Mining Observation and Cognitive Behavior Process Patterns of Bridge Inspector

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

Sharing Construction Safety Inspection Experiences and Site-Specific Knowledge through XR-Augmented Visual Assistance

Early identification of on-site hazards is crucial for accident prevention in the construction industry. Currently, the construction industry relies on experienced safety advisors (SAs) to identify site hazards and generate mitigation measures to guide field workers. However, more than half of the site hazards remain unrecognized due to the lack of field experience or site-specific knowledge of some SAs. To address these limitations, this study proposed an Extended Reality (XR)-augmented visual assistance framework, including Virtual Reality (VR) and Augmented Reality (AR), that enables capturing and transferring subconscious inspection strategies between workers or workers/machines for a construction safety inspection. The purpose is to enhance SA's training and real-time situational awareness for identifying on-site hazards while reducing their mental workloads

Quantifying the reliability of defects located by bridge inspectors through human observation behavioral analysis

Distinct site conditions and individual expertise contribute to the subjective nature of bridge inspection processes, which involve uncertain human factors. Assessing inspection reliability can be achieved by examining inspectors' behaviors that lead to inaccurately identified or overlooked defects. However, the scarcity of comprehensive behavioral data regarding defect observation poses a challenge in evaluating inspection consistency. This paper investigates observation behaviors in bridge inspection to quantify the reliability of structural defect localizations. We employ defect inspection strategies correlated with more dependable defect localization records to construct a behavioral process graph that quantifies inspectors' performance and predicts their “inspection reliability index.” The generated reliability index for inspectors serves as a weighting factor to emphasize the opinions of more reliable inspectors when consolidating inspection records. The findings reveal that aggregating the inspection records of 96 human subjects based on their reliability indices effectively filters out false alarms while retaining reliable defect records