A Computational Tool to Automate the Analysis of Stress-Strain Data from Tensile Tests of Soft Tissue
Trevor J. Lujan
Stress-strain curves are commonly generated to analyze the mechanical tensile behavior of biological materials and evaluate several key points. There is no standard method to find the transition point. Additionally, there is a need for a user-friendly program that automates the calculation of mechanical properties from stress-strain curves.
A custom graphical user interface (GUI) was created using Python, allowing users to upload stress-strain data where it automatically generates a stress-strain curve with marked points of interest, as well as a .csv file that appends those points. The software's accuracy was evaluated using tensile test data from our previous study on human meniscus and synthetic stress-strain curves with known transition points generated using FEBio studio.
The GUI closely predicted the same transition point calculated using a more complex FE optimization routine. We found that a 3% slope deviation returned the lowest error for transition stress in this study. The error in calculating the transition point was insensitive to changes in the modulus and damage rate of the stress-strain curve. In conclusion, by creating an accurate computational tool to automate the analysis of stress-strain curves, this study can advance the standardization of biomechanical testing in soft fibrous tissue.
Nelson, Miranda (2021) A computational tool - URS video.srt (5 kB)
Nelson, Miranda (2021) A computational tool - transcript.pdf (14 kB)
Nelson, Miranda L.; Nesbitt, Derek Q.; and Lujan, Trevor J., "A Computational Tool to Automate the Analysis of Stress-Strain Data from Tensile Tests of Soft Tissue" (2021). 2021 Undergraduate Research Showcase. 4.