本研究通过大数据分析和机器学习技术优化高海拔隧道施工中的通风策略,开发了一种高精度预测模型。采用随机森林和长短期记忆网络(LSTM)结合的方法,利用初期掘进阶段收集的环境数据预测远距离掘进时的通风需求和环境变化。在隧道掘进至100 m时,安装高精度传感器实时收集温度、湿度、风速、粉尘及CO浓度等数据,通过无线网络传输至中心数据库进行预处理。模型通过网格搜索和随机搜索优化超参数,并采用10折交叉验证评估性能。结果表明:随机森林和LSTM模型的预测准确率分别达89%和93%,在实际施工中展现出出色的预测能力和实用性;隧道掘进至1 km和2 km时,模型预测值与实测数据高度一致,误差率低于7%;模型能准确预测大规模爆破作业后的污染物浓度变化,确保施工现场空气质量和工人安全;相比传统线性回归和支持向量机模型,随机森林和LSTM融合模型在处理复杂非线性关系和时间序列数据方面表现优异,显著提高了通风系统设计和运行效率,具有广泛的应用前景。
The study optimized the ventilation strategy in high-altitude tunnel construction through big data analysis and machine learning techniques, and developed a high-precision prediction model. Using a combination of random forest and long short-term memory network (LSTM), the environmental data collected during the initial excavation stage is used to predict the ventilation demand and environmental changes during long-distance excavation. When the tunnel is excavated to 100 m, high-precision sensors are installed to collect real-time data such as temperature, humidity, wind speed, dust, and CO concentration, which are transmitted to the central database for preprocessing through wireless networks. The model optimizes hyperparameters through grid search and random search, and evaluates performance using 10-fold cross-validation. The results show that: The RF and LSTM models achieved prediction accuracies of 89% and 93%, respectively. It has demonstrated outstanding predictive capabilities and practicality in actual construction. When the tunnel was excavated to a depth of 1 km to 2 km, the predicted values of the model were highly consistent with the measured data, with an error rate of less than 7%.It effectively responded to large-scale blasting operations, ensuring air quality and worker safety. Compared to traditional methods, the RF and LSTM fusion model better handles complex nonlinear relationships and time-series data, improving ventilation system design and operational efficiency. This integrated approach offers a significant advancement in managing tunnel construction in challenging high-altitude environments, providing reliable predictions to maintain safe and efficient working conditions.
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