The ability to classify the gender of occupants in a building has far-reaching applications including security and retail sales. The authors demonstrate the success of machine learning techniques for gender classification. Highsensitivity accelerometers mounted non-invasively beneath an actual building floor provide the input for these machine learning methods. While other approaches using gait measurements, such as vision systems and wearable sensors, provide the potential for gender classification, they each face limitations. These limitations include an invasion of privacy, occupant compliance, required line of sight, and/or high sensor density. Underfloor mounted accelerometers overcome these limitations. The authors utilize the highly-instrumented Goodwin Hall smart building on the Virginia Tech campus to measure vibrations of the walking surface caused by walkers. In this study, the gait of fifteen individual walkers was recorded as they, alone, walked down the instrumented hallway. Fourteen accelerometers, mounted underneath the walking surface, recorded walking trials with the placement of the sensors unknown to the walker. This work studies Bagged Decision Trees, Boosted Decision Trees, Support Vector Machines (SVMs), and Neural Networks as the machine learning techniques for their ability to classify gender. A tenfold-cross-validation method is used to comment on the validity of the algorithm's ability to generalize to new walkers. This work demonstrates that a gender classification accuracy of 88% is achievable using the underfloor vibration data from the Virginia Tech Goodwin Hall by using Decision Tree approaches.

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As the complexity and scales of dynamic models increase, novel and efficient model correlation methodologies are vital to the development of accurate models. Classically, to correlate a Finite Element Model (FEM) such that it matches a dynamic test, an experienced engineer chooses a small subset of input parameters that are surmised to be crucial, sensitive and/or possibly erroneous. The operator will then use engineering judgment, or a model updating technique to update the selected subset of parameters until the error between the FEM and the test article is reduced to within a set bound. To reduce the intricacy and difficulty of model correlation, a methodology is proposed to provide a quantitative parameter importance ranking using a model reduction algorithm applied to a parameter sensitivity analysis. Four model reduction algorithms are studied in this effort, the Discrete Empirical Interpolation Method (SVD-DEIM), Q-DEIM, Projection Coefficient and finally Weighted Projection Coefficient. These model reduction methods identify and rank critical parameters, enabling the selection of a minimum set of critical correlation parameters. This reduced set of parameters results in reduced computational resources and engineering effort required to generate a correlated model. The insight gained using these methods is essential in developing an optimal, reduced parameter set that provides high correlation capability with minimal iterative costs. To evaluate the proposed parameter selection methodology, a representative set of academic and industry experts provided their engineering judgment for comparison with the methodology presented. A comprehensive investigation of the robustness of this methodology is performed on a simple cantilever beam for demonstration. The scale of the model has expressly been chosen to allow for all potential ranking variations to be evaluated so that these ranking methods can be understood relative to the true optimal ranking. The ranking robustness to incorrect engineering judgment, resulting in uncertainty in the assumed size of the design space and, therefore, the error bounds, is investigated. The methodology presented identifies the most useful parameters for correlation, enabling a straightforward and computationally efficient model correlation approach as compared with other methods. To quantify the ranking quality, a metric, the Correlation Norm Error, is developed. For the problem discussed, blind random assessments result in a Correlation Norm Error of 413.3. Engineering judgment has been shown to improve upon blind random assessments, reducing the Correlation Norm Error to 334.3. The best performing model reduction method, Q-DEIM using 10 FEM runs as the input, was able to identify the optimal ranking correctly, reducing the Correlation Norm Error to zero.