Purpose

An accurate prediction of the deformation of retaining structures is critical for ensuring the stability and safety of braced deep excavations, while the high nonlinear and complex nature of the deformation makes the prediction challenging. This paper proposes an explainable boosted combining global and local feature multivariate regression (EB-GLFMR) model with high accuracy, robustness and interpretability to predict the deformation of retaining structures during braced deep excavations.

Design/methodology/approach

During the model development, the time series of deformation data is decomposed using a locally weighted scatterplot smoothing technique into trend and residual terms. The trend terms are analyzed through multiple adaptive spline regressions. The residual terms are reconstructed in phase space to extract both global and local features, which are then fed into a gradient-boosting model for prediction.

Findings

The proposed model outperforms other established approaches in terms of accuracy and robustness, as demonstrated through analyzing two cases of braced deep excavations.

Research limitations/implications

The model is designed for the prediction of the deformation of deep excavations with stepped, chaotic and fluctuating features. Further research needs to be conducted to expand the model applicability to other time series deformation data.

Practical implications

The model provides an efficient, robust and transparent approach to predict deformation during braced deep excavations. It serves as an effective decision support tool for engineers to ensure the stability and safety of deep excavations.

Originality/value

The model captures the global and local features of time series deformation of retaining structures and provides explicit expressions and feature importance for deformation trends and residuals, making it an efficient and transparent approach for deformation prediction.

中文翻译：

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用于支撑深基坑开挖变形预测的可解释增强组合全局和局部特征多元回归模型

目的

准确预测支护结构的变形对于确保深基坑支护的稳定性和安全性至关重要，而变形的高度非线性和复杂性使得预测具有挑战性。本文提出了一种可解释的增强组合全局和局部特征多元回归（EB-GLFMR）模型，具有高精度、鲁棒性和可解释性，用于预测支撑深基坑开挖期间支护结构的变形。

设计/方法论/途径

在模型开发过程中，使用局部加权散点图平滑技术将变形数据的时间序列分解为趋势项和残差项。通过多个自适应样条回归分析趋势项。在相空间中重建残差项以提取全局和局部特征，然后将其输入梯度增强模型进行预测。

发现

通过分析两个支撑深基坑案例证明，所提出的模型在准确性和鲁棒性方面优于其他已建立的方法。

研究局限性/影响

该模型是为预测具有阶梯性、混沌性和波动性的深基坑变形而设计的。需要进行进一步的研究以扩展模型对其他时间序列变形数据的适用性。

实际影响

该模型提供了一种高效、稳健且透明的方法来预测支撑深基坑开挖期间的变形。它为工程师提供有效的决策支持工具，确保深基坑的稳定性和安全性。

原创性/价值

该模型捕获了支护结构时间序列变形的全局和局部特征，并为变形趋势和残差提供了明确的表达式和特征重要性，使其成为一种高效、透明的变形预测方法。