A decision support system for urban infrastructure inter-asset management employing domain ontologies and qualitative uncertainty-based reasoning. (15th November 2020)

• Record Type: Journal Article
• Authors: Wei, Lijun; Du, Heshan; Mahesar, Quratul-ain; Al Ammari, Kareem; Magee, Derek R.; Clarke, Barry; Dimitrova, Vania; Gunn, David; Entwisle, David; Reeves, Helen; Cohn, Anthony G.
• Is Part Of: Expert systems with applications. Volume 158(2020)
• Publication Date: 2020-11-15
• Subjects: Smart cities -- Infrastructure maintenance -- Underground utilities -- Rule-based system -- Reasoning under uncertainty; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2020.113461
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A Decision Support System for urban infrastructure management is presented. The system handles the asset dependencies using a set of domain ontologies and rules. The system models both the uncertainty and incompleteness of data and knowledge. A web-based prototype has been developed and demonstrated to potential users. Abstract: Urban infrastructure assets (e.g. roads, water pipes) perform critical functions to the health and well-being of society. Although it has been widely recognised that different infrastructure assets are highly interconnected, infrastructure management in practice such as planning, installation and maintenance are often undertaken by different stakeholders without considering these dependencies due to the lack of relevant data and cross-domain knowledge, which may cause unexpected cascading social, economic and environmental effects. In this paper, we present a knowledge based decision support system for urban infrastructure inter-asset management. By considering various infrastructure assets (e.g. road, ground, cable), triggers (e.g. pipe leaking) and potential consequences (e.g. traffic disruption) as a holistic system, we model each sub-domain using a modular ontology and encapsulate the interdependence between them using a set of rules. Moreover, qualitative likelihood is assigned to each rule by domain experts (e.g. civil engineers) to encode the uncertainty of knowledge, and an inference engine is applied to predict the potential consequences of a given trigger with location specific data and the encoded rules. A web-based prototype system has been developed based on the above concept and demonstrated to a wide range of stakeholders. The system can assist in the process of decision making by aiding data collation and integration, as well as presenting potential consequences of possible triggers, advising on whether additional information is needed or suggesting ways of obtaining such information. The work shows an intelligent approach to integrate and process multi-source data to pioneer a novel way to aid a complex decision process with a high social impact.