Interpretation of historic structure for non-invasive assessment using eye tracking

With the aims of ensuring safety and decreasing maintenance costs, previous studies in bridge inspection research have worked to elucidate damage indicators and understand their correspondence to structural deficiency. During this process, understanding how an inspector looks at a structure comprehensively as well as how they localize on damage is vital to examining diagnostic bias and how it can play a role in the preservation and maintenance process. To understand human perception and assess the humaninfrastructure interaction during the feature extraction process, eye tracking can be useful. Eye tracking data can accurately map where a human is looking and what they are focusing on based on metrics such as fixation, saccade, pupil dilation, and scan path. The present research highlights the use of eye tracking metrics for recognizing and inferring human implicit attention and intention while performing a structural inspection. These metrics will be used to learn the behavior of human eyes and how detection tasks can change a person's overall behavior. A preliminary study has been carried out for damage detection to analyze key features that are important for understanding human-infrastructure interaction during damage assessment. These eye tracking features will lay the foundation for human intent prediction and how an inspector performs inspection on historic structures for existing types of damage. In future, the results of this work will be used to train a machine learning agent for autonomous and reactive decision making.