Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Jinlei Shen"'
Publikováno v:
Integrating Materials and Manufacturing Innovation. 10:568-587
This paper establishes the wavelet transformation induced multi-time scaling (WATMUS) method as an enabler for modeling fatigue crack nucleation at microstructural and structural scales of polycrystalline metals. The WATMUS method derives its efficie
Publikováno v:
Acta Materialia. 252:118929
Publikováno v:
Journal of Civil Structural Health Monitoring. 10:957-972
This paper presents a novel approach to mitigating the effect of temperature variations on the bridges’ dynamic modal properties for more reliably detecting scour damage around bridge piles based on the vibration-based measurements. The novelty of
Publikováno v:
International Journal of Plasticity. 115:268-292
This paper develops necessary preprocessors for image-based micromechanical analysis of polycrystalline-polyphase microstructures of Al alloys such as Al7075-T651. Starting from input data in the form of electron back scatter diffraction (EBSD) and s
Publikováno v:
International Journal of Plasticity. 151:103182
Publikováno v:
Acta Materialia. 149:142-153
This paper implements the image-based crystal plasticity FE model with explicit twin evolution, developed in [1, 2], to study mechanisms of deformation and twinning in polycrystalline microstructures of the Mg alloy AZ31. The physics of twin nucleati
Autor:
Wei Zheng, Jinlei Shen
Publikováno v:
Journal of Civil Structural Health Monitoring. 6:153-173
This paper presents a novel Adjustable Hybrid Resampling (AHR) approach to deriving samples representing probabilistic distributions of unknown damage indexes through Bayesian inference based on vibration measurements. The AHR is motivated by the nee
Publikováno v:
Transportation Research Record: Journal of the Transportation Research Board. 2460:117-127
This paper presents a computationally efficient Bayesian inference framework and its application to infer damage in truss bridges. The novelty of the proposed framework for reducing the computational burden of Bayesian inference of structural damage