Assessing the Structural Stability of Spider Webs Following Leg Autotomy via Machine Learning and Finite Element Analysis

Spider orb webs are highly optimized biological structures capable of dissipating significant kinetic energy from prey impacts. The construction of these complex geometries requires precise motor coordination, utilizing all eight of the spider's legs. However, leg autotomy (the loss of a limb) is a common survival mechanism in arachnids. In this study, we investigate the mechanical consequences of leg autotomy on the structural integrity of the webs they subsequently build. Utilizing high-resolution images of webs spun by spiders before and after a controlled leg removal treatment, we employ machine learning segmentation techniques to accurately extract and digitize the altered web geometries. These digitized networks are then imported into the finite element analysis (FEA) software Strand7 to simulate their mechanical response under external loading. By analyzing stress distribution, energy dissipation, and overall deformation, we compare the structural stability of the pre- and post-treatment webs. We will discuss the preliminary results of our FEA models, highlighting the extent to which the loss of a single appendage alters the web's mechanical resilience and damage tolerance. This work provides quantitative insights into the intersection of biological adaptation and structural mechanics.