Application of non-equal interval GM (1,1) model in prediction of building settlement

Jiameng Wei,Huagen Jiang,Jianpeng Diao

doi:10.1088/1755-1315/636/1/012023

Jiameng Wei, Huagen Jiang + Show 1 more

Open Access

https://doi.org/10.1088/1755-1315/636/1/012023

Copy DOI

Abstract

In order to solve the impact of the uneven settlement of the building on the construction process and safety evaluation of the building, this paper takes the building settlement as the monitoring object, and uses the non-equidistant GM (1, 1) model to construct the model, and applies it to the construction of a building. Settlement analysis. In addition, the prediction accuracy of the model is tested for the posterior error ratio and small probability error to ensure the reliability and accuracy of the model application.

Highlights

The settlement phenomenon is common in nature
According to the characteristics of the non-equal time interval of building settlement monitoring, the 1-WAGO sequence is generated with the adjacent observation time interval as the weight, and the gray system non-equal interval GM (I1,1) model is established[3]
The results are tested by the posterior difference ratio, The simulation forecast data analysis shows that the settlement result predicted by the model is correct, reliable and accurate[4]

Summary

Introduction

The settlement phenomenon is common in nature. In order to make the work proceed smoothly, it is necessary to monitor the deformation of the building, that is, to use instruments to Dynamic buildings carry out static and periodic measurements[2]. Through the analysis of monitoring data, the safety monitoring and forecasting of buildings can be completed. According to the characteristics of the non-equal time interval of building settlement monitoring, the 1-WAGO sequence is generated with the adjacent observation time interval as the weight, and the gray system non-equal interval GM (I1,1) model is established[3]. The results are tested by the posterior difference ratio, The simulation forecast data analysis shows that the settlement result predicted by the model is correct, reliable and accurate[4]

Gray generation

Conclusion