A New Prediction Model of Dam Deformation and Successful Application

In most dam deformation monitoring practices, some single-point models do not consider the spatial correlation, and the traditional regression models do not consider the nonlinear relationship between the environmental quantity and the deformation quantity, resulting in poor prediction accuracy. In view of the poor accuracy of the monitoring data, which reflect the overall deformation response in the current dam monitoring practices, this paper proposes an innovative solution of ensemble empirical mode decomposition and a wavelet noise reduction method. A high-precision prediction model considering spatial correlation is constructed. By studying the measured deformation data of an arch dam and comparing the performance parameters of various models, the superiority and universality of the proposed method are verified. Dam deformation monitoring data are of great significance to describe the operation behavior of dams. It is significant for us to optimize the health monitoring of dam safety structures and ensure dam safety and realize social harmony in our country.

Authors

Institutions

Publication Details

Journal
Buildings
Published
2025-03-05
DOI
https://doi.org/10.3390/buildings15050818
Citations
3
Primary Topic
Dam Engineering and Safety
Type
article
Field-Weighted Citation Impact
2.86

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A New Prediction Model of Dam Deformation and Successful Application

Meng Yang, Zuqiang Liu, Senlin Li, Shuangping Li et al.
3 citations
Buildings
Dam Engineering and Safety
2.86
article

A New Prediction Model of Dam Deformation and Successful Application

Meng Yang, Zuqiang Liu, Senlin Li, Shuangping Li, Bin Zhang
article en
3 citations

Abstract

In most dam deformation monitoring practices, some single-point models do not consider the spatial correlation, and the traditional regression models do not consider the nonlinear relationship between the environmental quantity and the deformation quantity, resulting in poor prediction accuracy. In view of the poor accuracy of the monitoring data, which reflect the overall deformation response in the current dam monitoring practices, this paper proposes an innovative solution of ensemble empirical mode decomposition and a wavelet noise reduction method. A high-precision prediction model considering spatial correlation is constructed. By studying the measured deformation data of an arch dam and comparing the performance parameters of various models, the superiority and universality of the proposed method are verified. Dam deformation monitoring data are of great significance to describe the operation behavior of dams. It is significant for us to optimize the health monitoring of dam safety structures and ensure dam safety and realize social harmony in our country.

BuildingsVol. 15(5)
Jiangsu Changjiang Electronics Technology (China) (CN), Nanjing Hydraulic Research Institute (CN)
National Natural Science Foundation of China, China Postdoctoral Science Foundation, Natural Science Foundation of Jiangsu Province
Openalex Percentile: Top 11%
Dam Engineering and Safety
2.86
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

A New Prediction Model of Dam Deformation and Successful Application — Meng Yang, Zuqiang Liu, et al. · Buildings (2025) | TGRS Research Map | TGRS