摘要
在含富水带山岭地区的隧道工程建设中,往往引起隧道内涌水,不但影响施工安全,还会影响到隧道建成后运营质量,必须采取超前预报和措施,保证工程质量。此文以某在建隧道6个里程段的地质构造和涌水情况为样本,通过建立BP神经网络模型,对隧道内的涌水量进行预测,并与实测涌水量进行比较。所得结果表明,最大相对误差在10%以内,平均为3.8%,符合工程要求。可为隧道施工提供采取有效措施的依据。
The water inflow often happen during the construction of tunnel in the mountain area with rich water belt.And the water inflow not only affects the construction safety,but also affects the quality of tunnel operation. Therefore,the geological prediction and measures are must be taken to guarantee engineering quality. In this paper,the geological structure and water gushing of 6 mileage sections of a tunnel under construction are taken as samples. Through the establishment of BP neural network model,the water inflow in tunnel is predicted and compared with the actual water inflow. The results show that the maximum relative error is less than 10%,with an average of 3. 8%,which meets engineering requirements. And the results can provide the basis for effective measures for tunnel construction.
出处
《铁路工程技术与经济》
2017年第5期5-8,共4页
Railway Engineering Technology and Economy
关键词
隧道工程
BP神经网络
涌水量
预测
Tunnel engineering
BP neural network
Water inflow
Predict
