COGNIBUILD: Cognitive Digital Twin framework for advanced building management and predictive maintenance

According to contemporary challenges of digital evolution in management and maintenance of construction processes, the present study aims at defining valuable strategies for building management optimization. As buildings and infrastructures Digital Twins (DT) are directly connected to physical environment through the Internet of Things (IoT), asset management and control processes can be radically transformed. The proposed DT framework connects Building Information Model (BIM) three-dimensional objects to information about the planned maintenance of components, supplying system’s self-learning capabilities through input data coming from Building Management Systems (BMS), ticketing, as well as maintenance activities data flow both as-needed or unexpected. The concept of real-time acquisition and data processing set the basis for the proposed system architecture, allowing to perform analysis and evaluate alternative scenarios promptly responding to unexpected events with a higher accuracy over time. Moreover, the integration of Artificial Intelligence (AI) allows the development of maintenance predictive capabilities, optimizing decision making processes and implementing strategies based on the performed analysis, configuring a scalable approach useful for different scenarios. The proposed approach is related to the evolution from reactive to proactive strategies based on Cognitive Digital Twins (CDT) for Building and Facility Management, providing actionable solutions through operational, monitoring and maintenance data. Through the integration of BIM data with information systems, BMS, IoT and Machine Learning, the optimization and real-time automation of maintenance activities is performed, radically reducing failures and systems breakdowns. Therefore, integrating different technologies in a virtual environment allows to define data-driven predictive models supporting Building Managers in decision making processes improving efficiency over time and moving from reactive to proactive approaches

Immersive Facility Management – a methodological approach based on BIM and Mixed Reality for training and maintenance operations

Innovation technology in industries including manufacturing and aerospace is moving towards the use of Mixed Reality (MR) and advanced tools while Architecture, Engineering and Construction (AEC) sector is still remaining behind it. Moreover, the use of immersive technologies in the AEC digital education, as well as for professional training, is still little considered. Augmented and Mixed reality (AR/MR) have the capability to provide a “X-ray vision”, showing hidden objects in a virtual/real overlay. This feature in the digital object visualization is extremely valuable for improving operation performance and maintenance activities. The present study gives an overview of literature about the methodologies to integrate virtual technologies such as AR/MR and Building Information Modelling (BIM) to provide an immersive technology framework for training purposes together with the Digital Twin Model (DTM)-based approach. Furthermore, the Facility Management (FM) tasks’ training on complex building systems can benefit from a virtual learning approach since it provides a collaborative environment enhancing and optimizing efficiency and productivity in FM learning strategies. For this purpose, the technological feasibility is analysed in the proposed case study, focusing on the realization of a methodological framework prototype of immersive and interactive environment for building systems’ FM. Cloud computing technologies able to deal with complex and extensive information databases and to support users' navigation in geo-referenced and immersive virtual interfaces are include as well. Those ones enable the DTM-based opera-tion for building maintenance both in real-time FM operators’ training and FM tasks’ optimization

Historical analysis and refurbishment proposal of the “Red schools” in Viterbo

To hinder climate change, EU legislation requires that by 2020 each European state achieves the objectives set by the 2020 Climate and Energy Package. Particular attention is paid not only to new constructed buildings, the so-called Near Zero Energy Buildings, but also to the existing building stock: in Italy in fact, in addition to the National Action Plan to increase the NZEB buildings (PANZEB), the Strategy for Energy Renewal of the National Real Estate Park (STERPIN) is planned. The aim of the thesis work is a primary school built in 1938 within the historical centre of Viterbo. The work touched on three different areas of design: the design of the internal and external spaces, annexed to the school building, finding solutions for a flexible and functional distribution in line with the theories of modern pedagogy, moving from a school of homologation to a school of diversity enhancement. This was joined by a study concerning the original elevations and constructive features, bearers of historical and aesthetic values, which resulted in the proposal for conservative restoration of the Terranova plaster and the original iron-window profiles. Finally, attention was paid to energy upgrading and efficiency, in line with regulatory provisions. The interventions did not only concern the building envelope (through a thermal upgrading of the original iron-windows, the insulation of the flat roof and the indoor thermal coat of the perimeter walls), but also the system (through the replacement of the boiler with a heat pump, integrated with the photovoltaic system placed on the roof, the inclusion of thermostatic valves and lighting design with the replacement of fluorescent lamps with LED ones)

