Control of Rock Block Fragmentation Based on the Optimization of Shaft Blasting Parameters

In the construction of shaft, the blockage of the mucking shaft may cause the mud-water inrush disaster. Oversized rock fragmentation is the main cause for the blockage of the mucking shaft in the raise boring machine (RBM) construction method. The influence degree of blasting parameters on rock fra...

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Main Authors: Qingxiang Li, Zhanyou Luo, Man Huang, Jiangbo Pan, Guoshu Wang, Yunxin Cheng
Format: Article
Language:English
Published: Hindawi-Wiley 2020-01-01
Series:Geofluids
Online Access:http://dx.doi.org/10.1155/2020/6687685
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spelling doaj-cac63ffd28c9481598cb70c6d6baef352020-12-28T01:30:29ZengHindawi-WileyGeofluids1468-81232020-01-01202010.1155/2020/6687685Control of Rock Block Fragmentation Based on the Optimization of Shaft Blasting ParametersQingxiang Li0Zhanyou Luo1Man Huang2Jiangbo Pan3Guoshu Wang4Yunxin Cheng5College of Civil EngineeringZhejiang Collaborative Innovation Center for Prevention and Control of Mountain Geologic HazardsCollege of Civil EngineeringZhejiang Communications Construction Group Co. LTDZhejiang Communications Construction Group Co. LTDZhejiang Communications Construction Group Co. LTDIn the construction of shaft, the blockage of the mucking shaft may cause the mud-water inrush disaster. Oversized rock fragmentation is the main cause for the blockage of the mucking shaft in the raise boring machine (RBM) construction method. The influence degree of blasting parameters on rock fragmentation after blasting is quantified by adopting analytic hierarchy process (AHP). On this basis, the shaft blasting maximum rock fragmentation control model based on double hidden layer BP neural network is proposed. Results show that the maximum rock fragmentation discharged from the mucking shaft after blasting should not exceed 1/3 of the diameter of the slag chute. The influence weight of the minimum resistance line that accounts to 15.6%, when AHP is applied for the quantification of the blasting parameters, can be regarded as the most important blasting parameter. The average absolute errors between the predicted value and the actual value of the largest block size control model of the shaft blasting are only 2.6%. The inversion analysis of the model can rapidly obtain the required blasting parameters, which can be used to guide the construction of the tunnel ventilation shaft.http://dx.doi.org/10.1155/2020/6687685
collection DOAJ
language English
format Article
sources DOAJ
author Qingxiang Li
Zhanyou Luo
Man Huang
Jiangbo Pan
Guoshu Wang
Yunxin Cheng
spellingShingle Qingxiang Li
Zhanyou Luo
Man Huang
Jiangbo Pan
Guoshu Wang
Yunxin Cheng
Control of Rock Block Fragmentation Based on the Optimization of Shaft Blasting Parameters
Geofluids
author_facet Qingxiang Li
Zhanyou Luo
Man Huang
Jiangbo Pan
Guoshu Wang
Yunxin Cheng
author_sort Qingxiang Li
title Control of Rock Block Fragmentation Based on the Optimization of Shaft Blasting Parameters
title_short Control of Rock Block Fragmentation Based on the Optimization of Shaft Blasting Parameters
title_full Control of Rock Block Fragmentation Based on the Optimization of Shaft Blasting Parameters
title_fullStr Control of Rock Block Fragmentation Based on the Optimization of Shaft Blasting Parameters
title_full_unstemmed Control of Rock Block Fragmentation Based on the Optimization of Shaft Blasting Parameters
title_sort control of rock block fragmentation based on the optimization of shaft blasting parameters
publisher Hindawi-Wiley
series Geofluids
issn 1468-8123
publishDate 2020-01-01
description In the construction of shaft, the blockage of the mucking shaft may cause the mud-water inrush disaster. Oversized rock fragmentation is the main cause for the blockage of the mucking shaft in the raise boring machine (RBM) construction method. The influence degree of blasting parameters on rock fragmentation after blasting is quantified by adopting analytic hierarchy process (AHP). On this basis, the shaft blasting maximum rock fragmentation control model based on double hidden layer BP neural network is proposed. Results show that the maximum rock fragmentation discharged from the mucking shaft after blasting should not exceed 1/3 of the diameter of the slag chute. The influence weight of the minimum resistance line that accounts to 15.6%, when AHP is applied for the quantification of the blasting parameters, can be regarded as the most important blasting parameter. The average absolute errors between the predicted value and the actual value of the largest block size control model of the shaft blasting are only 2.6%. The inversion analysis of the model can rapidly obtain the required blasting parameters, which can be used to guide the construction of the tunnel ventilation shaft.
url http://dx.doi.org/10.1155/2020/6687685
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