Degree
Doctor of Philosophy
Program
Kinesiology
Supervisor
James P. Dickey
Abstract
Whole-body vibration describes vibrations that are transferred from a supporting surface to the human body. Low back injury is a major health issue amongst heavy machine operators and seat selection is important for reducing vibration exposure. Modeling the vibration attenuation properties of seats is one approach for predicting the performance of seats in different vibration environments. An efficient neural network (NN) algorithm identified the vibration attenuation properties of five suspension seats that are commonly used in the Northern Ontario mining sector. Each of the NN seat models strongly predicted vertical seatpan r.m.s. accelerations from the chassis accelerations and a measure of driver anthropometrics. We implemented the developed NN models to evaluate the performance of industrial seats for a variety of skidders from the forestry sector and load-haul-dump vehicles from the underground mining environment. Our results demonstrated that seat selection is not universal. The performance and rank orders of industrial seats varied between vibration environments based on the calculated equivalent daily exposure (A(8)) values. We performed a sensitivity analysis to evaluate the influence of specific vibration frequency components on the predicted daily exposure values. This analysis revealed that each of the industrial seats responded differently to specific vibration frequencies and explained why the seat selection algorithm matched particular seats to specific vibration environments. We also evaluated the performance of the new No-JoltTM air-inflated cushion with multi-axis vibration exposures and vertical jolt exposures. The vibration attenuation properties were assessed for two seat suspensions (with relatively good and poor initial performance) when their foam cushions were replaced with the air-inflated cushion. The air cushion only improved the vibration attenuation properties of the seat that initially had good performance. We also observed that operator’s anthropometrics and sex influenced the performance of the air-inflated cushion in certain cases when vibration environment includes jolt exposures. All of our findings emphasize the importance of matching the specific seat/cushion to the particular vibration environment in order to reduce heavy machine operators’ vibration exposure and minimize their health risks.
Recommended Citation
Ji, Xiaoxu, "Evaluation of Suspension Seats Under Multi-Axis Vibration Excitations - A Neural Net Model Approach to Seat Selection" (2015). Electronic Thesis and Dissertation Repository. 3095.
https://ir.lib.uwo.ca/etd/3095