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GA-NN Monitoring Model and its Application on Surface Settlement

机译:GA-NN监测模型及其在地表沉降中的应用

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Combining the advantages of basic genetic algorithm and neural network, analyze and set up GA & NN genetic neural network, explore and study the algorithm. The efficiency and effectiveness of this hybrid training has been significantly improved comparing with the single genetic evolution or BP training method, its versatility is better. The model is applied to predict the deformation of shield tunnel excavation. According to the effects of measured influence factors under construction, it can make the appropriate forecast to the surface settlement which is better than the conventional regression model. It shows that neural networks in the ground during tunneling shield analysis and prediction of settlement is practical and adaptable.
机译:结合基本遗传算法和神经网络的优势,分析并建立了GA和NN遗传神经网络,对算法进行了研究。与单基因进化或BP训练方法相比,该混合训练的效率和有效性得到了显着提高,其通用性更好。该模型被用于预测盾构隧道开挖的变形。根据施工中实测影响因素的影响,可以对地表沉降做出合适的预测,优于传统的回归模型。结果表明,在隧道盾构分析和沉降预测过程中,地下的神经网络是实用且适应性强的。

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