This work was motivated by the premise that next-generation smart city systems will be enabled by widespread adoption of sensing and communication technologies deeply embedded within the physical urban domain. These technological advances (e.g., sensing, processing, and data transmission) are what makes smart city digital twins possible. This paper explores approaches and challenges in architecting and the operation of smart city digital twins. A smart city digital twin architecture is proposed that supports semantic knowledge representation and reasoning, working side by side with machine learning formalisms, to provide complementary and supportive roles in the collection and processing of data, identification of events, and automated decision-making. The semantic and machine learning sides of the proposed architecture are exercised on a problem involving simplified analysis of energy usage in buildings located in the Chicago Metropolitan Area.

Artificial intelligence is set to transform the next generation of intelligent buildings through the application of information and semantic data models and machine learning algorithms. Semantic data models enable the understanding of real-world data for building automation, integration and control applications. This article explored the use of semantic models, a subfield of artificial intelligence, for knowledge representation and reasoning (KRR) across a wide variety of applications in building control, automation and analytics. These KRR-enabled applications include context-aware control of mechanical systems, building energy auditing and commissioning, indoor air monitoring, fault detection and diagnostics (FDD) of mechanical equipment and systems and building-to-grid integration. To this end, this work employed the Apache Jena Application Programming Interface (API) to develop KRR and integrate it with some domain-specific ontologies expressed in the Resource Description Framework (RDF) and Web Ontology Language (OWL). The ontology-driven rules were represented using Jena rule formalisms to enable the inference of implicit information from data asserted in the ontologies. Moreover, SPARQL (SPARQL Query Language for RDF) was used to query the knowledge graph and obtain useful information for a variety of building applications. This approach enhances building analytics through multi-domain knowledge integration; spatial and temporal reasoning for monitoring building operations, and control systems and devices; and the performance of compliance checking. We show that existing studies have not leveraged state-of-the-art ontologies to infer information from different domains. While the proposed semantic infrastructure and methods in this study demonstrated benefits for different building applications applicable to mechanical systems, the approach also has great potential for lighting, shading and security applications. Multi-domain knowledge integration that includes spatial and temporal reasoning allows the optimization of the performance of building equipment and systems.

Airport taxiway operations refer to the safe and efficient movement of aircraft while they are on the ground. It is now evident that as aviation systems become progressively crowded, mitigation of unnecessary congestion, accidents and costly delays will require new approaches to the scheduling and control of airport taxiway operations. This paper proposes a combined semantic model and reasoning, and real-time planning approach to decision making for safe and efficient airport taxiway operations. A key challenge in this work is finding ways to coordinate the real-time planning with the semantic model knowledge and reasoning capability to produce a system that has good performance, and can dynamically adapt to unexpected events. We exercise the proposed methodology on a planning problem that requires the coordination of multiple planes as they move from the terminal building to the same departure runway.

Waterway and canal systems are particularly cost effective in the transport of bulk and containerized goods to support global trade. Yet, despite these benefits, they are among the most under-appreciated forms of transportation engineering systems. Looking ahead, the long-term view is not rosy. Failures, delays, incidents and accidents in aging waterway systems are doing little to attract the technical and economic assistance required for modernization and sustainability. In a step toward overcoming these challenges, this paper argues that programs for waterway and canal modernization and sustainability can benefit significantly from system thinking, supported by systems engineering techniques. We propose a multi-level multi-stage methodology for the model-based design, simulation and formal verification of automated waterway system operations. At the front-end of development, semi-formal modeling techniques are employed for the representation of project goals and scenarios, requirements and high-level models of behavior and structure. To assure the accuracy of engineering predictions and the correctness of operations, formal modeling techniques are used for the performance assessment and the formal verification of the correctness of functionality. The essential features of this methodology are highlighted in a case study examination of ship and lock-system behaviors in a two-stage lock system.