Intelligent Control of Smooth Blasting Quality in Rock Tunnels Using BP-ANN, ENN, and ANFIS

Abstract

The construction quality of tunnel smooth blasting is difficult to control and fluctuates greatly. Moreover, the existing technology, which relies on the visual observation, empirical judgment, and artificial control, has difficulty meeting the requirements of tunnel smooth blasting construction quality control. This paper presents the construction principle of a tunnel smooth blasting quality control system, introduces a process quality control technology into quality control of tunnel smooth blasting construction, designs a framework for the tunnel smooth blasting quality control system, and collects control index data based on field investigation, expert consultation, and experimental research. By using the methods of index utilization rate statistics, gray correlation analysis, and principal component analysis, this paper primarily elects and selects the control indexes; establishes the tunnel smooth blasting quality control index system; constructs a comprehensive optimization control model of tunnel smooth blasting quality using back propagation artificial neural network (BP-ANN), Elman neural network (ENN), and adaptive neuro fuzzy inference systems (ANFIS); and studies the tunnel smooth blasting quality control system. The following are the conclusions of this study: (1) This paper presented a method of constructing a tunnel smooth blasting quality control index system and established this system with seven criteria layers, namely, geological conditions, explosive properties, borehole parameters, charge parameters, method of initiation, tunnel parameters, and construction factors, as well as a total of nine indexes. (2) The comprehensive optimization control model of BP-ANN, ANFIS, and ENN for tunnel smooth blasting quality was established. (3) The uniform design method was used to optimize the blasting parameters of the tunnel section, which needs to be controlled, and verify that the construction of these comprehensive optimization control models can change the focus of tunnel smooth blasting quality control from the traditional single index control method into a dynamic, intelligent, pluralistic, and integrated control technology.

Numerical Simulation of Rock Mass Structure Effect on Tunnel Smooth Blasting Quality: A Case Study

“…Sari et al [22] established a Monte Carlo stochastic model on the basis of controllable parameter adjustment to predict overbreak and underbreak. Zou [23] et al proposed the constructing method of tunnel smooth blasting quality control index system and established the smooth blasting quality control index system with levels of geological conditions, explosive properties, borehole parameters, charging parameters, initiation method, tunnel parameters, and construction factors as indices.…”

In-site Investigation of Rock Mass Structure Effect on Tunnel Smooth Blasting Quality

“…To achieve better blasting results, Feng et al [13] optimized blasting parameters such as hole spacing and smooth layer depth through a tunnel smooth blasting simulation. Zou et al [14] used a neural network to build a comprehensive optimal control model for tunnel smooth blasting quality and studied a tunnel smooth blasting quality control system. Liu et al [15] used field smooth-blasting test data as training samples for machine learning.…”

Prediction and Analysis Method of Mine Blasting Quality Based on GA-BP Neural Network

“…e rock stripping and ore recovery of large-scale open-pit iron mines mostly use deep hole blasting, which has a high degree of blasting mechanization, fast construction speed, and relatively concentrated blasting [1]. In order to accurately predict the ore-rock boundary after blasting, guide shoveling equipment to accurately locate high-grade ore, and minimize ore dilution caused by waste rock mixing during blasting, it is necessary to explore and study the movement of broken ore rock during blasting [2].…”