Smooth Mode Decomposition and Its Applications in Operational Modal Analysis
Highlights:
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Data-driven Mode Decomposition with Flexible Choice of Discrete Operators to Regularize the Resolved Modes
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Resolves Eigenfunctions if the System has Uniformly Distributed Parameters
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Approximate the True Mode Shapes when System Parameters Are Non-uniformly Distributed
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Resolves Modal Parameters with Sub-Nyquist Sampled Data
Predicting Freeze-Thaw Cycles of a PCM-Incorporated Concrete Slab based on
Multi-scale Finite Element Simulations and Machine Learning
Practical Prediction of freeze-thaw cycles using multi-scale simulations and machine learning
Phase change materials (PCMs) are used in numerous civil engineering applications to reduce effective heat transfer and energy losses. This study focuses on the effect of microencapsulated low-transition-temperature PCMs on protecting the roads and bridges of Rhode Island from freeze-thaw effects in an efficient and practical way.
Synergistic Fatigue Damage Sensing in 3D-printed Beam Structures
Practical damage ID and Tracking with the presence of Raster-angle-induced Anisotropy
As additive manufacturing becomes applicable in structural engineering, structural degradation and structural health monitoring on structures with 3D-printed materials/substructures become imperative. This project targets monitoring the damage evolution in 3D-printed polymeric structures using a non-parametric data-driven modeling technique, phase space warping, to practically track damages induced by the defect and material anisotropy.
Fatigue Life Prediction under Irregular Loading
with similar statistical and spectral characteristics
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