Research and application of deformation safety prediction for substation foundation pits based on ga-prophet model

Abstract

[Objective] Monitoring the deformation of foundation pits is crucial to ensuring the safe construction of foundation pits. To enhance the application value of monitoring data and ensure the safety of foundation pit construction, this study relies on a 330kV substation foundation pit project in Xi'an, Shaanxi Province, and is based on actual deformation monitoring result. [Methods] Using the mean square error(MSE) as the fitness function of the genetic algorithm(GA), the trend, seasonality, and holiday(sporadic event) parameters of the Prophet model were optimized, with particular attention to the trend parameters consistent with the deformation pattern of the foundation pit. The GA-Prophet foundation pit deformation prediction model was constructed, and its feasibility and effectiveness were verified using evaluation metrics such as MAE, RSS, RMSE, and Theil Inequality Coefficient values. Additionally, this model was employed for the early prediction of horizontal and vertical deformation of the foundation pit to evaluate the safety status of the foundation pit structure. [Results] The result indicate that the prediction curve of the GA-Prophet model closely aligns with the measured data curve, attributed to the adoption of a saturation model that conforms to the actual engineering displacement change pattern in the prediction model. Taking the horizontal displacement prediction result of the JC8 measurement point as an example, the MAE, RSS, RMSE, and Theil Inequality Coefficient values of the prediction result were 0.480, 1.310, 0.512, and 0.052, respectively, all superior to the prediction result of the Prophet, LSTM, ARIMA, and BP models. Moreover, the early prediction result of foundation pit deformation by this model show that the maximum predicted values of horizontal and vertical deformation at each measurement point did not exceed the deformation alarm values specified by the standards, indicating that the foundation pit structure is in a safe state. [Conclusion] The model demonstrates good applicability for predicting foundation pit deformation and improves the accuracy of the prediction result. It can be used for the safety prediction of foundation pit deformation.

Keywords

substation foundation pit / deformation monitoring / genetic algorithm / GA-Prophet model / advanced prediction / influencing factors