基于深度学习与欧氏距离的深基坑安全评估研究

    SAFETY EVALUATION OF A DEEP FOUNDATION PIT BASED ON DEEP LEARNING AND EUCLIDEAN DISTANCE METHODS

    • 摘要: 机器学习的快速发展为深基坑安全的预测与预警提供了重要的分析手段,本研究提出了深基坑安全评估等级的概念以反映施工中基坑围护系统的安全程度。研究中采用BP(Back Propagation)神经网络、GA-BP(Genetic Algorithm-Back Propagation)神经网络、NARX(Nonlinear Autoregressive Exogenous Model)动态神经网络、Elman神经网络4种算法,对深圳某深基坑近1 a的支撑系统轴力、沉降、支护结构位移数据进行训练并建立预测模型,研究发现机器学习预测的平均误差低于5.5%,其中GA-BP神经网络算法的误差最小。结合神经网络算法预测数据与监测数据的差异性进行分析,发现该差异性满足正态分布,可作为连续随机变量进行分析。基于正态分布和欧氏距离法研究两者的显著性差异水平,构建了深基坑安全评估等级的系统性评估算法,结合深基坑现场的安全情况进行了综合验证。本研究中提出的算法具有方便工程师使用、实时性预测、多种神经网络算法优化对比实现精准预警的特征,为深基坑的安全评估提供了重要手段。

       

      Abstract: Rapid development of machine learning algorithms provides important analysis methods for safety prediction and early warning of deep foundation pits. In this study, the concept of safety levels of deep foundations is proposed to indicate the safety condition of foundation systems. Different machine learning methods, including BP (Back Propagation) neural network, GA-BP (Genetic Algorithm-Back Propagation) neural network, NARX (Nonlinear Autoregressive Exogenous Model) dynamic neural network, and Elman neural network method, were used to train and establish the mathematical prediction models of the monitored axial force, settlement, and support structure displacement of deep foundation pits within the past year. A comparative study shows that the average maximum error was less than 5.5%, and the obtained error of the GA-BP based neural network was the smallest among all prediction methods. The data difference between measured data and the related predictions was further analyzed and found to satisfy a normal distribution. This data difference can be considered a continuous random variable in a continuous normal distribution. The level of significance of the data difference between the measured and predicted data of three key parameters—including axial force, settlement, and support structure displacement—was analyzed in terms of both normal distribution and Euclidean distance methods. A new method was proposed to evaluate the safety level of deep foundation pits and was further verified using field data of a real foundation pit. This method is characterized by the advantages of ease of use for engineers, real-time prediction, optimized prediction methods, high accuracy prediction, and hence offers a critical method for the evaluation of safety levels of foundation pits.

       

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