# Reducing slip-and-fall accidents in the workplace: Role of small-scale roughness of floor surfaces to improve friction > **NIH ALLCDC R21** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2022 · $203,963 ## Abstract Project Summary Fall-related injuries burden over 140,000 workers annually, causing significant human suffering and an economic cost of $10 billion in Workers' Compensation. Approximately half of occupational falls are caused by slipping. An under-explored pathway to preventing these slip-and-fall events is to design flooring for workplaces with high friction performance. High-friction flooring prevents the slip events that lead to a fall. Unfortunately, current methods to characterize floor-surface topography are unable to predict friction performance, limiting innovation in this area. In order to catalyze innovation in high-friction flooring, there is a need for improved scientific understanding of the flooring factors that contribute to friction. Our preliminary studies and existing literature suggest that small-scale topography (features at the 1-nm to 1-µm scale) is critical for predicting floor performance, but is not measurable using conventional characterization techniques. The purpose of this R21 project is to measure these small-scales of floor-surface topography, and to use them to develop a mechanics-based predictive model for friction. This research is innovative because it will employ novel experimental methods and analysis techniques that have never been applied to flooring surfaces, and because it will develop a mechanics-based model to predict the relationship between floor structure and friction performance, where prior research has relied solely on empirical correlations. The proposed research will be accomplished through two Aims: Aim 1: Quantify the dependence of shoe-floor friction performance on small-scale topography. This Aim will investigate the ability of small-scale topography to explain variations in shoe-floor friction performance that cannot be explained using current measurement techniques. Then we will test the first hypothesis: Hypothesis 1: Roughness parameters that consider the full range of scales will improve our ability to predict COF values compared with those using just stylus profilometry. Aim 2: Establish a predictive mechanics-based model for shoe-floor friction based on multiscale surface topography. In this Aim, we will develop and validate a multiscale finite element model that captures viscoelastic contributions to friction across all length scales. We will test the second hypothesis: Hypothesis 2: A mechanics-based model using multiscale topography will more accurately predict shoe-floor friction compared with conventional approaches, i.e., statistical models based on stylus profilometry. This research is expected to lead to foundational knowledge and a modeling tool for optimizing high-friction flooring in workplaces. Working with an industry trade group, the Tile Council of North America (TCNA), this research will achieve impact by guiding the evidence-based development of high-friction flooring for workplaces. Thus, the proposed research is expected to achieve impact in improving workplace safet... ## Key facts - **NIH application ID:** 10556441 - **Project number:** 5R21OH012126-02 - **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH - **Principal Investigator:** Kurt E Beschorner - **Activity code:** R21 (R01, R21, SBIR, etc.) - **Funding institute:** ALLCDC - **Fiscal year:** 2022 - **Award amount:** $203,963 - **Award type:** 5 - **Project period:** 2021-09-30 → 2023-09-29 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10556441 ## Citation > US National Institutes of Health, RePORTER application 10556441, Reducing slip-and-fall accidents in the workplace: Role of small-scale roughness of floor surfaces to improve friction (5R21OH012126-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10556441. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*