原文链接：
https://link.springer.com/article/10.1007/s40789-023-00630-4或点击文末“阅读原文”
煤矿井下最危险的安全隐患之一是在采煤工作面回采过程中发生的顶板冒落。顶板冒落不仅可能对矿工造成致命或非致命伤害，还可能阻碍采矿和运输作业。因此，开发一个可靠的顶板冒落预测模型对于有效应对此类问题至关重要。顶板冒落受多种不明确且难以量化的参数影响，这使得相关研究成为一个充满不确定性和挑战性的课题。美国国家职业安全与健康研究所从37个煤矿中收集了有关顶板性能的国家数据库，以探索导致顶板冒落的因素。由于受到多种限制, 37个煤矿获得的数据有限，这增加了数据不完整的可能性。模糊逻辑是一种应对模糊性、不完整性和不确定性的技术。因此，本文介绍了模糊推理方法，该方法采用遗传算法，基于109条顶板冒落数据记录，创建模糊规则，并通过模式搜索来优化参数的隶属函数。采用均方根误差、均值-绝对误差和判定系数等统计指标对所部署模型的性能进行了评估。根据这些标准，建议的模型在使用较少的模糊规则精确预测顶板冒落率方面优于现有模型。
Abstract One of the most dangerous safety hazard in underground coal mines is roof falls during retreat mining. Roof falls may cause life-threatening and non-fatal injuries to miners and impede mining and transportation operations. As a result, a reliable roof fall prediction model is essential to tackle such challenges. Different parameters that substantially impact roof falls are ill-defined and intangible, making this an uncertain and challenging research issue. The National Institute for Occupational Safety and Health assembled a national database of roof performance from 37 coal mines to explore the factors contributing to roof falls. Data acquired for 37 mines is limited due to several restrictions, which increased the likelihood of incompleteness. Fuzzy logic is a technique for coping with ambiguity, incompleteness, and uncertainty. Therefore, In this paper, the fuzzy inference method is presented, which employs a genetic algorithm to create fuzzy rules based on 109 records of roof fall data and pattern search to refine the membership functions of parameters. The performance of the deployed model is evaluated using statistical measures such as the Root-Mean-Square Error , Mean-Absolute-Error, and coefficient of determination (R_2). Based on these criteria, the suggested model outperforms the existing models to precisely predict roof fall rates using fewer fuzzy rules.
Fig. 1 Fuzzy inference system
Fig. 4 Rules for prediction
Fig. 6 Final MFs value of other parameters
引用格式：
Sahu, A., Sinha, S. & Banka, H. Fuzzy inference system using genetic algorithm and pattern search for predicting roof fall rate in underground coal mines. Int J Coal Sci Technol 11, 1 (2024). https://doi.org/10.1007/s40789-023-00630-4
