提出并分析了一种用于隧道和地下结构位移演变预测预报的遗传神经网络方法·应用遗传算法优化传统的神经网络结构 ,避免了人为选择网络结构的盲目性 ,较好地解决了神经网络结构选择问题 ,同时提高了网络学习的效率和推广预测能力 ;利用这种非线性智能识别新方法 ,预测下步施工位移变形量 ,并与工程中监测到的历史数据进行对比分析 ,以便工程技术人员据此及时调整和优化施工步序 ,维护地下结构的稳定性·工程实例分析表明 ,该方法随着样本的积累 ,预测精度不断提高 ,并具有实时性的优点 ,具有广泛的应用前景 A genetic neural network method for prediction and prediction of displacement evolution of tunnels and underground structures is proposed and analyzed. By applying genetic algorithm to optimize the traditional neural network structure, the blindness of artificial selection of network structure is avoided and the neural network structure Select the problem, at the same time improve the efficiency of network learning and promote the prediction ability; use this new method of nonlinear intelligent identification to predict the displacement of the next construction deformation, and compared with the historical data monitored in the project, so that engineering and technical personnel According to this timely adjustment and optimization of construction steps to maintain the stability of underground structures · Engineering example analysis shows that the method with the sample accumulation, continuous improvement of accuracy, and has the advantages of real-time, has a wide range of applications