  In bridge inspection, engineers should diagnose the observed bridge defects
by identifying the factors underlying those defects. Traditionally, engineers
search and organize structural condition-related information based on visual
inspections. Even following the same qualitative inspection standards,
experienced engineers tend to find the critical defects and predict the
underlying reasons more reliably than less experienced ones. Unique bridge and
site conditions, quality of available data, and personal skills and knowledge
collectively influence such a subjective nature of data-driven bridge
diagnosis. Unfortunately, the lack of detailed data about how experienced
engineers observe bridge defects and identify failure modes makes it hard to
comprehend what engineers' behaviors form the best practice of producing
reliable bridge inspection. Besides, even experienced engineers could sometimes
fail to notice critical defects, thereby producing inconsistent, conflicting
condition assessments. Therefore, a detailed cognitive behavior analysis of
bridge inspectors is critical for enabling a proactive inspector coaching
system that uses inspectors' behavior histories to complement personal
limitations. This paper presents a computational framework for revealing
engineers' observation and cognitive-behavioral processes to identify bridge
defects and produce diagnosis conclusions. The authors designed a bridge
inspection game consisting of FEM simulation data and inspection reports to
capture and analyze experienced and inexperienced engineers' diagnosis
behaviors. Mining these behavioral logs have revealed reusable behavioral
process patterns that map critical bridge defects and diagnosis conclusions.
The results indicate that the proposed method can proactively share inspection
experiences and improve inspection processes' explainability and reliability.
