Predicting whole-body vibration (WBV) exposure of Malaysian Army three-tonne truck drivers using Integrated Kurtosis-Based Algorithm for Z-Notch Filter Technique 3D (I-kaz 3D)

• I-kaz 3D is developed to monitor whole-body vibration (WBV) in the driver's seat.
• I-kaz 3D coefficient (Z3D∞) is generated to represent WBV from the driver's seat.
• The dirt road with a rough surface has a higher value of IRIavg than the tarmac road with a soft surface.
• The developed models for Z3D∞ and VDV(8) have high R2 value for both types of roads.
• Predicted WBV exposures generated by the regression models were close to the measured WBV exposures.

The objective of this study is to present a new method for determination of whole-body vibration (WBV) exposure in the driver's seat of Malaysian Army (MA) three-tonne trucks based on changing vehicle speed using regression models and the statistical analysis method known as Integrated Kurtosis-Based Algorithm for Z-Notch Filter Technique 3D (I-kaz 3D). The study was conducted on two different road conditions; tarmac and dirt roads. WBV exposure was measured using a Brüel & Kjær Type 3649 vibration analyser, which is capable to record WBV exposures from the driver seat and vibration from the truck, and comparisons were made between the two types of roads. The data was analysed using I-kaz 3D to determine the WBV values in relation to varying speeds of the truck and to determine the degree of data scattering for WBV data signals. Based on the results obtained, WBV exposure levels can be presented using frequency weighted root mean square (RMS) accelerations (aw), vibration dose value equivalent to 8 h (VDV(8)  ), I-kaz 3D coefficient (Z3D∞) and the I-kaz 3D display. The I-kaz 3D displays showed greater scatterings, indicating that the values of Z3D∞ and VDV(8)   were getting higher. The prediction of WBV exposure was done using the developed regression models and graphical representations of Z3D∞. The results of the regression models showed that Z3D∞ increased when vehicle speed and WBV exposure increased. For model validation, predicted and measured noise exposures were compared, with high coefficient of correlation (R2) values obtained, indicating that a good agreement was obtained between them. By using the developed regression models, we can easily predict WBV exposure in the driver's seat for WBV exposure monitoring.