GIS-based framework to manage Whole-Body Vibration exposure

Hoz‐Torres, María L. de la; Aguilar, Antonio J.; Ruíz, Diego P.; Martínez‐Aires, María Dolores

doi:10.1016/j.autcon.2021.103885

Cited by 9 publications

(4 citation statements)

References 43 publications

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“…In this context, some researchers have used new approaches to generate models and frameworks to reduce WBV exposure to driving operations. De la Hoz-Torres et al [43] proposed a GIS-based methodological framework to manage and reduce the exposure to WBV using the least-cost path algorithm for the process of route design. Upadhyay et al [44] used a statistical approach to reduce WBV exposure among dumper operators.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Aguilar,

de la Hoz-Torres,

Martínez-Aires

et al. 2024

Buildings

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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.

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Section: Literature Reviewmentioning

confidence: 99%

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

Aguilar,

de la Hoz-Torres,

Martínez-Aires

et al. 2024

Buildings

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“…Mostly, these vibrations lie above the HGCZ limit of both A (8) and VDV (8). Therefore, vibrations reducing strategy is to be and Heyns, 2011;Chaudhary et al, 2015;de la hoz-torres et al, 2021a, 2021bKumar, 2004;Lynas and Burgess-Limerick, 2020;Mayton et al, 2018;Pollard et al, 2017;Smets et al, 2010;Wolfgang and Burgess-Limerick, 2014). Measured WBVexposure level is below HGCZ (Chi et al, 2017).…”

Section: Classification Of Wbv Magnitude Studies Across Various Indus...mentioning

confidence: 99%

Role of contributing factors on health risks of whole-body vibration exposure of heavy equipment and vehicle operators: A critical review

Singh

Palei

Karmakar

2023

Journal of Vibration and Control

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Whole-body vibration (WBV) is a recognized health hazard that primarily affects the musculoskeletal system of human beings. Although research works on WBV are documented, for the past few decades or so, researchers are still attempting to explore the role of contributing factors of WBV for the causation of musculoskeletal disorders (MSDs). Therefore, this paper synthesizes up-to-date knowledge on WBV with respect to measurement methods, critical vibration magnitudes, contributing factors, and effects on human health. The present review is based on PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyzes) that includes 112 studies on WBV exposure and its contributing factors. It also summarizes the contributing factors of MSDs that include personal factors, environmental factors, machine-related factors, and WBV exposure of the heavy vehicles and equipment operators across various industries. Most of the studies reported lower back pain as the most common MSDs experienced by the vehicle operators exposed to WBV, followed by upper back pain. However, it is apparent from the prevailing literature that musculoskeletal health risks do not result from exposure to WBV alone. The role of multivariate contributing factors should also be considered, analyzed, assessed, and understood to establish correlations between these factors with the development of MSDs. In addition to the measurement and assessment of WBV as per the ISO guidelines to evaluate the potential and likely health risks faced by the heavy equipment operators, researchers should also focus on studying various impacts of WBV, and understanding the role of its contributing factors on human health using advanced machine learning tools.

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“…The selected route was representative of the complete work cycle and comprises different types of surfaces. Previous studies have shown that the magnitude of WBV transmitted to the driver is influenced by the characteristics of the road surface [41][42][43]. The route layout and the different surfaces are shown in Figure 1.…”

Section: Measurement Of Wbv and Field Testmentioning

confidence: 99%

Whole Body Vibration Exposure Transmitted to Drivers of Heavy Equipment Vehicles: A Comparative Case According to the Short- and Long-Term Exposure Assessment Methodologies Defined in ISO 2631-1 and ISO 2631-5

Hoz‐Torres

Aguilar

Ruiz

et al. 2022

IJERPH

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The construction and transport sectors are the industries with the highest proportions of workers exposed to vibrations in the European Union. Heavy equipment vehicle (HEV) drivers often perform operations on different uneven surfaces and are exposed to whole body vibration (WBV) on a daily basis. Recently, a new version of ISO 2631-5 was published. However, since this new method required as input the individual exposure profile and the acceleration signals recorded on more surfaces, limited studies have been carried out to evaluate HEV operations according to this standard. The objectives of this study were to assess the WBV exposure using the methods defined in ISO 2631-1:1997 and ISO 2631-5:2018 and to compare the obtained health risk assessments between drivers with different anthropometric characteristics. For this purpose, two drivers were selected and a field measurement campaign was conducted. Regarding short-term assessment, results showed that VDV was the most restrictive method with exposure levels above the exposure action limit value, while SdA indicated that the same exposures were safe for the worker. With respect to long-term assessment, Risk Factor RA showed that the driver with the highest body mass index was the only one who exceeded the low probability limit of adverse health effects.

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GIS-based framework to manage Whole-Body Vibration exposure

Cited by 9 publications

References 43 publications

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

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

Role of contributing factors on health risks of whole-body vibration exposure of heavy equipment and vehicle operators: A critical review

Whole Body Vibration Exposure Transmitted to Drivers of Heavy Equipment Vehicles: A Comparative Case According to the Short- and Long-Term Exposure Assessment Methodologies Defined in ISO 2631-1 and ISO 2631-5

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