Stage-Sensitive Risk Structure Analysis in Construction Digital Transformation: An Unsupervised Learning-Enhanced DEMATEL–ISM Framework

Digital transformation projects in the construction sector are usually implemented through staged processes involving changing technical conditions, organizational priorities and expert participation. Existing risk assessment studies are often based on static or single-round models, thus limiting their ability to support structural comparisons when assessments are repeated under changing project conditions. To address this issue, this study proposes an unsupervised learning-enhanced DEMATEL–ISM framework for stage-sensitive risk structure analysis in construction digital transformation. DEMATEL and ISM are used to identify causal roles and hierarchical relationships among risk factors within each assessment round, while K-means clustering and principal component analysis are introduced to extract historical relational patterns and incorporate them into subsequent structural modeling. The framework is applied to a digital transformation project in a large construction enterprise using a two-round expert assessment with partial panel continuity. The results show that the baseline structure is mainly driven by tangible resources and strategic planning, whereas the follow-up structure places greater emphasis on data management and intangible organizational capabilities. Comparative and robustness analyses further indicate that the main structural interpretation is not driven by the enhancement layer, threshold selection, panel reduction or individual expert judgement. This study offers a decision-support approach for updating risk structures across assessment rounds and for adjusting risk governance as construction digital transformation progresses.