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该文结合基坑沉降监测实例,对沉降数据进行分析,发现沉降速率越来越快,为更合理的对这种增幅较大的累计沉降值做出合理的预报,本文采用GM(1,1)、DGM(1,1)、新陈代谢GM(1,1)和新陈代谢DGM(1,1)四种模型对基坑沉降进行预测。结果表明新陈代谢DGM(1,1)预测模型提高了预测稳定性,新陈代谢DGM(1,1)预测模型与另外三种模型相比,精度有较大幅度提高,残差值增加缓慢,近似于一条水平线,预测值与实测值非常接近,且适用于长期预测,这是另外三种模型所不具有的。对于增幅较大的累计沉降量,新陈代谢DGM(1,1)模型具有较高的预测精度,对其他类似基坑监测具有一定的借鉴意义。
Based on the example of foundation pit settlement monitoring, the settlement data are analyzed and the sedimentation rate is found to be faster and faster. In order to make a reasonable prediction of the cumulative settlement with larger increase, this paper uses GM (1,1 ), DGM (1,1), metabolism GM (1,1) and metabolism DGM (1,1) were used to predict the settlement of foundation pit. The results showed that the metabolic DGM (1,1) predictive model improved the predictive stability. Compared with the other three models, the metabolic DGM (1,1) predictive model improved greatly, the residual value increased slowly and approximated to one Horizontal line, the predicted value and the measured value is very close, and for long-term prediction, which is the other three models do not have. The metabolic DGM (1,1) model has a high prediction accuracy for large incremental cumulative settlement, which is of great value to other similar pit monitoring.