3D asset management graph analytics for proactive maintenance: a spatio-temporal graph modeling approach

Effective management of infrastructure assets during the operation and maintenance (O&M) phase increasingly demands the integration of spatial and temporal data to enable data-driven decision-making. Yet, the current approach has managed these bidimensional data in isolation, creating data silos that hinder the ability to uncover patterns essential for proactive maintenance. Spatial data in this context are 3D city models that capture infrastructure assets as city objects, while temporal data are records of timestamped events throughout an asset’s lifecycle. Efficient graph modeling of these infrastructure assets and their historical events is capable of allowing graph-based analytics that look into event trends for a proactive maintenance approach. Therefore, this paper presents an approach that enriches the semantic information of infrastructure assets represented in 3D city models with temporal O&M data through a graph modeling approach. This resulting graph network of assets and O&M events has allowed graph analytics through an external network analysis package. We have introduced and compared centrality analysis based on the full graph and subgraph for a targeted analysis of critical assets. The findings demonstrate that while subgraph-based analyses introduce additional computational overhead, they produce more direct event-centric insights compared to analyses conducted on full graphs. The proposed spatio-temporal modeling framework unifies essential spatial and temporal dimensions, addressing the limitations of conventional asset management approaches that treat these data independently. 3D asset management is further enhanced through graph-based modeling to support proactive maintenance strategies that account for the dynamic interactions between spatial context and historical asset information. This work demonstrates the practical value of spatio-temporal graph modeling for enabling meaningful network analysis to support a proactive maintenance strategy.