Chrystal Chern

The number of modes of a mechanical system is generally assumed to equal its number of degrees of freedom. In this work, a simple non-classically damped system is shown to possess fewer modes than degrees of freedom unless a restrictive condition is met. When this condition is met, while there is a full set of modes, they are not orthogonal. These results hold for vanishingly small non-classical damping, can evade detection by the modal phase collinearity metric, and are relevant to system identification of mechanical systems in which non-classical damping is present.

Hula hooping is a rhythmic activity where coordinated body movements induce a steady motion of the hoop. While a range of coordinated movements can successfully actuate the hoop, extracting the causal human motions for this multi-harmonic symphony of gyrations and rotations is an open problem that we seek to answer. Our methods use motion tracking of the body and hoop along with a network analysis for a single set of coordinated motions to establish that a specific rotation of the femur enables the tilting motion of the hoop and that specific rotations of the femur, tibia, and metatarsal enable the whirling motion of the hoop. The conclusions from network analysis are shown to be far superior to the results obtained from a principal component analysis and have application to human motion studies involving rehabilitation and assistive devices.

In bridge health monitoring (BHM), a prominent goal is to rapidly deliver assessment metrics for these essential and aging urban lifelines when subjected to natural hazard. A vibration-based machine learning (ML) BHM paradigm has been established over the past three decades to allow near-real-time automated health state classification, with a particular focus on the tasks of feature engineering and ML damage identification. This paper presents the human–machine collaboration (H-MC) framework to address challenges of this paradigm, especially in the context of reinforced concrete highway BHM. These challenges include specification of strong motion events, data multidimensionality, and ML model interpretability. The H-MC framework for BHM employs the techniques of multivariate novelty detection and probability of exceedance envelope models with ordinal filter-based feature selection to maximize the use of available data from both recorded and simulated events while maintaining the statistical and physical significance of the results. The framework is demonstrated using a numerical example and two case studies. The findings show the effectiveness of the proposed method for efficient damage assessment to facilitate rapid decision-making.