Random forest method-based prediction and control of bridge pier displacements during construction of two overlapped EPBM tunnels. Issue 6 (26th April 2022)

• Record Type: Journal Article
• Authors: Zheng, Gang; Gu, Xingyuan; Zhang, Tianqi; Sun, Jibin; Zheng, Weihong; Fan, Qi; Tong, Jingbo; Diao, Yu
• Is Part Of: European journal of environmental and civil engineering. Volume 26:Issue 6(2022)
• Page Start: 2273
• Page End: 2293
• Publication Date: 2022-04-26
• Subjects: Bridge piers -- vertical displacements -- artificial intelligence -- random forest -- naive Bayes theory -- EPBM variables; Sustainable engineering -- Periodicals; Civil engineering -- Environmental aspects -- Periodicals; Environmental engineering -- Periodicals; 628
• DOI: 10.1080/19648189.2020.1760141
• Languages: English
• ISSNs: 1964-8189

Abstract

Abstract: In this article, a novel artificial intelligence method, i.e. random forest (RF), was adopted as a computer-aided tool to predict the vertical displacement of several pile-supported bridge piers above two overlapped earth-pressure-balanced machine (EPBM) tunnels. Naive Bayes theory was introduced for the statistical analysis of the results predicted by the RF method to address the operational variables of the EPBMs required to safely pass beneath the bridge piers. The in situ observations indicated that the EPBM variables were reasonably determined based on the fact that the vertical displacements of the concerned piers were successfully controlled within the allowable range.