Automated Reasoning for City Infrastructure Maintenance Decision Support

Wei, Lijun; Magee, Derek R.; Dimitrova, Vania; Clarke, Barry; Du, Heshan; Mahesar, Quratul-ain; Ammari, Kareem Al; Cohn, Anthony G.

doi:10.24963/ijcai.2018/868

Cited by 4 publications

(1 citation statement)

References 2 publications

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“…Reasoning for asset planning and management under scenarios (Wei et al, 2018); Operational rules for water quality improvement and cost effectiveness (Meng et al, 2017) Commonsense reasoning…”

Section: Reliability Resilience and Sustainabilitymentioning

confidence: 99%

Artificial intelligence underpins urban water infrastructure of the future: A holistic perspective

Fu,

Sun,

Hoang

et al. 2023

Camb. prisms Water

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The potential of artificial intelligence (AI) in water management is widely recognised by research and practice communities alike, with an increasing number of applications showed tackling water supply, stormwater and wastewater management challenges. However, there is a critical knowledge gap in understanding the fundamental role of AI in the development of urban water infrastructure (UWI). This review aimed to provide an analysis of how AI could be aligned to support the future development of UWI systems. Four types of AI analytics – descriptive, diagnostic, predictive and prescriptive – are discussed and linked to the improvement in the performance of UWI systems from three categories: reliability, resilience and sustainability. It is envisioned that AI technology will play a pivotal role in UWI transitioning to the future through underpinning the five development pathways: decentralisation, circular economy, greening, decarbonisation and automation. The barriers in improving AI adoption in the real world are also highlighted from four dimensions: cyber-physical infrastructure, institutional governance, social-economic systems and technological development in wider society. Embedding AI in the development pathways and tackling the barriers can ensure that AI-empowered systems are deployed in an equitable and responsible way to improve the resilience and sustainability of future UWI systems.

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Section: Reliability Resilience and Sustainabilitymentioning

confidence: 99%

Artificial intelligence underpins urban water infrastructure of the future: A holistic perspective

Fu,

Sun,

Hoang

et al. 2023

Camb. prisms Water

View full text Add to dashboard Cite

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City infrastructure ontologies

Wei

Dimitrova

et al. 2023

Computers, Environment and Urban Systems

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An ontological approach for pathology assessment and diagnosis of tunnels

Dimitrova

Mehmood

Thakker

et al. 2020

Engineering Applications of Artificial Intelligence

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Tunnel maintenance requires complex decision making, which involves pathology diagnosis and risk assessment, to ensure full safety while optimising maintenance and repair costs. A Decision Support System (DSS) can play a key role in this process by supporting the decision makers in identifying pathologies based on disorders present in various tunnel portions and contextual factors affecting a tunnel. Another key aspect is to identify which spatial stretches within a tunnel contain pathologies of similar kinds within neighbouring tunnel segments. This paper presents PADTUN, a novel intelligent decision support system that assists with pathology diagnosis and assessment of tunnels with respect to their disorders and diagnosis influencing factors. It utilises semantic web technologies for knowledge capture, representation, and reasoning. The core of PADTUN is a family of ontologies which represent the main concepts and relations associated with pathology assessment, and capture the decision process concerning tunnel maintenance. Tunnel inspection data is linked to these ontologies to take advantage of inference capabilities offered by semantic technologies. In addition, an intelligent mechanism is presented which exploits abstraction and inference capabilities. Thus PADTUN provides the world's first semantically based intelligent DSS for tunnel maintenance. PADTUN was developed by an interdisciplinary team of tunnel experts and knowledge engineers in real-world settings offered by the NeTTUN EU Project. An evaluation of the PADTUN system is performed using real-world tunnel data and diagnosis tasks. We show how the use of semantic technologies allows addressing the complex issues of tunnel pathology inferencing, aiding in, and matching transportation experts' expectations of decision support. The methodology is applicable to any linear transport structures, offering intelligent ways to aid with complex decision processes related to diagnosis and maintenance.

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Automated Reasoning for City Infrastructure Maintenance Decision Support

Cited by 4 publications

References 2 publications

Artificial intelligence underpins urban water infrastructure of the future: A holistic perspective

Artificial intelligence underpins urban water infrastructure of the future: A holistic perspective

City infrastructure ontologies

An ontological approach for pathology assessment and diagnosis of tunnels

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