Application of artificial neural network model to human body vibrations in large haul trucks

Abstract

The working conditions in oil sand mines in Northern Alberta, Canada, are greatly affected by the climate and geology. The ground is very hard and competent in winter but very soft during summer [1]. This changing behaviour of the ground has a great impact on the truck’s frame and the health of the operator because he is exposed to large Whole Body Vibrations (WBV). When the human body is exposed to large WBV for prolonged periods, it begins to have chronic back problems resulting in diseases of the lumbar spine, disc degeneration and other pathological effects to the spine and skeletal structure [2]. Many of these health effects are irreversible and people suffering from WBV disorders can experience pains for the rest of their lives. Studies carried out to correlate WBV to health problems have resulted in the setting of standards with the help of various government agencies, physiologists and industry experts. The main WBV standards that the mining industry has to observe are those of International Standards Organization (ISO) and the British Standards Institute [2]. ISO 2631-1 uses a three dimensional coordinate system where the axes are orthogonal to each other as shown in Figure 1 [3]. The standards require that the measuring device (accelerometer) should be placed at a point where vibrations are entering the body. In this study the data was collected for the seat accelerations in X, Y and Z directions by installing the accelerometer on the seat pan of a CAT 797 haul truck. An Artificial Neural Network (ANN) model was developed to predict the seat vibrations in very large capacity haul trucks in the X and Y directions. The study was done to find if the vibrations in the truck and their effects on the operator can be correlated to the truck’s operational parameters like speed, payload and strut pressures.