Off-Road Vehicle Seat Suspension Optimisation, Part I: Derivation of an Artificial Neural Network Model to Predict Seated Human Spine Acceleration in Vertical Vibration. Issue 4 (December 2014)

• Record Type: Journal Article
• Authors: Gohari, M.; Rahman, R.A.; Tahmasebi, M.; Nejat, P.
• Is Part Of: Journal of low frequency noise, vibration, and active control. Volume 33:Issue 4(2014)
• Page Start: 429
• Page End: 441
• Publication Date: 2014-12
• Subjects: Artificial neural network -- biodynamic model -- spine responses to vibration -- seated whole body vibration -- low frequency -- seat to spine vibration transmissibility; Vibration -- Periodicals; Noise -- Periodicals; Sound -- Periodicals; Damping (Mechanics) -- Periodicals; Damping (Mechanics); Noise; Sound; Vibration; Periodicals; 620.205
• DOI: 10.1260/0263-0923.33.4.429
• Languages: English
• ISSNs: 1461-3484

Abstract

Whole body vibration produces some serious problems for human health in the long term. Low-frequency vibration, generated during vehicle operation, and transmitted to the vehicle operator, plays a major role in the development of low-back pain. Back pain is one of epidemic injuries in heavy duty vehicle drivers. Generally seat suspensions are designed and optimised to remove this unwanted movement. Human body biodynamic model is essential in passive seat suspension optimisation and active control seat suspension design. Lumped parameter models have been used by researchers for this purpose, but they have some limitations such as fixed body weight. With reference to this limitation, in first part of this paper a new artificial neural network (ANN) model is introduced which can predict spine acceleration from excitation signal and human body mass and height. The accuracy of model is 96% and makes it useful in real-time and off-line analysis. In second part of the paper, an off-road seat suspension will be optimised via this achieved ANN model and three Meta-Heuristic algorithms.