【摘要】 地下建筑工程中,对地下结构的变形监测无法有效预防灾害发生,因此需要开展变形预测的研究。传统预测方法多属于静态预测,无法实现随沉降发展的动态调整,且评价指标单一。结合灰色模型GM(2,1)与长短时记忆神经网络(LSTM)两种动态预测模型,基于快速非支配遗传算法构建了同时考虑两种评价指标的组合模型,依据某工程长期监测数据对隧道沉降进行预测。结果显示:长短时记忆神经网络模型与灰色模型在精度上优于其他模型,能够准确预估沉降发展,其中长短时记忆神经网络优势更明显;使用快速非支配多目标优化遗传算法进行组合模型的权值分配可以较快得到两种评价指标下的最优定权方案,结合每种模型的优势,得到较精确的预测效果。更多还原

【Abstract】 The deformation monitoring of underground structures is no longer sufficient to prevent disasters. Therefore,the prediction of underground structure deformation is necessary. In the study,two dynamic prediction models of GM( 2,1) and LSTM are combined to predict the tunnel settlement under artificial mountain,which used the NSGA-Ⅱalgorithm considering two evaluation indicators to achieve better prediction results,based on the long-term monitoring data of a tunnel project. The results show that LSTM can ensure higher prediction accuracy and can better simulate the development trend of settlement,meanwhile the gray model is difficult to simulate more local settlement changes. The combined models can quickly obtain the optimal weighting scheme under multiple evaluation indexes with the NSGA-Ⅱalgorithm. It can combine the advantages of each model,and get a more accurate prediction effect.更多还原