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Analysis of correlation and sensitivity influences on the variation of mechanical parameters of proximate structures in the delta region

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27 févr. 2025
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Figure 1.

Research content and method flow
Research content and method flow

Figure 2.

Multiple Structures and Mechanical parameters Influencing Change Correlation Model.
Multiple Structures and Mechanical parameters Influencing Change Correlation Model.

Figure 3.

Research process for intelligent analysis of multi-structure and multi mechanical parameters impact changes based on feed-forward neural network.
Research process for intelligent analysis of multi-structure and multi mechanical parameters impact changes based on feed-forward neural network.

Figure 4.

A typical geotechnical structure system in South China. (The stratigraphic lithology is mainly artificial fill, alluvial clayey and sandy soils, silty soft soils, slope clayey soils, residual clayey soils with weathered bedrock.)
A typical geotechnical structure system in South China. (The stratigraphic lithology is mainly artificial fill, alluvial clayey and sandy soils, silty soft soils, slope clayey soils, residual clayey soils with weathered bedrock.)

Figure 5.

Layout of monitoring points. (Satellite map obtained from Baidu Maps at https://map.baidu.com/)
Layout of monitoring points. (Satellite map obtained from Baidu Maps at https://map.baidu.com/)

Figure 6.

Time-series for each monitoring indicator: (a) Time-series of rainfall date; (b) time-series of foundation pit monitoring mechanical parameters data; (c) time-series of slope monitoring mechanical parameters data; and (d) time-series of embankment monitoring mechanical parameters data. (The project started in May 2022)
Time-series for each monitoring indicator: (a) Time-series of rainfall date; (b) time-series of foundation pit monitoring mechanical parameters data; (c) time-series of slope monitoring mechanical parameters data; and (d) time-series of embankment monitoring mechanical parameters data. (The project started in May 2022)

Figure 7.

Observed versus predicted output curves: (a) Predicted output of each indicator for S; and (b) predicted output of each indicator for J.
Observed versus predicted output curves: (a) Predicted output of each indicator for S; and (b) predicted output of each indicator for J.

Figure 8.

Gray Correlation Degree for each influencing mechanical parameters: (a) Correlation with S; and (b) Correlation with J.
Gray Correlation Degree for each influencing mechanical parameters: (a) Correlation with S; and (b) Correlation with J.

Figure 9.

Ranking of the sensitivity contribution of each indicator: (a) Ranking of the contribution of each indicator to the impact of S; and (b) Ranking of the contribution of each indicator to the impact of J.
Ranking of the sensitivity contribution of each indicator: (a) Ranking of the contribution of each indicator to the impact of S; and (b) Ranking of the contribution of each indicator to the impact of J.

Figure 10.

Proportion of factors influencing: (a) S; and (b) J.
Proportion of factors influencing: (a) S; and (b) J.

Advantages, disadvantages, and applications of different methods [34]

Serial Number Method Advantage Disadvantage Application
1 Linear regression prediction Good at acquiring linear relationships in the dataset; easy to operate; fast training and prediction speed. The measurement data are discrete, and the prediction accuracy is affected by complex geological conditions. It is suitable for low latitudes and there is no covariance between each dimension.
2 Grayscale Model Simple and practical; few model parameters. Little fault tolerance; not suitable for long-term forecasting. It is suitable for short-term prediction.
3 Support vector machine Simple algorithm; good robustness (in the case of small samples). Limited by the sample size; when the sample size is too large, the accuracy will be affected. It is mainly used for data classification but can also be used for regression prediction.
4 Time series It allows for full consideration of the impact of seasonal and cyclical variations on specific points in time. Single linearity, stable monitoring time, equidistant data feature. It is applied to predicts related to its own previous period.
5 Neural network technology Better nonlinear mapping capability; better self-learning and self-adaptive capability; certain faulttolerance capability. With low learning efficiency, slow convergence speed, and easy to fall into a local minimum state. Theoretically, it can be mapped to any function.

Main monitoring items_

Serial Number Object Monitoring items Unit Obtain access
1 Climate RF mm Weather forecast
2 Foundation pit M kN SSC-101 Frequency reading instrument
SW mm SVW-1 Electric water level gauge
WY mm TS30 Total station
CJ mm DNA03 Electronic level
3 Slope JM kN SSC-101 Frequency reading instrument
PSW mm SVW-1 Electric water level gauge
PWY mm TS30 Total station
PWX mm DNA03 Electronic level
4 Embankment S mm DNA03 Electronic level
J mm TS30 Total station