A Digital Twin Decision Support System for the Urban Facility Management Process

Abstract

The ever increasing pace of IoT deployment is opening the door to concrete implementations of smart city applications, enabling the large-scale sensing and modeling of (near-)real-time digital replicas of physical processes and environments. This digital replica could serve as the basis of a decision support system, providing insights into possible optimizations of resources in a smart city scenario. In this article, we discuss an extension of a prior work, presenting a detailed proof-of-concept implementation of a Digital Twin solution for the Urban Facility Management (UFM) process. The Interactive Planning Platform for City District Adaptive Maintenance Operations (IPPODAMO) is a distributed geographical system, fed with and ingesting heterogeneous data sources originating from different urban data providers. The data are subject to continuous refinements and algorithmic processes, used to quantify and build synthetic indexes measuring the activity level inside an area of interest. IPPODAMO takes into account potential interference from other stakeholders in the urban environment, enabling the informed scheduling of operations, aimed at minimizing interference and the costs of operations.

Trends in Adopting BIM, IoT and DT for Facility Management: A Scientometric Analysis and Keyword Co-Occurrence Network Review

“…The union of BIM and IoT information can lead to the creation of a DT, which for the building sector can be defined as "the interaction between the real-world indoor environment of the building and a digital but realistic virtual representation model of the building environment, which provides the opportunity for real-time monitoring and data acquisition" [17]. DT can offer a real-time insight on assets' conditions in order to individuate and spatially localize possible failures and also "support decision making process" [20,21]. In fact, the data monitoring can reveal future machines' health state and consent to organize proper maintenance action, which lead towards predictive maintenance, which allows users to know where, when and if a failure will occur [22].…”

Critical Infrastructures: Reliability, Resilience and Wastage

“…Going forwards, a synergy should be established between (i) upgraded infrastructure and (ii) a reduction in waste behaviours (i.e, excess) to cater for strain and the future demands placed by urbanisation. Relating to point (i), suitable technologies discussed in the literature include integrating AI/machine learning techniques [15], IoT sensors [16], digital twinning technologies [17,18], smart grid [19] and solar and wind [20]. Regarding point (ii), as discussed later in the findings, a suitable approach for eliminating wastegenerating behaviour is through education and developing an awareness of the impact waste behaviour has on the availability of critical services.…”

Towards Digital Twin-Driven Performance Evaluation Methodology of FMS

“…Decision Support Systems (DSSs), based on online modelling and simulation, play a crucial role in solving the problems under consideration (Vaisi, 2022;Bujari et al, 2021;Makris, Michalos & Chryssolouris, 2012;Sliwa & Patalas-Maliszewska, 2016). The methods used in DSSs design are usually problem-oriented, which limits the range of their possible applications (Bakar, Henry & Ali, 1991;Banaszak, 1992;Banaszak, Skolud & Zaremba, 2003;Viswandham & Narahari, 1992).…”