FORSEES is project dedicated to predict (or to foresee) forces flux on simple structural arrangements only by visual analysis. Amongst human senses, vision is the most developed one, producing a wealth of information for human cognition. We decided to capitalize on visual data to develop a Computer Vision (CV) tool for Structural Engineering (SE) applications to fill the diagnostic gap in the sector with other fields of study (like medicine) and to exploit its educational potentials. The project has been tackled by multiple angles to answer to fundamental questions and to identify standalone scientific advancements: 1) does exist or can be developed a human structural intuition (a sense that identify equilibrium in a fast thinking scheme)? and 2) can it be used to interface modern CV diagnostic system for SE assessments? Finally, 3) could a system that identify and visualize forces in real time be helpful for teaching purposes and enhance equilibrium perception in engineering education? The project started by merging the state of the art on visual analyses in structural applications in a CV framework and then expanded it to a Neuroscience approach. For example, Rapid Visual Screening (RSV) is a methodology widely used in engineering for seismic vulnerability assessment of the building stock that requires squadron of engineers deployed into an area to fill a survey with building features relevant for earthquake behavior. In collaboration with Senseable City Lab (SCL), we were able to replicate RSV results and exploit the power of CV analyses, using street level imagery (i.e. Google Street View) to assess seismic vulnerability of different world. We were also able to identify what features trained engineers and general public find more relevant for safety perception. For a deeper analyses in fundamentals of structural mechanics, we investigated the possibility to reconcile methods like photoelasticity or graphic statics with CV methods and cognition. We researched the analogy with the Free Energy Principle (FEP), that defines the cognitive set of mind+brain as an inference engine, since it can be modeled through a variational equation formally identical to that of the elastic potential of solids. By limiting the equation to the visual sense only, we aimed to formalize an equivalence between the terms of entropy, divergence, surprise, energy and similarity to the FEP terms. To validate this, problems known to SE have been used to reconstruct and identify forces fluxes, altogether with strain and stresses identification.
