Development of Artificial Neural Network Based Model for Assessing the Whole-Body Vibration exposure [Dataset]
This dataset is part of a study that proposes a methodology to facilitate the application of individual long-term whole-body vibration (WBV) exposure assessment models. The proposed methodology can be applied to a wide variety of activities that expose vehicle drivers to WBV in the construction sect...
| Authors: | , , , , , |
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| Format: | conjunto de datos |
| Publication Date: | 2024 |
| Country: | España |
| Institution: | Universidad de Sevilla (US) |
| Repository: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/158149 |
| Online Access: | https://hdl.handle.net/11441/158149 https://doi.org/10.12795/11441/158149 |
| Access Level: | Open access |
| Keyword: | WBV whole-body vibration construction artificial neural network long-term assessment safety management workers’ health VCE Vibraciones transmitidas a cuerpo completo construcción red neuronal artificial evaluación a largo plazo gestión de la seguridad salud de los trabajadores |
| Summary: | This dataset is part of a study that proposes a methodology to facilitate the application of individual long-term whole-body vibration (WBV) exposure assessment models. The proposed methodology can be applied to a wide variety of activities that expose vehicle drivers to WBV in the construction sector (such as demolition tasks, earth moving or material transport). For this purpose, a driver with extensive experience in driving heavy equipment vehicles was selected and a measurement campaign was conducted. During the measurement campaign, typical activities were assessed, and the magnitude of the transmitted acceleration was characterized and analyzed. The collected data were processed and integrated into a database. |
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