBM AR is desirable and critical for mitigating risk throughout the tunnel building phase. The developed PCAs (a total of four PCAs) were used with the artificial bee colony (ABC) method to predict TBM AR. To assess the created PCA-ANN-ABC model's capabilities, an imperialist competitive algorithm-ANN and regression-based methods for estimating TBM AR were also suggested. To evaluate the artificial intelligence and statistical models, many statistical evaluation metrics were evaluated and generated, including the coefficient of determination (R-2). The findings indicate that the PCA-ANN-ABC model (with R-2 values of 0.9641 for training and 0.9558 for testing) is capable of predicting AR values with a high degree of accuracy, precision, and flexibility. The modelling approach utilized in this study may be used to other comparable studies involving the solution of engineering challenges.
format Article
author Wang, Jiangfeng
Mohammed, Ahmed Salih
Macioszek, Elzbieta
Ali, Mujahid
Ulrikh, Dmitrii Vladimirovich
Fang, Qiancheng
author_facet Wang, Jiangfeng
Mohammed, Ahmed Salih
Macioszek, Elzbieta
Ali, Mujahid
Ulrikh, Dmitrii Vladimirovich
Fang, Qiancheng
author_sort Wang, Jiangfeng
title A novel combination of PCA and machine learning techniques to select the most important factors for predicting tunnel construction performance
title_short A novel combination of PCA and machine learning techniques to select the most important factors for predicting tunnel construction performance
title_full A novel combination of PCA and machine learning techniques to select the most important factors for predicting tunnel construction performance
title_fullStr A novel combination of PCA and machine learning techniques to select the most important factors for predicting tunnel construction performance
title_full_unstemmed A novel combination of PCA and machine learning techniques to select the most important factors for predicting tunnel construction performance
title_sort novel combination of pca and machine learning techniques to select the most important factors for predicting tunnel construction performance
publisher MDPI
publishDate 2022
url http://eprints.um.edu.my/41674/
_version_ 1781704545788755968
score 13.211869

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