A novel combination of PCA and machine learning techniques to select the most important factors for predicting tunnel construction performance
Document Type
Article
Publication Date
7-1-2022
Abstract
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.
Keywords
PCA, ANN, Artificial bee colony algorithm, TBM advance rate, Hard rock condition
Divisions
sch_civ
Funders
Project of tackling key problems of science and technology in Henan Province (Grant No: 222102320164),Key Scientific Research Project Plan of Henan Province colleges and universities (Grant No: 22B560009)
Publication Title
Buildings
Volume
12
Issue
7
Publisher
MDPI
Publisher Location
ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND