A review of the use of artificial intelligence methods in infrastructure systems. (November 2022)

• Record Type: Journal Article
• Authors: McMillan, Lauren; Varga, Liz
• Is Part Of: Engineering applications of artificial intelligence. Volume 116(2022)
• Publication Date: 2022-11
• Subjects: Artificial Intelligence AI -- Adaptive neuro-fuzzy inference systems ANFIS -- Artificial neural network ANN -- Radial basis function RBF -- Recurrent neural network RNN -- Echo state network ESN -- Long short-term memory LSTM -- Multilayer perceptron MLP -- Unmanned aerial vehicle UAV -- Convolutional neural network CNN -- Support vector machine SVM -- Decision Tree DT -- Random forest RF -- Deep belief networks DBN -- Vehicular ad-hoc network VANET -- Wireless sensor network WSN -- Autonomous underwater vehicle AUV -- Gaussian mixture model GMM -- Software defined network SDN -- Quality of experience QoE -- Quality of service QoS -- Quality of transmission QoT; Machine learning -- Deep learning -- AI method -- Neural network -- Knowledge representation -- Infrastructure sectors; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2022.105472
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: The artificial intelligence (AI) revolution offers significant opportunities to capitalise on the growth of digitalisation and has the potential to enable the 'system of systems' approach required in increasingly complex infrastructure systems. This paper reviews the extent to which research in economic infrastructure sectors has engaged with fields of AI, to investigate the specific AI methods chosen and the purposes to which they have been applied both within and across sectors. Machine learning is found to dominate the research in this field, with methods such as artificial neural networks, support vector machines, and random forests among the most popular. The automated reasoning technique of fuzzy logic has also seen widespread use, due to its ability to incorporate uncertainties in input variables. Across the infrastructure sectors of energy, water and wastewater, transport, and telecommunications, the main purposes to which AI has been applied are network provision, forecasting, routing, maintenance and security, and network quality management. The data-driven nature of AI offers significant flexibility, and work has been conducted across a range of network sizes and at different temporal and geographic scales. However, there remains a lack of integration of planning and policy concerns, such as stakeholder engagement and quantitative feasibility assessment, and the majority of research focuses on a specific type of infrastructure, with an absence of work beyond individual economic sectors. To enable solutions to be implemented into real-world infrastructure systems, research will need to move away from a siloed perspective and adopt a more interdisciplinary perspective that considers the increasing interconnectedness of these systems. Highlights: Literature on the use of AI in infrastructure sectors is reviewed. Types of AI, trends across sectors and purposes within infrastructure are identified. Findings have been evaluated using an analysis framework. The future of AI and the 'Survey PaperInternet of Things' in infrastructure are discussed.