Assessment of tunnel blasting-induced overbreak: A novel metaheuristic-based random forest approach. (March 2023)

• Record Type: Journal Article
• Authors: He, Biao; Armaghani, Danial Jahed; Lai, Sai Hin
• Is Part Of: Tunnelling and underground space technology. Volume 133(2023)
• Publication Date: 2023-03
• Subjects: Tunnel blasting -- Overbreak: random forest -- Metaheuristic algorithms -- Tunicate swarm algorithm; Tunneling -- Periodicals; Underground construction -- Periodicals; Tunnels -- Periodicals; Underground areas -- Periodicals; 624.193
• DOI: 10.1016/j.tust.2022.104979
• Languages: English
• ISSNs: 0886-7798

Abstract

Graphical abstract: Highlights: Three hybrid RF models were developed to predict blast-induced overbreak. The RF-TSA model outperformed other predictive RF models in estimating overbreak. The blast design parameters showed a deep impact on the overbreak generation. The PF was selected as the most influential factor in the overbreak generation. Abstract: Overbreak is a detrimental phenomenon caused by tunnel blasting, which can lead to increased time and cost in the construction schedule. It is very important to establish a model that can accurately predict the overbreak caused by tunnel blasting. To achieve this goal, the random forest (RF) is an ensemble machine learning model optimised by three metaheuristic algorithms to predicted overbreak, i.e. the grey wolf optimiser (GWO), the whale optimisation algorithm (WOA), and the tunicate swarm algorithm (TSA). The primary roles of GWO, WOA, and TSA are to search for the optimal hyper-parameters of the RF model in the solution space. To create the models above, 523 data samples were taken from a highway tunnel in China. The established database comprised seven predictors or inputs, including the number of holes, hole depth, total charge, advance length, rock mass rating, tunnel cross-sectional area, and powder factor. Three hybrid RF-based models (RF-GWO, RF-WOA, and RF-TSA) were constructed to predict overbreak. Subsequently, the performance levels of the developed hybrid models were evaluated according to four indices: the coefficient of determination, the root mean square error, the variance accounted for, and the A-20 index. The results showed that the TSA optimisation algorithm was better than the other two algorithms (WOA and GWO) at finding the best hyper-parameters for the RF model. Moreover, the results of comparative analysis with the single RF model confirmed that the proposed RF-TSA model is a strong solution with high accuracy for tackling the overbreak issues. The results of this study showed that the developed models can provide more accurate overbreak values compared to the intelligent techniques available in the literature; they can be used in practice and similar projects.