Computational Model for Personal Fall Arrest Systems

Objective:

To develop a comprehensive computational model to replicate personal fall arrest systems, with the goal of analyzing and improving their safety performance and reducing the need for expensive and time-consuming physical experiments.

Approach:

Built the model based on experimental test results, rigid body dynamic simulations, and a reverse engineering approach using an optimization algorithm.
Conducted an in-depth analysis to identify failure modes in current fall arrest system designs and evaluated the shortcomings in existing industry standards.
Based on the analysis, proposed critical design improvements aimed at significantly enhancing the safety and reliability of personal fall arrest systems.
While not replacing physical testing, this model helps reduce the number of required tests, offering a more efficient path to validation without compromising safety standards.

Outcome:

The findings and recommendations have the potential to influence new industry standards, fostering innovation and increasing the effectiveness of personal fall protection solutions while optimizing the testing process.