Visual bridge inspection is a knowledge-intensive task in which inspectors coordinate visual search, spatial navigation, structural reasoning, and defect identification and documentation. It is a central maintenance task for bridges and a key basis for safety assessments, yet its results are susceptible to individual subjectivity. While eye-tracking-based behavioral studies quantify underlying processes, existing research often imposes restrictive simplifications to reduce environmental complexity, thereby compromising ecological validity. This study proposes an automated data analytics framework for converting multimodal inspection data into an inspection mode time series. Unconstrained 3D gaze, head-movement, drone navigation, and scene geometry data are segmented into temporal windows and classified into three functional modes: global scanning, local inspection, and navigation. The resulting temporal representation enables the extraction of interpretable behavioral descriptors, including transition probabilities, dwell times, transition entropy, fixation measures, and spatial revisit metrics. A feasibility study using a virtual bridge inspection platform demonstrates that the proposed representation captures meaningful differences in inspection strategy and reveals exploratory relationships with inspection performance. This study contributes a framework for human-informed computer-aided infrastructure inspection systems, inspector training, and data-driven assessment of constructed facilities.

翻译：视觉桥梁检测是一项知识密集型任务，检测人员需协调视觉搜索、空间导航、结构推理以及缺陷识别与记录。它是桥梁维护的核心任务及安全评估的关键基础，但其结果易受个体主观影响。尽管基于眼动追踪的行为研究可量化潜在过程，但现有研究常通过施加限制性简化来降低环境复杂度，从而损害生态效度。本研究提出一种自动化数据分析框架，用于将多模态检测数据转换为检测模式时间序列。无约束三维注视、头部运动、无人机导航及场景几何数据被分割为时间窗口，并分类为三种功能模式：全局扫描、局部检测与导航。所得时序表征能够提取可解释的行为描述指标，包括转移概率、驻留时间、转移熵、注视度量及空间重访指标。基于虚拟桥梁检测平台的可行性研究表明，该表征方法能捕捉检测策略中的有意义差异，并揭示其与检测绩效之间的探索性关联。本研究为基于人类认知的计算机辅助基础设施检测系统、检测人员培训及建成设施数据驱动评估提供了框架支持。