Ranking-Supervised Adaptation of Vision-Language Models for Temporal Progress Ordering Toward Automated Construction Monitoring

Automated interpretation of construction images and videos is important for scalable site monitoring. However, construction-oriented vision-language model (VLM) studies have largely focused on frame-level interpretation, leaving temporal reasoning over evolving construction states insufficiently established. To address this gap, this study investigated temporal progress ordering as an explicit temporal-ordinal capability and proposed ConRank, a ranking-supervised VLM framework. ConRank learned pairwise earlier–later relationships and global chronological ordering from construction videos and evaluated whether the acquired ordinal knowledge transferred to progress description generation. Results showed that ranking supervision improved both local and sequence-level ordering, while the interaction between pairwise and global supervision revealed both transferable and competing learning signals. The ranking-adapted model also produced more temporally grounded progress descriptions. By formulating construction progress as a rankable visual process and linking local ordering, global sequence consistency, and open-ended generation, this study established temporal progress ordering as an intermediate capability connecting visual recognition with higher-level automated construction monitoring.