A Discrete-Time Model-Based Method for Predicting Settlement of Geotechnical Foundations in Buildings

Author:

Yang Chengwei12ORCID,Jiang Zhenzhen1

Affiliation:

1. College of Architecture and Civil Engineering, Anhui University of Architecture, Hefei 230000, China

2. Environmental Science and Engineering, Anhui Normal University, Wuhu 241000, China

Abstract

The selection of a single prediction method is difficult to adapt to the actual engineering situation in the construction geotechnical foundation’s settlement prediction. This article proposes a modified optimized comprehensive prediction model to independently mine the construction geotechnical foundation settlement monitoring data from different angles. The proposed model analyzes the construction geotechnical foundation settlement’s change law and realizes the comprehensive prediction of construction geotechnical foundation settlement. Initially, the preliminary integrated discrete-time prediction model is established based on the combination of the hyperbolic method and the GM (1, 1) model’s modeling mechanism. The GM (1, 1) modeling mechanism is based on the idea of arithmetic weighted average combination. Then the real-time correction weight coefficients are constructed and the real-time correction amount is calculated to modify the initial integrated prediction model. The modified optimized integrated prediction model is established too. Finally, the modified optimized integrated prediction model is utilized to predict the settlement of building geotechnical foundations. The experimental results of A-Ma city project, China show that the modified optimized integrated prediction model has better prediction accuracy and has universal applicability than the hyperbolic method and GM (1, 1) in the prediction of foundation settlement of building geotechnical foundations. It can effectively reduce the prediction error after the combination of single methods.

Publisher

Hindawi Limited

Subject

Computer Networks and Communications,Computer Science Applications

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