Yushan Ye¹, Tao Gao¹, Liankun Wang², Junjie Ma², Yingchun Cai², Heng Liu^2,*, Xiaoge Liu²

Structural Durability & Health Monitoring, Vol.19, No.1, pp. 145-166, 2025, DOI:10.32604/sdhm.2024.053756 - 15 November 2024

Abstract To investigate the evolution of load-bearing characteristics of pre-stressed beams throughout their service life and to provide a basis for accurately assessing the actual working state of damaged pre-stressed concrete T-beams, destructive tests were conducted on full-scale pre-stressed concrete beams. Based on the measurement and analysis of beam deflection, strain, and crack development under various loading levels during the research tests, combined with the verification coefficient indicators specified in the codes, the verification coefficients of bridges at different stages of damage can be examined. The results indicate that the T-beams experience complete, incomplete linear, and… More >

Chaozhi Cai^*, Tiexin Xu, Jianhua Ren, Yingfang Xue

Structural Durability & Health Monitoring, Vol.19, No.1, pp. 125-144, 2025, DOI:10.32604/sdhm.2024.052813 - 15 November 2024

Abstract A bearing fault diagnosis method based on the Markov transition field (MTF) and SEnet (SE)-IShufflenetV2 model is proposed in this paper due to the problems of complex working conditions, low fault diagnosis accuracy, and poor generalization of rolling bearing. Firstly, MTF is used to encode one-dimensional time series vibration signals and convert them into time-dependent and unique two-dimensional feature images. Then, the generated two-dimensional dataset is fed into the SE-IShufflenetV2 model for training to achieve fault feature extraction and classification. This paper selects the bearing fault datasets from Case Western Reserve University and Paderborn University… More >

Ting Tan^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.2, pp. 1-1, 2024, DOI:10.32604/icces.2024.012670

Abstract Owing to their compliance, soft robots demonstrate enhanced compatibility with humans and the environment compared with traditional rigid robots. However, ensuring the working effectiveness of artificial muscles that actuate soft robots in confined spaces or underloaded conditions remains a challenge. Drawing inspiration from avian pneumatic bones, we propose the incorporation of a light weight endoskeleton into artificial muscles to augment the mechanical integrity and tackle load-bearing environmental difficulties. We present a soft origami hybrid artificial muscle that features a hollow origami metamaterial interior with a rolled dielectric elastomer exterior. The programmable nonlinear origami metamaterial endoskeleton More >

Nuo Ma¹, Qingyang Liu¹, Junhui Meng^1,2,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.1, pp. 1-2, 2024, DOI:10.32604/icces.2024.011195

Abstract Due to its flexibility and foldable ability, the inflatable wing is widely employed to loitering munitions and aerostats [1-3]. Meanwhile, as a typical flexible thin-walled structure, the wrinkling and buckling behaviors of the inflatable wing induced in flight will limit its load-bearing capacity [4,5]. Therefore, a wrinkling-resistant structural configuration is the key to improving performance of the inflatable wing. Among various schemes, the swept baffled structure is considered to have the potential to retard wrinkling because of the designable axis of twist [6,7]. However, owing to the flexible large deformation of inflatable wing under aerodynamic… More >

Alaa U. Khawaja¹, Ahmad Shaf^2,*, Faisal Al Thobiani³, Tariq Ali⁴, Muhammad Irfan⁵, Aqib Rehman Pirzada², Unza Shahkeel²

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2399-2420, 2024, DOI:10.32604/cmes.2024.054257 - 31 October 2024

Abstract Electric motor-driven systems are core components across industries, yet they’re susceptible to bearing faults. Manual fault diagnosis poses safety risks and economic instability, necessitating an automated approach. This study proposes FTCNNLSTM (Fine-Tuned TabNet Convolutional Neural Network Long Short-Term Memory), an algorithm combining Convolutional Neural Networks, Long Short-Term Memory Networks, and Attentive Interpretable Tabular Learning. The model preprocesses the CWRU (Case Western Reserve University) bearing dataset using segmentation, normalization, feature scaling, and label encoding. Its architecture comprises multiple 1D Convolutional layers, batch normalization, max-pooling, and LSTM blocks with dropout, followed by batch normalization, dense layers, and More >

