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

Xun Chen¹, Jinbao Lin^1,2,*, Zai Wang¹

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072495 - 12 January 2026

Abstract This work investigates the effects of deformation mechanisms on the mechanical properties and anisotropy of rolled AZ31B magnesium alloy under uniaxial tension, combining experimental characterization with Visco-Plastic Self Consistent (VPSC) modeling. The research focuses particularly on anisotropic mechanical responses along transverse direction (TD) and rolling direction (RD). Experimental measurements and computational simulations consistently demonstrate that prismatic <a> slip activation significantly reduces the strain hardening rate during the initial stage of tensile deformation. By suppressing the activation of specific deformation mechanisms along RD and TD, the tensile mechanical behavior of the magnesium alloy was further investigated. More >

Junwen Huo, Haicheng Liang, Weiye Dong, Xiaoming Du^*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-20, 2026, DOI:10.32604/cmc.2025.073285 - 09 December 2025

Abstract With the rapid advancement of electromagnetic launch technology, enhancing the structural stability and thermal resistance of armatures has become essential for improving the overall efficiency and reliability of railgun systems. Traditional aluminum alloy armatures often suffer from severe ablation, deformation, and uneven current distribution under high pulsed currents, which limit their performance and service life. To address these challenges, this study employs the Johnson–Cook constitutive model and the finite element method to develop armature models of aluminum matrix composites with varying heterogeneous graphene volume fractions. The temperature, stress, and strain of the armatures during operation… More >

Qinghua Wang^*, Yi Zhu

The International Conference on Computational & Experimental Engineering and Sciences, Vol.34, No.1, pp. 1-2, 2025, DOI:10.32604/icces.2025.012693

Abstract This study presents a robust and automated image registration framework designed to enhance the precision and efficiency of full-field deformation measurement in experimental mechanics applications. Traditional optical techniques, such as the sampling moiré method, rely on manual alignment processes that introduce significant errors and inefficiencies, particularly when analyzing large datasets or operating under dynamic experimental conditions. Addressing these limitations, the proposed method integrates the Maximally Stable Extremal Regions (MSER) algorithm to automate the alignment of grating images with sub-pixel accuracy. The technique is specifically tailored to handle challenges arising from high-noise environments and complex deformation… More >

Xiangru He¹, Ying Deng¹, Zefu Li¹, Jie Zhi^1,2, Yonglin Chen^1,2, Weidong Yang^1,2,3,*, Yan Li^1,2,3,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.34, No.1, pp. 1-1, 2025, DOI:10.32604/icces.2025.012395

Abstract Coordinated control of structural accuracy and mechanical properties is the key to composites manufacturing and the prerequisite for aerospace applications. In particular, accurate and efficient prediction of curing-induced deformation (CID) is of vital importance for fiber reinforced polymer composites quality control. In this study, we explored a novel spatio-temporal prediction model, which incorporates the finite element method with a deep learning framework to efficiently forecast the curing-induced deformation evolution of composite structures. Herein, we developed an integrated convolutional neural network (CNN) and long short-term memory (LSTM) network approach to capture both the space-distributed and time-resolved… More >

Yulong Zhao¹, Yang Luo^1,*, Yuming Luo², Yulai Pang², Ruihan Zhang¹, Zihan Zhao³

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.12, pp. 3073-3090, 2025, DOI:10.32604/fdmp.2025.070685 - 31 December 2025

Abstract The development of underground gas storage (UGS) systems is vital for maintaining stability between energy supply and demand. This study explores the dynamic response mechanisms of carbonate reservoirs subjected to intense injection–production cycling during UGS operations. By integrating three-dimensional digital core technology with a coupled poro-mechanical model, we simulate the pore-scale behavior of a representative Huangcaoxia UGS carbonate core. The results demonstrate that fluid–solid coupling effects markedly amplify permeability reduction, far exceeding the influence of porosity variations alone. More significantly, gas production leads to a pronounced decline in permeability driven by rising effective stress, arising More >

Mohd Hakimi Abdul Rahman¹, Amir Sharifuddin Ab Latip^1,*, Zulkiflee Abd Latif^1,2, Siti Balqis Mohd Tun¹, Nur Azlina Hariffin¹, Mohd Fikri Razali³

Revue Internationale de Géomatique, Vol.34, pp. 959-972, 2025, DOI:10.32604/rig.2025.071109 - 12 December 2025

Abstract Atmospheric phase delay, primarily caused by water vapor in the troposphere, is a major source of error in InSAR measurements, especially for land subsidence monitoring. This study integrates GNSS and GACOS data to correct tropospheric delay and enhance InSAR accuracy in Banting, Selangor. A total of 27 Sentinel-1A images, 14 GNSS stations, and 27 corresponding GACOS ZTD maps were used to monitor subsidence between 2023 and 2025. The InSAR data were processed using SNAP, StaMPS, and the TRAIN toolbox, incorporating both GNSS- and GACOS-derived ZTD corrections. The results show that applying atmospheric correction improved the… More >

Si-Kai Wang¹, Ti-Cai Wang¹, Di-Fei Yi², Jia Rui³, Peng-Fei Cao⁴, Hua-Ping Wang^1,5,*

Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1411-1432, 2025, DOI:10.32604/sdhm.2025.070034 - 17 November 2025

Abstract As a key storage facility, the structural safety of large oil tanks is directly related to the stable operation of the energy system. The static pressure caused by the change of liquid level is one of the main loads in the service process of storage tanks, which determines the structural deformation and damage risk. To explore the structural deformation properties under the change of liquid levels and provide a theoretical basis for the prevention and control of damage risk, this paper systematically analyzes the mechanical response of storage tanks under the pressures induced by different… More > Graphic Abstract

Yidong Xu¹, Weijie Zhuge^1,2, Jialei Wang¹, Xiaopeng Yu^3,*, Kan Wu⁴

Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1507-1527, 2025, DOI:10.32604/sdhm.2025.069021 - 17 November 2025

Abstract The performance of concrete can be affected by many factors, including the material composition, environmental conditions, and construction methods, and it is challenging to predict the performance evolution accurately. The rise of artificial intelligence provides a way to meet the above challenges. This article elaborates on research overview of artificial neural network (ANN) and its prediction for concrete strength, deformation, and durability. The focus is on the comparative analysis of the prediction accuracy for different types of neural networks. Numerous studies have shown that the prediction accuracy of ANN can meet the standards of the More >

Roman Shults^1,2,*, Natalia Kulichenko³, Andriy Annenkov³, Oleksandr Adamenko³

Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1433-1456, 2025, DOI:10.32604/sdhm.2025.068099 - 17 November 2025

Abstract Oil tanks are essential components of the oil industry, facilitating the safe storage and transportation of crude oil. Safely managing oil tanks is a crucial aspect of environmental protection. Oil tanks are often used under extreme operational conditions, including dynamic loads, temperature variations, etc., which may result in unpredictable deformations that can cause severe damage or tank collapses. Therefore, it is essential to establish a monitoring system to prevent and predict potential deformations. Terrestrial laser scanning (TLS) has played a significant role in oil tank monitoring over the past decades. However, the full extent of… More >