Geographic mapping model for underground structural deformation monitoring

Zheng, Wei; Li, Yinsheng; Lu, Qi; Lü, Ping

doi:10.1080/19475705.2013.812994

Cited by 1 publication

(1 citation statement)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To overcome the shortcomings of traditional methods in the temporal prediction of tunnel deformation, we can apply intelligent algorithms to improve the traditional prediction models 1 – 6 . Intelligent algorithms are capable of self-optimizing measured tunnel settlement deformation data and fitting complex nonlinear relationships.…”

Section: Introductionmentioning

confidence: 99%

Time-series prediction of settlement deformation in shallow buried tunnels based on EMD-SSA-GRNN model

Li,

Shu,

Chang

2024

Sci Rep

View full text Add to dashboard Cite

Tunnel settlement deformation monitoring is a complex task and can result in nonlinear dynamic changes. To overcome the disturbances caused by historical data and the difficulty in selecting input parameters during deformation prediction, a decomposition, reconstruction and optimization method for tunnel settlement deformation prediction is proposed. First, empirical mode decomposition (EMD) is used to decompose the in-situ monitoring data and reduce the interactions among information at different scales in sequences. Then, the monitoring data are decomposed into intrinsic mode functions (IMFs). Secondly, the smoothing factor of the generalized regression neural network (GRNN) is optimized by using the sparse search algorithm (SSA). An EMD-SSA-GRNN deformation prediction model is developed using the optimized GRNN algorithm and is used to predict the changes in the decomposed IMFs. Finally, using the measured deformation data from a shallowly buried tunnel along the Kaizhou-Yunyang Highway in Chongqing, China, the reliability and accuracy of different models are analysed. The results show that tunnel settlement deformation exhibited a trend and a slow change in the early stage, a rapid change in the middle stage and a slow change in the late stage, and the rate of change was significantly influenced by the excavation time and the upper and lower geological layers. The prediction accuracy of the EMD-SSA-GRNN model after EMD improved from 19.2 to 59.4% relative to that of the SSA-GRNN and single GRNN models. Moreover, we find that the three error evaluation indicators of the EMD-SSA-GRNN model are lower than those of the other models and that the results of the proposed model and are more strongly correlated with measured data.

show abstract

Section: Introductionmentioning

confidence: 99%