Kai Wang^1,2,3, Lihua Yang^1,2,3,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.29, No.3, pp. 1-1, 2024, DOI:10.32604/icces.2024.011018

Abstract Although offset-halves journal bearings (OHJBs) are widely used in marine powertrains, the research on nonlinear tribo-dynamics is still limited, particularly under dynamic loading. To overcome such limitations, this study proposes a novel dynamic model that couples the influences of step load and thermoelastohydrodynamic (TEHD) effect. Based on the numerical model, a transient TEHD analysis for dynamically loaded OHJBs is done. Moreover, a modified stability criterion is developed. Nonlinear behaviors and transient stability of OHJBs under step load are systematically studied. The correlations of bearing characteristics such as the maximum film temperature, minimum film thickness, maximum More >

Yan Zhao^1,*, Yuanyuan Wu², Shengliang He³, Zhenglu Gao¹, Ziyan Huang¹, Chenzheng Lv⁴

Structural Durability & Health Monitoring, Vol.18, No.5, pp. 641-661, 2024, DOI:10.32604/sdhm.2024.049601 - 19 July 2024

Abstract Prestress enables the Glulam beam could make full use of the compression strength, and then increase the span, but it still could not reduce all drawbacks, such as cross-section weakening and small force arm. To avoid slotting and ensure suitable tension and compression couple, one kind of novel anchor has been proposed, which could meet the bearing capacity requirement. And then the bending test of prestressed Glulam beams with a geometric scale ratio of 1: 2 was simulated, to investigate the effect of the force arm on bending capacities, failure modes, and deformation performance. Results More > Graphic Abstract

Chunming Wu¹, Shupeng Zheng^2,*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4395-4411, 2024, DOI:10.32604/cmc.2024.049665 - 20 June 2024

Abstract Deep neural networks have been widely applied to bearing fault diagnosis systems and achieved impressive success recently. To address the problem that the insufficient fault feature extraction ability of traditional fault diagnosis methods results in poor diagnosis effect under variable load and noise interference scenarios, a rolling bearing fault diagnosis model combining Multi-Scale Convolutional Neural Network (MSCNN) and Long Short-Term Memory (LSTM) fused with attention mechanism is proposed. To adaptively extract the essential spatial feature information of various sizes, the model creates a multi-scale feature extraction module using the convolutional neural network (CNN) learning process.… More >

Guangfei Jia^*, Yanchao Meng, Zhiying Qin

Structural Durability & Health Monitoring, Vol.18, No.4, pp. 445-463, 2024, DOI:10.32604/sdhm.2024.049298 - 05 June 2024

Abstract The vibration signals of rolling bearings exhibit nonlinear and non-stationary characteristics under the influence of noise. In intelligent fault diagnosis, unprocessed signals will lead to weak fault characteristics and low diagnostic accuracy. To solve the above problem, a fault diagnosis method based on parameter optimization feature mode decomposition and improved deep belief networks is proposed. The feature mode decomposition is used to decompose the vibration signals. The parameter adaptation of feature mode decomposition is implemented by improved whale optimization algorithm including Levy flight strategy and adaptive weight. The selection of activation function and parameters is More > Graphic Abstract

Yang Li¹, Zikang Zheng^1,*, Jiangkun Zhang²

Structural Durability & Health Monitoring, Vol.18, No.4, pp. 425-444, 2024, DOI:10.32604/sdhm.2024.046584 - 05 June 2024

Abstract Due to the lack of a quantitative basis for the inspection, evaluation, and identification of existing transmission tower foundations, a new fuzzy comprehensive evaluation method is proposed to assess the reliability of transmission tower foundation bearing capacity. This method is based on the reliability analysis of the transmission tower foundation bearing capacity by analyzing the sensitivity of degradation of detection indexes on the reliability of transmission tower foundation bearing capacity, the weighting coefficient matrix is established about the influencing factors in the evaluation model. Through the correlation analysis between the bearing capacity degradation of the More > Graphic Abstract