An ontology is built to automate information searching in the bridge rehabilitation stage.•
The ontology focuses on information of project constraints, tasks, and participants.•
SWRL, SWQRL, and OWL API are combined to address syntax limitations in traditional ontologies.•
The ontology can effectively integrate both static and dynamic project information.•
The ontology also supports essential project management functions.
Concrete bridges are critical infrastructures, which require effective rehabilitation to maintain a good condition. Bridge rehabilitation projects have complex constraints and multiple participants. Constraint management is critical for such projects. Integrating and searching for relevant information is a key step for constraint management to timely remove constraints. However, accessing project information still relies on manual searching, which can delay information flow in constraint management and hinder constraint removal. Thus, this study introduces the concrete bridge rehabilitation project management ontology (CBRPMO) to improve information integration and constraint management. The CBRPMO was created by comprehensively collecting domain knowledge of bridge rehabilitation and following standard procedures. Reasoning rules were combined with an application programming interface (i.e. OWL API) to enable functions not supported in traditional ontologies (e.g. temporal computation and dynamic updating). As such, the CBRPMO can effectively handle dynamic information in ongoing projects. The CBRPMO was validated in a case study. The results show that the CBRPMO can: 1) integrate project information of constraints, tasks and procedures, participants, and relations between these project entities; 2) support various management functions based on dynamic project information, including evaluating project progress, constraint removal, and participants’ performance. The CBRPMO contributes to the industry by: 1) extending the application of ontologies in the bridge sector to cover the rehabilitation stage; 2) enhancing functions of conventional ontologies; and 3) reducing information searching time compared to manual searching, which improves constraint management approaches by automating the information searching step.