A Novel Combination of PCA and Machine Learning Techniques to Select the Most Important Factors for Predicting Tunnel Construction Performance
Autor(en): |
Jiangfeng Wang
Ahmed Salih Mohammed Elżbieta Macioszek Mujahid Ali Dmitrii Vladimirovich Ulrikh Qiancheng Fang |
---|---|
Medium: | Fachartikel |
Sprache(n): | Englisch |
Veröffentlicht in: | Buildings, 5 Juli 2022, n. 7, v. 12 |
Seite(n): | 919 |
DOI: | 10.3390/buildings12070919 |
Abstrakt: |
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 (R2). The findings indicate that the PCA-ANN-ABC model (with R2 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. |
Copyright: | © 2022 by the authors; licensee MDPI, Basel, Switzerland. |
Lizenz: | Dieses Werk wurde unter der Creative-Commons-Lizenz Namensnennung 4.0 International (CC-BY 4.0) veröffentlicht und darf unter den Lizenzbedinungen vervielfältigt, verbreitet, öffentlich zugänglich gemacht, sowie abgewandelt und bearbeitet werden. Dabei muss der Urheber bzw. Rechteinhaber genannt und die Lizenzbedingungen eingehalten werden. |
11.5 MB
- Über diese
Datenseite - Reference-ID
10688562 - Veröffentlicht am:
13.08.2022 - Geändert am:
10.11.2022