Dynamic prediction of building subsidence deformation with data-based mechanistic self-memory model

Abstract

This paper describes a building subsidence deformation prediction model with the self-memorization principle. According to the non-linear specificity and monotonic growth characteristics of the time series of building subsidence deformation, a data-based mechanistic self-memory model considering randomness and dynamic features of building subsidence deformation is established based on the dynamic data retrieved method and the self-memorization equation. This model first deduces the differential equation of the building subsidence deformation system using the dynamic retrieved method, which treats the monitored time series data as particular solutions of the nonlinear dynamic system. Then, the differential equation is evolved into a difference-integral equation by the self-memory function to establish the self-memory model of dynamic system for predicting nonlinear building subsidence deformation. As the memory coefficients of the proposed model are calculated with historical data, which contain useful information for the prediction and overcome the shortcomings of the average prediction, the model can predict extreme values of a system and provide higher fitting precision and prediction accuracy than deterministic or random statistical prediction methods. The model was applied to subsidence deformation prediction of a building in Xi'an. It was shown that the model is valid and feasible in predicting building subsidence deformation with good accuracy. The fast development of economics promotes the urbanization of China. More and more high-rises appear in our cities [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. In order to ensure the service life and security of constructional works, it is necessary to monitor the constructional work systematically during its building and operating. Due to the diversity, complexity and non-deterministic characters of inducing factors of the building subsidence deformation, the mechanical behavior and deformation trend of building subsidence also reflect non-linear characteristics with the coexistence of the uncertainty and randomness [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34]. Existing methods for building subsidence deformation prediction usually establish the time series analysis model by utilizing monitored data [35][36][37][38][39][40]. However, most of these models are of the "parameter model" class which may not have clear physical meaning of parameters and discard effective parts of monitoring data. On the basis of the retrieved modeling, self-memorization principle has been recently developed based on the mechanism of time series data. As a mathematic realization on integration of deterministic and random theory, the self-memory model is a statistical-dynamical method to solve problems in nonlinear dynamic systems [41][42][43]. The core method of the model is to transform differential equations into difference-integral equations by introduction of the self-memory function. For dynamic systems described by differential equations, pre...

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