ISSN 1003-8035 CN 11-2852/P

    黄土高填方场地工后沉降预测模型性能评估方法

    Evaluation methods for performance of post-construction settlement prediction models in thick loess filled ground

    • 摘要: 工后沉降预测结果是黄土高填方场地变形稳定性评价和建筑物规划布局的重要参考依据。为遴选适合黄土高填方场地的工后沉降预测模型,基于某典型黄土高填方工程的实测沉降数据,分析了工后沉降曲线的变化规律和发展趋势,建立了17种回归参数模型,提出了模型预测效果的评价指标和方法。结果表明:(1)该工程填方区工后沉降历时曲线呈“缓变型”变化,土方填筑完工初期无陡增段,随时间增加沉降速率逐步降低,尚未出现沉降趋于稳定的水平段;(2)将外推预测误差、内拟合误差和后验误差比最小化作为综合控制目标,可遴选出理想的回归参数模型;(3)MMF模型(Ⅱ型)和双曲线模型具有较高的预测精度、较好的稳定性和较强的适应性,在17种模型中的预测效果最佳;(4)沉降数据的变化越平稳,模型预测效果越好;(5)增大建模数据的时间跨度,会提升预测精度,但增大至一定值后,预测精度提升效果不再显著。

       

      Abstract: The prediction of post-construction settlement is an important reference for the evaluation of deformation stability evaluation and building layout planning in thick loess filled ground. To choose suitable models for predicting post-construction settlement in thick loess filled grounds, the characteristics of post-construction settlement curves are analyzed based on the measured settlement of a thick loess fill ground project. Seventeen regression parameter models are established, and some evaluation indexes and methods for models are proposed. The best prediction models for post-construction settlement prediction are optimized. The results indicate that the post-construction settlement curves of the filling area change slowly, with no steep increase in the initial stage of earthwork filling. The settlement rate gradually decreases with time, and there is no horizontal section where the settlement tends to be stable. The optimal regression parameter model can be selected by minimizing the extrapolation prediction error, the internal fitting error, and the posteriori error ratio as the comprehensive control objective. The MMF model (TypeⅡ) and hyperbolic model show high prediction accuracy, good stability, and strong adaptability, with the prediction effect being the best among the 17 models. The more stable the settlement data changes, the better the model prediction effect. Increasing the time span of modeling data would improve the prediction accuracy, but the improvement effect on prediction accuracy would no longer be significant after reaching a certain value.

       

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