Li Wenxin, Chen Juntao, Zhu Jun, Ji Xinbo, Fu Ziqun
Shandong Univ Sci & Technol, Coll Energy & Min Engn, Qingdao, People's Republic of China.
Shandong Energy Xinwen Mining Group Suncun Coal Mine, Taian, 271219, China.
Sci Rep. 2024 Apr 2;14(1):7782. doi: 10.1038/s41598-024-57837-x.
The evaluation of grouting effects constitutes a critical aspect of grouting engineering. With the maturity of the grouting project, the workload and empirical characteristics of grouting effect evaluation are gradually revealed. In the context of the Qiuji coal mine's directional drilling and grouting to limestone aquifer reformation, this study thoroughly analyzes the influencing factors of grouting effects from geological and engineering perspectives, comparing these with various engineering indices associated with drilling and grouting. This led to the establishment of a "dual-process, multi-parameter, and multi-factor" system, employing correlation analysis to validate the selected indices' reasonableness and scientific merit. Utilizing the chosen indices, eight high-performing machine learning models and three parameter optimization algorithms were employed to develop a model for assessing the effectiveness of directional grouting in limestone aquifers. The model's efficacy was evaluated based on accuracy, recall, precision, and F-score metrics, followed by practical engineering validation. Results indicate that the "dual-process, multi-parameter, multi-factor" system elucidates the relationship between influencing factors and engineering parameters, demonstrating the intricacy of evaluating grouting effects. Analysis revealed that the correlation among the eight selected indicators-including the proportion of boreholes in the target rock strata, drilling length, leakage, water level, pressure of grouting, mass of slurry injected, permeability properties of limestone aquifers before being grouted, permeability properties of limestone aquifers after being grouted-is not substantial, underscoring their viability as independent indicators for grouting effect evaluation. Comparative analysis showed that the Adaboost machine learning model, optimized via a genetic algorithm, demonstrated superior performance and more accurate evaluation results. Engineering validation confirmed that this model provides a more precise and realistic assessment of grouting effects compared to traditional methods.
注浆效果评价是注浆工程的关键环节。随着注浆工程的成熟，注浆效果评价的工作量和经验特征逐渐显现。在邱集煤矿定向钻孔注浆改造灰岩含水层的背景下，本研究从地质和工程角度深入分析了注浆效果的影响因素，并将其与钻孔和注浆的各种工程指标进行对比。在此基础上建立了“双过程、多参数、多因素”体系，通过相关性分析验证所选指标的合理性和科学性。利用所选指标，采用8种高性能机器学习模型和3种参数优化算法，建立了灰岩含水层定向注浆效果评价模型。基于准确率、召回率、精确率和F1值指标对模型效果进行评估，随后进行实际工程验证。结果表明，“双过程、多参数、多因素”体系阐明了影响因素与工程参数之间的关系，揭示了注浆效果评价的复杂性。分析发现，所选的8个指标，包括目标岩层钻孔比例、钻孔长度、漏水量、水位、注浆压力、注浆量、注浆前灰岩含水层渗透特性、注浆后灰岩含水层渗透特性之间的相关性不强，这突出了它们作为注浆效果评价独立指标的可行性。对比分析表明，通过遗传算法优化的Adaboost机器学习模型表现优异，评价结果更准确。工程验证表明，与传统方法相比，该模型能更精确、更实际地评估注浆效果。
