A Predictive Statistical Model for Shoe-Floor-Fluid Coefficient of Friction

Details

• Personal Author:
  Beschorner KE ; Vidic N
• Description:
  Slip and fall accidents are a major and growing source of occupational injuries. Slip-resistant shoes with a high coefficient of friction (COF) are effective at a reducing slipping risk. However, neither experts nor industry has agreed upon a consistent set of criteria for labeling a shoe as slip-resistant. A consequence of this lack of standardization is that significant variability exists across shoes that are labeled slip-resistant. Furthermore, independent testing of shoe COF is expensive, which may limit its use by employers and employees. The proposed research aims to address this problem by developing a predictive model for shoe-floor-contaminant COF based on shoe parameters that can be measured with little cost. The overall objective of this R03 study is to train and validate a statistical model for predicting the COF of footwear against a floor surface in the presence of a liquid contaminant. Aim 1: Using mechanical friction experiments, we characterized the friction response of 73 shoes including 58 slip-resistant shoe designs and 15 non-slip-resistant shoe designs. A prediction model was developed that predicted 88% of the variability in friction performance across slip-resistant shoes based on the tread surface area, shoe beveling, and hardness, while controlling for the flooring. This model did not apply to non-slip-resistant shoes due to the presence of fluid pressures. Thus, strategies to improve the friction performance of slip-resistant shoes may consider the design parameters utilized in this study, while strategies to improve friction performance of non-slip-resistant shoes should prioritize improving fluid drainage. Safety managers intending to use this model should be aware that it only applies to slip-resistant shoes. Aim 2: The model was validated based on 38 human slips in oily conditions. The model was found to sensitively predict whether a participant would experience a slip. Thus, the model appeared to provide friction predictions that are relevant to human safety. This study led to numerous outputs and outcomes. The work was disseminated by 1 published peer-reviewed paper (1 more in review and 2 in preparation) and several presentations that targeted the scientific, footwear producer, and safety professional communities. [Description provided by NIOSH]
• Subjects:
  Accidental Falls Accidents Occupational Health Safety
• Keywords:
  Equipment Design Floors Footwear Shoe Manufacturing Slips, Trips, And Falls Statistical Analysis STF Walking Surfaces
• Series:
  Grant Final Reports
• Publisher:
  National Institute for Occupational Safety and Health
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Final Grant Report
• Place as Subject:
  OSHA Region 3 ; Pennsylvania
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Division:
  OEP - Office of Extramural Programs
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  1-59
• NIOSHTIC Number:
  nn:20066128
• Citation:
  Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, R03-OH-011069, 2021 Nov; :1-59
• Contact Point Address:
  Kurt E. Beschorner, Ph.D., University of Pittsburgh, Swanson School of Engineering, 4420 Bayard St #306, Pittsburgh, PA 15213
• Email:
  beschorn@pitt.edu
• Federal Fiscal Year:
  2022
• NORA Priority Area:
  Wholesale and Retail Trade
• Performing Organization:
  University of Pittsburgh at Pittsburgh
• Peer Reviewed:
  False
• Start Date:
  20180901
• Source Full Name:
  National Institute for Occupational Safety and Health
• End Date:
  20200831
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:f5cfff58a240eacc706a5d1af0000ecba29fe807ef3f914266729c78ba71ab3d7ac45560cd79ef8886eaa08af90a66aa18727602cad88ea7fce2cd4729c0d206
• Download URL:
  https://stacks.cdc.gov/view/cdc/219142/cdc_219142_DS1.pdf
• File Type:
  [PDF - 4.42 MB ]