Bin Ou^1,2,3,4, Haoquan Chi^1,3, Xu’an Qian^1,3, Shuyan Fu^1,3, Zhirui Miao^1,3, Dingzhu Zhao^1,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2026.074757 - 29 January 2026

Abstract Deformation prediction for extra-high arch dams is highly important for ensuring their safe operation. To address the challenges of complex monitoring data, the uneven spatial distribution of deformation, and the construction and optimization of a prediction model for deformation prediction, a multipoint ultrahigh arch dam deformation prediction model, namely, the CEEMDAN-KPCA-GSWOA-KELM, which is based on a clustering partition, is proposed. First, the monitoring data are preprocessed via variational mode decomposition (VMD) and wavelet denoising (WT), which effectively filters out noise and improves the signal-to-noise ratio of the data, providing high-quality input data for subsequent prediction… More > Graphic Abstract

Songlin Yang^1,2, Xingjin Han^1,2, Chufeng Kuang^1,2, Weihua Fang³, Jianfei Zhang⁴, Tiantang Yu^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 49-72, 2022, DOI:10.32604/cmes.2022.018325 - 24 January 2022

Abstract The deformation prediction models of Wuqiangxi concrete gravity dam are developed, including two statistical models and a deep learning model. In the statistical models, the reliable monitoring data are firstly determined with Lahitte criterion; then, the stepwise regression and partial least squares regression models for deformation prediction of concrete gravity dam are constructed in terms of the reliable monitoring data, and the factors of water pressure, temperature and time effect are considered in the models; finally, according to the monitoring data from 2006 to 2020 of five typical measuring points including J₂₃ (on dam section ),… More >

Mengcheng Sun^1,2, Weiya Xu^1,2,*, Huanling Wang^1,3, Qingxiang Meng^1,2, Long Yan^1,2, Wei-Chau Xie⁴

CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 1057-1074, 2021, DOI:10.32604/cmc.2020.012537 - 30 October 2020

Abstract The narrowing deformation of reservoir valley during the initial operation period threatens the long-term safety of the dam, and an accurate prediction of valley deformation (VD) remains a challenging part of risk mitigation. In order to enhance the accuracy of VD prediction, a novel hybrid model combining Ensemble empirical mode decomposition based interval threshold denoising (EEMD-ITD), Differential evolutions—Shuffled frog leaping algorithm (DE-SFLA) and Least squares support vector machine (LSSVM) is proposed. The non-stationary VD series is firstly decomposed into several stationary subseries by EEMD; then, ITD is applied for redundant information denoising on special sub-series,… More >