dynamic interaction

The seismic performance of non-structural components (NSCs) has been the focus of intensive study during the last few decades. Modern building codes define design forces on components using too simple relationships. The component accelerates faster than the floor acceleration to which it is connected. Therefore, component dynamic amplification factors (CDAFs) are calculated in this work to quantify the amplification in the acceleration of NSCs for the various damping ratios and tuning ratios of the NSC, and the primary structural periods. From the analysis results, it was observed that CDAF peaks are either underestimated or overestimated by the code-based formulae. A prediction model to ascertain the CDAFs was also developed using artificial neural networks (ANNs). Following that, the suggested model is contrasted with the established relationships from the past research. The ANN model's coefficient of correlation ( ) was 0.97. Hence, using an ANN algorithm reduces the necessity of laborious and complex analysis.

Hippotherapy as a treatment modality relies on patient-equine dynamic interaction to enhance physical abilities in a range of neuromuscular diseases. The modality takes advantage of external stimulations in the form of kinetic and kinematic inputs to patient upper body. Current practices and procedures could be greatly enhanced by an objective approach to session planning based on a predictive neuromuscular model. Individualization of the treatment program is both subject-specific and equine-specific.To this effect, kinesiological aspects of the three main upper body flexor-extensor muscles which are directly affected by this treatment modality are presented in a biomechanical model. Events and phases of this dynamic interaction are identified and described using a phase plane analysis. Physical interpretations of coefficients in the movement differential equation illustrates that the proposed approach and mathematical modeling have the potential to be tailored for various musculoskeletal or neuromuscular disorders. Validation results show that the model has the ability to simulate kinematic response and muscle forces of the patient upper body during a hippotherapy session. This predictive ability could provide the therapist with a tool to estimate the effects prior to therapy sessions and choose the most suitable combination of horse and exercises.