Abbaszadeh Shahri Abbas, Shan Chunling, Larsson Stefan, Johansson Fredrik
Johan Lundberg AB, 754 50 Uppsala, Sweden.
Division of Rock Engineering, Tyrens, 118 86 Stockholm, Sweden.
Sensors (Basel). 2024 Feb 14;24(4):1209. doi: 10.3390/s24041209.
In the context of geo-infrastructures and specifically tunneling projects, analyzing the large-scale sensor-based measurement-while-drilling (MWD) data plays a pivotal role in assessing rock engineering conditions. However, handling the big MWD data due to multiform stacking is a time-consuming and challenging task. Extracting valuable insights and improving the accuracy of geoengineering interpretations from MWD data necessitates a combination of domain expertise and data science skills in an iterative process. To address these challenges and efficiently normalize and filter out noisy data, an automated processing approach integrating the stepwise technique, mode, and percentile gate bands for both single and peer group-based holes was developed. Subsequently, the mathematical concept of a novel normalizing index for classifying such big datasets was also presented. The visualized results from different geo-infrastructure datasets in Sweden indicated that outliers and noisy data can more efficiently be eliminated using single hole-based normalizing. Additionally, a relational unified PostgreSQL database was created to store and automatically transfer the processed and raw MWD as well as real time grouting data that offers a cost effective and efficient data extraction tool. The generated database is expected to facilitate in-depth investigations and enable application of the artificial intelligence (AI) techniques to predict rock quality conditions and design appropriate support systems based on MWD data.
在地理基础设施，特别是隧道工程的背景下，分析基于大规模传感器的随钻测量（MWD）数据对于评估岩石工程条件起着关键作用。然而，由于多种形式的堆叠而处理大量MWD数据是一项耗时且具有挑战性的任务。从MWD数据中提取有价值的见解并提高地质工程解释的准确性，需要在迭代过程中将领域专业知识和数据科学技能相结合。为了应对这些挑战并有效地对噪声数据进行归一化和过滤，开发了一种自动处理方法，该方法集成了逐步技术、众数和基于单孔及同组孔的百分位数门限带。随后，还提出了一种用于对这类大数据集进行分类的新型归一化指数的数学概念。瑞典不同地理基础设施数据集的可视化结果表明，使用基于单孔的归一化可以更有效地消除异常值和噪声数据。此外，创建了一个关系统一的PostgreSQL数据库，用于存储和自动传输处理后的和原始的MWD以及实时灌浆数据，该数据库提供了一种经济高效的数据提取工具。预计生成的数据库将有助于深入研究，并能够应用人工智能（AI）技术根据MWD数据预测岩石质量条件并设计合适的支护系统。
