Ground-motion data processing is a necessity for most earthquake engineering related studies. Important engineering parameters such as the peak values of ground motion and the ordinates of the response spectra are determined from the strong ground-motion data recorded by accelerometers. However, the raw data needs to be processed since the recorded data always contains high- and low-frequency noise from different sources. Low-cut filters are the most popular ground-motion data processing scheme for removing long-period noise. Removing long-period noise from the raw accelogram is important since the displacement spectrum that provides primary information about deformation demands on structural systems is highly sensitive to the long-period noise. The objective of this study is to investigate the effect of low-cut filtering period on linear and nonlinear deformation demands. A large number of strong ground motions from Europe and the Middle East representing different site classes as well as different magnitude and distance ranges are used to conduct statistical analysis. The statistical results are used to investigate the influence of low-cut filter period on spectral displacements. The results of the study are believed to be useful for future generation ground-motion prediction equations on deformation demands that are of great importance in performance-based earthquake engineering.

A percussion performance by a skilled musician will often extend beyond a written score in terms of expressiveness. This assertion is clearly evident when comparing a human performance with one that has been rendered by some form of automaton that expressly follows a transcription. Although music notation enforces a significant set of constraints, it is the responsibility of the performer to interpret the piece and “bring it to life” in the context of the composition, style, and perhaps with a historical perspective. In this sense, the sheet music serves as a general guideline upon which to build a credible performance that can carry with it a myriad of subtle nuances. Variations in such attributes as timing, dynamics, and timbre all contribute to the quality of the performance that will make it unique within a population of musicians. The ultimate goal of this research is to gain a greater understanding of these subtle nuances, while simultaneously developing a set of stochastic motion models that can similarly approximate minute variations in multiple dimensions on a purpose-built robot. Live or recorded motion data, and algorithmic models will drive an articulated robust multi-axis mechatronic system that can render a unique and audibly pleasing performance that is comparable to its human counterpart using the same percussion instruments. By utilizing a non-invasive and flexible design, the robot can use any type of drum along with different types of striking implements to achieve an acoustic richness that would be hard if not impossible to capture by sampling or sound synthesis. The flow of this thesis will follow the course of this research by introducing high-level topics and providing an overview of related work. Next, a systematic method for gesture acquisition of a set of well-defined percussion scores will be introduced, followed by an analysis that will be used to derive a set of requirements for motion control and its associated electromechanical subsystems. A detailed multidiscipline engineering effort will be described that culminates in a robotic platform design within which the stochastic motion models can be utilized. An analysis will be performed to evaluate the characteristics of the robotic renderings when compared to human reference performances. Finally, this thesis will conclude by highlighting a set of contributions as well as topics that can be pursued in the future to advance percussion robotics. / Graduate / 2019-12-10