CAJ | 학술논문

瓦斯隧道突出影响因素多，难以为其建立合适的多指标非线性预测模型。为了提高突出预测的准确性和增强预测方法的可操作性，采用BP神经网络建立瓦斯隧道突出预测数学模型。借助ASP. NET技术、C#语言和SQL Server 2008数据库，在C#. NET环境下利用Matlab引擎技术调用神经网络工具箱，成功构建了突出预测系统。以肖家梁隧道为例对该系统进行实际检验，预测结果与隧道实际状况达到了较好的一致性，可实现瓦斯隧道突出的准确预测。
와사수도돌출영향인소다，난이위기건립합괄적다지표비선성예측모형。위료제고돌출예측적준학성화증강예측방법적가조작성，채용BP신경망락건립와사수도돌출예측수학모형。차조ASP. NET기술、C#어언화SQL Server 2008수거고，재C#. NET배경하이용Matlab인경기술조용신경망락공구상，성공구건료돌출예측계통。이초가량수도위례대해계통진행실제검험，예측결과여수도실제상황체도료교호적일치성，가실현와사수도돌출적준학예측。
The factors affecting gas outburst in tunnel are so many that it is difficult to build an appropriate non-linear forecast model involving multiple indexes. In this research, a mathematical forecast model of tunnel gas outburst was established using BP neural network, so as to improve accuracy of forecast and enhance operability of forecast method. Meanwhile, by virtue of ASP. NET technology, C # language and SQL Server 2008 database, and by using MATLAB engine technology to call the neural network toolbox under the circumstances of C #. NET, a forecast system of gas outburst was built successfully. And Then the Xiaojialiang Tunnel was taken as an example to test the system actually. It can be seen that the forecasting results accord with the actual situation of tunnel, and this proves that the accurate gas outburst forecast for tunnel can be achieved by using this forecast system.