基于3D激光感知的隧道爆破参数优化设计方法
Tunnel Blasting Parameters Optimization Design Method Based on Three-Dimensional Laser Sensing
肖清华 1袁浩 1夏金选 2欧小强 3臧熙玮 2钟德超 2刘志强3
作者信息
- 1. 西南交通大学,四川 成都 610031
- 2. 中铁开发投资集团有限公司,云南 昆明 650500
- 3. 中铁西南科学研究院有限公司,四川 成都 611731
- 折叠
摘要
为解决隧道爆破质量控制困难的问题,探究新型爆破参数优化方法,提出一种 3D激光扫描技术与BP神经网络相结合的实时优化方法,对隧道爆破超欠挖感知及其后续钻爆参数进行实时修正,构建现场扫描方法、数据处理、点云提取与超欠挖图像等工作程序,建立BP神经网络模型、算法并确定模型参数.通过试验验证表明:1)将 3D激光扫描技术应用于隧道爆破超欠挖质量感知中切实可行,与人工测量结果吻合较好,具有快速、实时和准确的优点;2)采用 3D激光扫描方法能够对隧道爆破后的断面轮廓线进行精确评测,在获取爆破超欠挖的精细数据后,通过样本学习完成模型训练能推理出较为合理的爆破参数,改进后的优化方案使平均超挖降低 54.8%.
Abstract
During tunnel blasting construction,the blasting quality is difficult to control.Therefore,a real-time blasting parameter optimization method based on three-dimensional(3D)laser scanning technology and back propagation(BP)neural network is proposed.This method percepts the over-and under-excavation of tunnel blasting and corrects subsequent drilling and blasting parameters in real time.The working procedures of field scanning method,data processing,point cloud extraction,and over-and under-excavation images are constructed.Finally,the BP neural network model,algorithm,and model parameters are established.The verification test results show that:(1)It is feasible to apply 3D laser scanning technology to the over-and under-excavation quality perception of tunnel blasting,which is in good agreement with the manual measurement results,and has the advantages of fast,real-time,and accurate.(2)The 3D laser scanning method can accurately evaluate the profile line of the tunnel after blasting.After obtaining precise data on blasting over-and under-excavation,the model training can be completed through sample learning to infer reasonable blasting parameters.The improved optimization scheme reduces the average over excavation by 54.8%.
关键词
隧道/3D激光扫描/BP神经网络/爆破参数优化/超欠挖识别Key words
tunnel/three-dimensional laser scanning/back propagation neural network/blasting parameters optimization/over-and under-excavation identification引用本文复制引用
基金项目
中国中铁股份有限公司科技研究开发计划项目(实用技术2022-专项-01)
中铁开发投资集团有限公司科技研究开发计划项目(2022-B类-05)
出版年
2024
NETL
NSTL
万方数据