Use of Foamed Cement Banking for Reducing Expressways Embankment Settlement

Expressways are often built on soft ground, the foundation of which is not processed adequately during the construction period. Consequently, the traffic safety and embankment stability will be seriously affected due to uneven settlement. The technology of holing the embankment and replacing foamed cement banking (FCB) could control the settlement of an embankment without road closure, thus reduce the impact of construction on normal operation of highways. In this paper, the principle of FCB is described. Additionally, a sedimentation ratio calculation method, through the analysis of the settlement load ratio, is proposed for calculating the roadbed replacement thickness. This paper takes the example of the test section EK0 + 323 on Shen-Jia-Hu expressway in Zhejiang Province and combines with site settlement monitoring data to confirm the effectiveness of the calculation method proposed.

Application of non-equidistant GM(1,1) model based on the fractional-order accumulation in building settlement monitoring

Non-equidistant GM(1,1) (abbreviated as NEGM) model is widely used in building settlement prediction because of its high accuracy and outstanding adaptability. To improve the building settlement prediction accuracy of the NEGM model, the fractional-order non-equidistant GM(1,1) model (abbreviated as FNEGM) is established in this study. In the modeling process of the FNEGM model, the fractional-order accumulated generating sequence is extended based on the first-order accumulated generating sequence, and the optimal parameters that increase the prediction precision of the model are obtained by using the whale optimization algorithm. The FNEGM model and the other two grey prediction models are applied to three cases, and five prediction performance indexes are used to evaluate the prediction precision of the three models. The results show that the FNEGM model is more suitable for predicting the settlement of buildings than the other two grey prediction models.