Mining Observation and Cognitive Behavior Process Patterns of Bridge Inspector

TL;DR

This paper develops a computational framework to analyze and model the observation and cognitive behaviors of bridge inspectors, aiming to improve inspection reliability and facilitate inspector training through behavioral pattern mining.

Contribution

It introduces a novel game-based data collection method and reveals reusable behavioral process patterns for bridge defect diagnosis, enhancing understanding of inspector decision-making.

Findings

Behavioral process patterns correlate with defect types and diagnosis accuracy

The framework improves inspection process explainability and reliability

Proactive sharing of inspection experiences is feasible through behavioral analysis

Abstract

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.