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深度学习在实测沉降数据预处理中的应用研究
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Deep Learning for Preprocessing of Measured Settlement Data
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    基于深度学习中的长短期记忆网络LSTM,通过搭建Seq2Seq模型,提出了可对实测沉降数据进行预处理的新方法. Seq2Seq可通过观测大量有效的测点数据来自动学习沉降发展规律,并在训练完成后能对异常测点沉降进行重新计算,可有效避免异常数据对后续沉降预测的干扰. 以某机场多个区域的实测沉降数据为背景,通过将Seq2Seq模型重计算出的沉降值与实测值对比,验证了该模型的可靠性. 结合超参数与数据集等参数分析,研究了提升模型学习能力的影响因素. 研究结果表明:在训练集选取40个测点、测试集选取15个的条件下,模型重计算值与实测值全过程平均误差3 cm. 增大训练集与数据特征,且减小训练集与测试集之间的偏差时,模型的精度提升明显,误差缩小到2 cm.

    Abstract:

    Based on Long Short Term Memory (LSTM) in deep learning,a new method for preprocessing the measured settlement data is proposed by building a Seq2Seq model. Seq2Seq can automatically learn the law of settlement development by observing a large number of effective measuring point data,and can recalculate the settlement of abnormal measuring points after training is completed,which can effectively avoid the interference of abnormal data on the subsequent settlement prediction. Taking the actual measured settlement data in multiple areas of an airport as the background,the reliability of the model was verified by comparing the calculated settlement value of the Seq2Seq model with the measured values. Combined with parametric analysis such as hyperparameters and data sets,the influencing factors on improving model learning ability are studied. The research results show that,under the condition that the training set selects 40 measurement points and the test set selects 15,the average error of the model recalculated value and the measured value in the whole process is 3 cm. When the training set and data features are increased and the deviation between the training set and the test set is reduced,the accuracy of the model is significantly improved and the error is reduced to 2 cm.

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胡安峰,李唐?覮,陈缘,葛红斌,李怡君.深度学习在实测沉降数据预处理中的应用研究[J].湖南大学学报:自然科学版,2021,(9):43~51

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  • 在线发布日期: 2021-09-28
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