Artificial Neural Network-Based Model for Assessing the Whole-Body Vibration of Vehicle Drivers

Abstract

Musculoskeletal disorders, which are epidemiologically related to exposure to whole-body vibration (WBV), are frequently self-reported by workers in the construction sector. Several activities during building construction and demolition expose workers to this physical agent. Directive 2002/44/CE defined a method of assessing WBV exposure that was limited to an eight-hour working day, and did not consider the cumulative and long-term effects on the health of drivers. This study aims to propose a methodology for generating individualised models for vehicle drivers exposed to WBV that are easy to implement by companies, to ensure that the health of workers is not compromised in the short or long term. A measurement campaign was conducted with a professional driver, and the collected data were used to formulate six artificial neural networks to predict the daily compressive dose on the lumbar spine and to assess the short- and long-term WBV exposure. Accurate results were obtained from the developed artificial neural network models, with R2 values above 0.90 for training, cross-validation, and testing. The approach proposed in this study offers a new tool that can be applied in the assessment of short- and long-term WBV to ensure that workers’ health is not compromised during their working life and subsequent retirement.

Keywords

• WBV
• occupational vibration
• construction
• artificial neural network
• long-term assessment
• safety management