马婧, 刘婷婷, 吕岩. 2019: 基于灰色理论和BP神经网络的季冻区草炭土路基沉降预测模型研究. 工程地质学报, 27(s1): 74-83. DOI: 10.13544/j.cnki.jeg.2019082
    引用本文: 马婧, 刘婷婷, 吕岩. 2019: 基于灰色理论和BP神经网络的季冻区草炭土路基沉降预测模型研究. 工程地质学报, 27(s1): 74-83. DOI: 10.13544/j.cnki.jeg.2019082
    MA Jing, LIU Tingting, LÜ Yan. 2019: SEASONAL FROZEN TURFY SOIL SUBGRADE SETTLEMENT PREDICTION STUDY ON GREY AND BP NEURAL NETWORK THEORY. JOURNAL OF ENGINEERING GEOLOGY, 27(s1): 74-83. DOI: 10.13544/j.cnki.jeg.2019082
    Citation: MA Jing, LIU Tingting, LÜ Yan. 2019: SEASONAL FROZEN TURFY SOIL SUBGRADE SETTLEMENT PREDICTION STUDY ON GREY AND BP NEURAL NETWORK THEORY. JOURNAL OF ENGINEERING GEOLOGY, 27(s1): 74-83. DOI: 10.13544/j.cnki.jeg.2019082

    基于灰色理论和BP神经网络的季冻区草炭土路基沉降预测模型研究

    SEASONAL FROZEN TURFY SOIL SUBGRADE SETTLEMENT PREDICTION STUDY ON GREY AND BP NEURAL NETWORK THEORY

    • 摘要: 随着北方交通工程建设范围的不断扩大,很多高速公路不可避免地穿越草炭土分布区。季冻区草炭土具有高含水率,高有机质,低分解度等特殊工程地质性质,使沉降预测的理论计算误差较大不能满足实际工程需求。本文首先分析了季冻区草炭土路基沉降机理的特殊性,以此提出了优化的灰色沉降预测模型和二维-双隐层BP神经网络沉降预测模型。两种沉降预测模型不仅考虑了草炭土本身复杂的工程地质性质、北方地区季冻情况对土体自然沉降的影响,还引入了填筑情况等工程因素对路基沉降量的深度学习。以长白山吉林到延吉高速公路草炭土路基沉降实际监测数据为例,将两种模型拟合及预测结果进行对比分析,结果表明两种模型的拟合预测精度均较高,并且各有优势,由此本文对该类工程中两种预测模型各自的特点进行了总结,为北方地区草炭土路基沉降多因素预测模型的研究提供一定参考价值。

       

      Abstract: With the development of highway and railway construction in northern China, expressways inevitably pass through the turfy soil area. Turfy soil in seasonal frozen area has high water content, organic matter and low decomposition degree etc, so that theoretical calculation cannot meet the precision requirements of practical engineering. Therefore, this paper firstly analyzes the particular settlement mechanism of turfy soil subgrade in seasonal frozen area, and then puts forward to the optimized grey settlement prediction model and the two-dimension double-layer BP neural network settlement prediction model. The two simulation models not only take the disturbance due to complicated engineering geological properties of turfy soil and seasonal freezing in northern China into account, but also introduce filling condition as one of engineering factors to deep learn subgrade settlement changes law. This paper takes the actual settlement monitoring data of seasonal turfy soil subgrade from jilin to yanji close to Changbai Mountain for instance, both the fitted and the predicted results of two models are comparatively analyzed which shows both of two models have high accuracy, but each has its own advantages and disadvantages. thus, this paper further summarizes the similarities and differences of two models in this kind of engineering, which in the meantime provides some reference for the application of the multi-factor prediction model to such turfy soil subgrade settlement prediction in northern China.

       

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