Linked-data based dynamic constraint solving framework to support look-ahead-planning in construction

This paper presents a framework for solving dynamic constraints to aid the automation of look ahead planning. The constraints in construction processes are highly dynamic and complex with numerous interdependencies, making it difficult to be modelled using relational databases or similar informational models. Recent research has used linked-data based modelling approaches to address this issue. Linked-data enables linking of data across domains and heterogeneous sources, without being limited by the underlying schema. This addresses the problem of complexities and interdependencies in construction constraints. Although there is extensive research done on linked-data applications to address the limitations of current information modelling approaches in the constructions sector, the focus has been on the modelling of product information. Applications of linked-data to address the limitations in information models for representing process information such as constraints relationships and resource dependencies are relatively less explored. Building on the research on linked-data in the construction sector, this paper presents a framework for dynamic constraint solving using linked-data and Shapes Query Language (SHACL) to address this limitation. The process information is modelled in Resource Description Framework (RDF) on top of the ifcOWL ontology, and constraints are modelled in SHACL. A prototype is developed based on the framework presented, which uses a python server to process the constraints modelled in SHACL. The prototype validates the process information in the model, against the modelled constraints to check whether a schedule is feasible or not. This framework can be used to automate look-ahead-planning in construction.