Mohammad Javad Rahimdel - Department of Mining Engineering, Sahand University of Technology, Tabriz, Iran
Mehdi Mirzaei - Department of Mechanical Engineering, Sahand University of Technology, Tabriz, Iran
Javad Sattarvand - Department of Mining Engineering, Sahand University of Technology, Tabriz, Iran
Behzad Ghodrati - Division of Operation and Maintenance Engineering, Lulea University of Technology, Lulea, Sweden

Drivers of mining trucks are exposed to whole-body vibrations (WBV) and shocks during the various working cycles. These exposures have an adversely influence on the health, comfort and also working efficiency ofdrivers. Determination and prediction of the vibrational health risk of the mining haul trucks at the various operational conditions is the main goal of this study. To this aim, three haul roads with low, medium and poorqualities are considered based on the ISO 8608 standard. Accordingly, the vibration of a mining truck in different speeds, weights and distribution qualities of the materials in the dump body are evaluated for each haul roadquality using the Trucksim software. An artificial neural network (ANN) is used to predict the vibrational health risk. The obtained results indicate that the haul road qualities, the truck speeds and the accumulation sides ofmaterial in the truck dump body have significant effects on the root mean square (RMS) of vertical vibrations. However, there is no significant relation between the material’s weight and the RMS values. Also, theapplication of ANN revealed that there is a good correlation between the predicted and simulated RMS values. The performance of the proposedneural network to predict the moderate and high health risk are 88.11% and 93.93% respectively.

Mining trucks,Health risk,Whole body vibration,Artificial neural network,