Automated knowledge graphs for complex systems (AutoGraCS): Applications to management of bridge networks

With the ability to harness the power of big data, the digital twin (DT) technology has been increasingly applied to the modeling and management of structures and infrastructure systems, such as buildings, bridges, and power distribution systems. Supporting these applications, an important family of methods are based on graphs. For DT applications in modeling and managing smart cities, large-scale knowledge graphs (KGs) are necessary to represent the complex interdependencies and model the urban infrastructure as a system of systems. To this end, this paper develops a conceptual framework: Auto mated knowledge Gra phs for C omplex S ystems (AutoGraCS). In contrast to existing KGs developed for DTs, AutoGraCS can support KGs to account for interdependencies and statistical correlations across complex systems. The established KGs from AutoGraCS can then be easily turned into Bayesian networks for probabilistic modeling, Bayesian analysis, and adaptive decision supports. Besides, AutoGraCS provides flexibility in support of users’ need to implement the ontology and rules when constructing the KG. With the user-defined ontology and rules, AutoGraCS can automatically generate a KG to represent a complex system consisting of multiple systems. The bridge network in Miami-Dade County, FL is used as an illustrative example to generate a KG that integrates multiple layers of data from the bridge network, traffic monitoring facilities, and flood water watch stations.