基于ARIMA-BP神经网络模型的桥梁SHM应变预测分析
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Strain Prediction and Analysis of Bridge SHM Based on ARIMA-BP Neural Network Model
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    桥梁结构健康监测的应变监测数据具有较强的趋势性与随机性,为提升数据的预测精度,提出将传统单一的自回归积分滑动平均模型(ARIMA)和BP神经网络预测模型进行加权与组合,并将这两种方法分别运用于江西省某跨江大桥桥梁结构健康监测系统记录的应变监测数据的预测进行验证。结果表明:仅运用单一模型预测时,BP神经网络的预测效果要优于ARIMA模型;加权与组合模型的预测精度均优于单一模型,其中加权模型及组合模型的残差平方和(SSE)与BP神经网络模型相差最大,分别高达50.23%与49.87%;对比加权模型与组合模型的各项误差指标,发现二者预测模型的预测精度极为接近;单一预测模型的误差包络范围大于其他两类模型,其中ARIMA模型的误差总和约为50 με,BP神经网络模型的误差总和约为30με,加权模型的误差总和约为21.09 με,组合模型的误差总和约为20.97 με。经分析,加权预测模型与组合预测模型均能实现对桥梁SHM应变预测。

    Abstract:

    The strain monitoring data of bridge structure health monitoring has strong tendency and randomness, in order to improve the prediction accuracy of the data, the traditional single autoregressive integral moving average model (ARIMA) and BP neural network prediction model are weighted and combined, and the two methods are applied to the prediction of the strain monitoring data recorded by the bridge structural health monitoring system of a river crossing bridge in Jiangxi Province. The results show that when the first mock exam is used alone, the BP neural network has better prediction effect than the ARIMA model.The prediction accuracy of weighted and combined models are better than that of single model, and the difference between the sum of squares error (SSE) of weighted model and combined model with BP neural network model is the largest, up to 50.23% and 49.87% respectively.By comparing the error indexes of the weighted model and the combined model, it is found that the prediction accuracy of the two prediction models is very close.The error envelope range of single prediction model is larger than that of the other two models, and the total error sum of ARIMA model is about 50με, the total error sum of BP neural network model is about 30με, the total error sum of the weighted model is about 21.09με, the total error sum of the combined model is about 20.97με. After analysis, both the weighted prediction model and the combined prediction model can predict the SHM strain of the bridge.

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邱卓,胡琼清,伍伟斌,钟菊芳,万灵.基于ARIMA-BP神经网络模型的桥梁SHM应变预测分析[J].科技与产业,2022,22(08):392-397

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  • 在线发布日期: 2022-08-23