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 >

Qian Li^*, Jiali Zhou, Yan Li^*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.4, pp. 1-1, 2025, DOI:10.32604/icces.2025.012234

Abstract Plant fibres are emerging as sustainable composite reinforcements. Compared to synthetic fibres, the hierarchical and twisted structure of plant fibres may produce microfibril angle (MFA) reorientation and untwisting time-varying behaviors after loading and consequently decide the mechanical response of plant fibre reinforced composites (PFRCs) in macro-scale. Existing theories, assuming homogeneous fibres, cannot accurately describe the multi-scale coupling nonlinear deformations of PFRCs. Based on this, a multi-scale analysis method on the nonlinear tensile responses of flax fibre reinforced composites (FFRCs) was proposed, focusing on the effect of the evolution of MFA in micro-scale and twist angle… More >

Nan Yang^*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.4, pp. 1-1, 2025, DOI:10.32604/icces.2025.011055

Abstract Integrating mechanical computing capabilities into robotic materials or systems enhances their intelligence in stimulus-response processes. However, current mechanical computing systems face limitations such as incomplete functionality, inflexible computational rules, challenges in implementing sequential and random logic operations, and lack of reusability. To address these issues, we propose a straightforward design method based on logical expressions to achieve more complex computational tasks. We developed soft B-shaped mechanical metamaterial units and introduced stress inputs through compression. The outputs are represented by light-shielding effects caused by unit deformation. Using this approach, we successfully implemented logic gates and their… More >

Linfeng Jiang^1,*, Guisen Liu¹, Yao Shen¹, Jian Wang²

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.3, pp. 1-1, 2025, DOI:10.32604/icces.2025.011397

Abstract The plastic deformation of Mg/Ti alloys arises from the synergistic interplay of dislocation slip and deformation twinning. To model these mechanisms, we previously developed a mesoscale CPFE-PF framework that couples crystal plasticity finite element (CPFE) and phase field (PF) methods, enabling predictions of microstructure evolution and mechanical behavior under complex loading. A central challenge, however, lies in accurately capturing deformation twinning—a process critical for accommodating shear and reorienting crystal domains in low-symmetry metals. Twin propagation and thickening occur via twinning dislocations/disconnections at the atomic scale, while at larger scales they are governed by the migration… More >

Chenghuan Liu, Xiangbo Hu, Xiaogang Wang^*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.3, pp. 1-1, 2025, DOI:10.32604/icces.2025.010910

Abstract Digital image correlation (DIC) is an emerging non-contact optical measurement method that tracks speckle patterns on the specimen surface to obtain the deformation, providing an advanced methodology for the quantitative evaluation of full-field strain. The present work focuses on the quantitative assessment of deformation from micro to macro scales based on the DIC method and examines damage evolution in metal materials under static and cyclic loading conditions. First, an SEM-based DIC method allowing high-resolution strain measurement at subgrain scales is developed for investigating strain partitioning in dual-phase steel. The results reveal that the strain distribution… More >

Weizheng Lu, Shuo Wang, Yang Chen, Jia Li^*, Qihong Fang^*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.3, pp. 1-1, 2025, DOI:10.32604/icces.2025.010711

Abstract The multi-principal elemental alloys (MPEAs) exhibit excellent combinations of mechanical properties and radiation-resistant, are considered potential candidates for aerospace industries and advanced reactors. However, the quantitative contribution of microstructure on the strengthening mechanism remains challenging at the micro-scale, which greatly limits the long-term application. To address this, we developed a hierarchical multiscale simulation framework that covers potential physical mechanisms to explore the hardening effects of chemical short-range order (CSRO) and irradiation defects in MPEA. Firstly, by combining atomic simulation, discrete dislocation dynamics, and crystal plasticity finite element method, a hierarchical cross-scale model covering heterostructure lattice… More >

Zhuoyuan Zheng^*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.2, pp. 1-1, 2025, DOI:10.32604/icces.2025.010939

Abstract Silicon (Si) anodes are promising candidates for lithium-ion batteries due to their high theoretical capacity and low operating voltage. However, the significant volume expansion that occurs during lithiation presents challenges, including material degradation and decreased cycle life. This study employs an electrochemical-mechanical-thermal coupled finite element model, supported by experimental validation, to investigate the impact of lithiation-induced deformation on the energy dissipation of Si anodes. We quantitatively investigate the effects of several key design parameters—C-rate, Si layer thickness, and lithiation depth—on energy losses resulting from various mechanisms, such as mechanical energy loss, polarization, and joule heating.… More >

Seunghwan Seo^1,2,*, Moonkyung Chung¹

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 2893-2922, 2025, DOI:10.32604/cmes.2025.069668 - 30 September 2025

Abstract Excavation-induced deformations of earth-retaining walls (ERWs) can critically affect the safety of surrounding structures, highlighting the need for reliable prediction models to support timely decision-making during construction. This study utilizes traditional statistical ARIMA (Auto-Regressive Integrated Moving Average) and deep learning-based LSTM (Long Short-Term Memory) models to predict earth-retaining walls deformation using inclinometer data from excavation sites and compares the predictive performance of both models. The ARIMA model demonstrates strengths in analyzing linear patterns in time-series data as it progresses over time, whereas LSTM exhibits superior capabilities in capturing complex non-linear patterns and long-term dependencies within… More > Graphic Abstract