Book Chapter
Showing results 1 - 6 out of 6
Journal Articles
Showing results 101 - 120 out of 302
2023
Wang, Z. W., Lu, X. F., Zhang, W. M., Fragkoulis, V. C., Beer, M., & Zhang, Y. F. (2023). Deep learning-based reconstruction of missing long-term girder-end displacement data for suspension bridge health monitoring. Computers and Structures, 284, Article 107070. https://doi.org/10.1016/j.compstruc.2023.107070
Weng, L. L., Yang, J. S., Chen, J. B., & Beer, M. (2023). Structural design optimization under dynamic reliability constraints based on probability density evolution method and quantum-inspired optimization algorithm. Probabilistic Engineering Mechanics, 74, Article 103494. https://doi.org/10.1016/j.probengmech.2023.103494
Xu, Y., Ji, J. C., Ni, Q., Feng, K., Beer, M., & Chen, H. (2023). A graph-guided collaborative convolutional neural network for fault diagnosis of electromechanical systems. Mechanical Systems and Signal Processing, 200, Article 110609. https://doi.org/10.1016/j.ymssp.2023.110609
Yuan, X., Valdebenito, M. A., Zhang, B., Faes, M. G. R., & Beer, M. (2023). Efficient decoupling approach for reliability-based optimization based on augmented Line Sampling and combination algorithm. Computers and Structures, 280, Article 107003. https://doi.org/10.1016/j.compstruc.2023.107003
Yuan, X., Qian, Y., Chen, J., Faes, M. G. R., Valdebenito, M. A., & Beer, M. (2023). Global failure probability function estimation based on an adaptive strategy and combination algorithm. Reliability Engineering and System Safety, 231, Article 108937. https://doi.org/10.1016/j.ress.2022.108937
Yuan, X., Wang, S., Valdebenito, M. A., Faes, M. G. R., & Beer, M. (2023). Sample regeneration algorithm for structural failure probability function estimation. Probabilistic Engineering Mechanics, 71, Article 103387. https://doi.org/10.1016/j.probengmech.2022.103387
Zeng, D., Zhang, H., Dai, H., & Beer, M. (2023). Scalable risk assessment of large infrastructure systems with spatially correlated components. Structural safety, 101, Article 102311. https://doi.org/10.1016/j.strusafe.2022.102311
Zhang, K., Chen, N., Liu, J., Yin, S., & Beer, M. (2023). An efficient meta-model-based method for uncertainty propagation problems involving non-parameterized probability-boxes. Reliability Engineering and System Safety, 238, Article 109477. https://doi.org/10.1016/j.ress.2023.109477
Zhang, Y., Xu, J., & Beer, M. (2023). A single-loop time-variant reliability evaluation via a decoupling strategy and probability distribution reconstruction. Reliability Engineering and System Safety, 232, Article 109031. https://doi.org/10.1016/j.ress.2022.109031
Zhang, Y., Ren, Z., Feng, K., Yu, K., Beer, M., & Liu, Z. (2023). Universal source-free domain adaptation method for cross-domain fault diagnosis of machines. Mechanical Systems and Signal Processing, 191, Article 110159. https://doi.org/10.1016/j.ymssp.2023.110159
Zheng, Z., Beer, M., & Nackenhorst, U. (2023). An iterative multi-fidelity scheme for simulating multi-dimensional non-Gaussian random fields. Mechanical Systems and Signal Processing, 200, Article 110643. https://doi.org/10.1016/j.ymssp.2023.110643
Zheng, Z., Valdebenito, M., Beer, M., & Nackenhorst, U. (2023). A stochastic finite element scheme for solving partial differential equations defined on random domains. Computer Methods in Applied Mechanics and Engineering, 405, Article 115860. https://doi.org/10.1016/j.cma.2022.115860
Zheng, Z., Dai, H., & Beer, M. (2023). Efficient structural reliability analysis via a weak-intrusive stochastic finite element method. Probabilistic Engineering Mechanics, 71, Article 103414. https://doi.org/10.1016/j.probengmech.2023.103414
Zheng, Z., Valdebenito, M., Beer, M., & Nackenhorst, U. (2023). Simulation of random fields on random domains. Probabilistic Engineering Mechanics, 73, Article 103455. https://doi.org/10.1016/j.probengmech.2023.103455
2022
Behrendt, M., Kitahara, M., Kitahara, T., Comerford, L., & Beer, M. (2022). Data-driven reliability assessment of dynamic structures based on power spectrum classification. Engineering structures, 268, Article 114648. https://doi.org/10.1016/j.engstruct.2022.114648
Behrendt, M., Angelis, M. D., Comerford, L., Zhang, Y., & Beer, M. (2022). Projecting interval uncertainty through the discrete Fourier transform: An application to time signals with poor precision. Mechanical Systems and Signal Processing, 172, Article 108920. https://doi.org/10.1016/j.ymssp.2022.108920
Behrendt, M., Bittner, M., Comerford, L., Beer, M., & Chen, J. (2022). Relaxed power spectrum estimation from multiple data records utilising subjective probabilities. Mechanical Systems and Signal Processing, 165, Article 108346. https://doi.org/10.1016/j.ymssp.2021.108346
Bi, S., He, K., Zhao, Y., Moens, D., Beer, M., & Zhang, J. (2022). Towards the NASA UQ Challenge 2019: Systematically forward and inverse approaches for uncertainty propagation and quantification. Mechanical Systems and Signal Processing, 165, Article 108387. https://doi.org/10.1016/j.ymssp.2021.108387
Chen, G., Beer, M., & Liu, Y. (2022). Modeling response spectrum compatible pulse-like ground motion. Mechanical Systems and Signal Processing, 177, Article 109177. https://doi.org/10.1016/j.ymssp.2022.109177
Cheng, M., Dang, C., Frangopol, D. M., Beer, M., & Yuan, X.-X. (2022). Transfer prior knowledge from surrogate modelling: A meta-learning approach. Computers and Structures, 260, Article 106719. https://doi.org/10.1016/j.compstruc.2021.106719
