围岩和掘进工况识别建模数据集

围岩和掘进工况识别建模数据集以操作参数掘进优化决策建模数据集为基础，结合地质勘探信息和围岩台账，基于关键特征进行数据转换融合，选取能反映开挖围岩困难，可作为TBM的开挖负荷的刀盘扭矩和总推进力；选取能表明不同围岩下TBM开挖效率的掘进速度和刀盘转速；以桩号为基准确定各掘进循环的围岩岩性，构成数据集特征空间。

The dataset for surrounding rock and tunneling condition recognition modeling is developed based on the tunneling optimization decision modeling dataset centered on operating parameters. It integrates geological exploration data and surrounding rock inventory records via key feature-based data conversion and fusion. Selected features include cutterhead torque and total thrust force, which can reflect the excavation difficulty of surrounding rock and act as TBM excavation load indicators; tunneling speed and cutterhead rotational speed, which indicate the TBM excavation efficiency under different surrounding rock conditions. The surrounding rock lithology of each tunneling cycle is determined using the stake number as the reference, thereby constructing the feature space of this dataset.

搜集汇总

背景与挑战

背景概述

该数据集专注于围岩和掘进工况识别建模，基于操作参数掘进优化决策建模数据集，融合地质勘探和围岩台账信息，构建了以刀盘扭矩、总推进力、掘进速度和刀盘转速为关键特征的数据集，用于评估开挖围岩困难和TBM开挖效率。数据量较小（1.53MB，5个文件），格式为csv和docx，由浙江大学团队创建，服务于国家重点研发计划项目'全断面隧道掘进装备运行服务平台及智能终端研制'。