@misc{10481/93584,
year = {2024},
month = {6},
url = {https://hdl.handle.net/10481/93584},
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.},
organization = {project PID2019-108761RB-I00, funded by
MCIN/AEI/10.13039/501100011033},
organization = {Ministerio de Ciencia, Innovación y Universidades of Spain under a Margarita Salas
post-doctoral contract (MS2022-32) funded by the European Union-NextGenerationEU},
organization = {University of Granada under a post-doctoral
research contract},
publisher = {MDPI},
keywords = {WBV},
keywords = {occupational vibration},
keywords = {construction},
title = {Artificial Neural Network-Based Model for Assessing the Whole-Body Vibration of Vehicle Drivers},
doi = {10.3390/buildings14061713},
author = {Aguilar, Antonio J. and de la Hoz-Torres, María L. and Martínez Aires, María Dolores and Ruiz, Diego P. and Arezes, Pedro and Costa, Nélson},
}