ZEB Prototype Controlled by a Machine Learning System

This communication concerns a research project by the Interdepartmental Research Centre for Territory Construction Restoration and Environment (CITERA) of Sapienza University of Rome in collaboration with ENEA (Italian National Agency for New Technologies, Energy and Sustainable Economic Development) based on the realization of a 1:1 scale demonstrator of a Zero Energy Building that allows continuous experimentation of new technologies for innovative photovoltaic systems, efficient storage systems and high-performance envelope materials. In particular a measurement protocol has been developed for both the overall efficiency of the building and the individual technological components with a view to a comparative critical analysis of the integration of the individual components in the building-system complex. All the technological systems has been used in Solar Decathlon Middle East 2018 competition in Dubai. The project concerns the development of a control and management system for photovoltaic energy production systems for the ZEB prototype, based on an intelligent self-learning system (AI) able to optimize the parameters of self-produced electricity supply based on real consumption of air conditioning equipment, electrical power supply to the equipment, access control and safety equipment. The most immediate result concerns the integrated design of both the hardware systems for the production and use of electricity and the algorithms that continuously measure parameters such as grid load, consumption and electricity production, and which takes into account weather forecasts, energy tariffs, and learns the trend of electricity consumers through the use of artificial intelligence

STRATEGIES AND OUTCOMES OF BIM EDUCATION: ITALIAN EXPERIENCES

The growing need on the part of operators in the construction sector for expertise in digital management of construction information processes has been answered since 2015 in the first level master course called BIM Master, from the Department of Planning Design and Technology of Architecture of Sapienza University of Rome. The training course is now in its fifth edition, registering a growing number of students. It has evolved in terms of content delivery methods, and always provides opportunities for internships in client, designers and construction organizations, achieving significant results in terms of placement. The BIM Master is one of the most consolidated national experiences, and has been proposed since the first edition as a training opportunity that links the needs of the market and the expectations of students. It is configured as a project in continuous development that adapts itself year after year to the evolutionary dynamics of the digital construction market, also resulting from the recent national regulatory innovations (UNI 11337). The complexity of BIM training also increases in relation to the different nature and needs of the operators in the sector: owners; designers; builders; and managers, who lead the need for articulated training courses in relation to the role played in the construction supply chain. This paper critically examines, also on the basis of the results of past editions, the training experience of the BIM Master course of Sapienza University of Rome, analyzing the structure, examining the articulation of the main training modules, assessing the results produced by the students, measuring the critical points and, at the same time, identifying the drivers of greater effectiveness in the transfer of skills of a theme that lends itself to ambiguous interpretations at this history time by operators in the construction industry

Cyber-Physical Systems Improving Building Energy Management: Digital Twin and Artificial Intelligence

The research explores the potential of digital-twin-based methods and approaches aimed at achieving an intelligent optimization and automation system for energy management of a residential district through the use of three-dimensional data model integrated with Internet of Things, artificial intelligence and machine learning. The case study is focused on Rinascimento III in Rome, an area consisting of 16 eight-floor buildings with 216 apartment units powered by 70% of self-renewable energy. The combined use of integrated dynamic analysis algorithms has allowed the evaluation of different scenarios of energy efficiency intervention aimed at achieving a virtuous energy management of the complex, keeping the actual internal comfort and climate conditions. Meanwhile, the objective is also to plan and deploy a cost-effective IT (information technology) infrastructure able to provide reliable data using edge-computing paradigm. Therefore, the developed methodology led to the evaluation of the effectiveness and efficiency of integrative systems for renewable energy production from solar energy necessary to raise the threshold of self-produced energy, meeting the nZEB (near zero energy buildings) requirements

Intron 4-5 hTERT DNA Hypermethylation in Merkel Cell Carcinoma: Frequency, Association with Other Clinico-pathological Features and Prognostic Relevance

Merkel cell carcinoma (MCC) is an aggressive skin tumor with neuroendocrine differentiation, mainly affecting elderly population or immunocompromised individuals. As methylation of the human telomerase reverse transcriptase (mhTERT) has been shown to be a prognostic factor in different tumors, we investigated its role in MCC, in particular in intron 4-5 where rs10069690 has been mapped and recognized as a cancer susceptibility locus. DNA methylation analysis of hTERT gene was assessed retrospectively in a cohort of 69 MCC patients from the University of Bologna, University of Turin and University of Insubria. Overall mortality was evaluated with Kaplan-Meier curves and multivariable Royston-Parmar models. High levels of mhTERT (mhTERThigh) (HR\u2009=\u20092.500, p\u2009=\u20090.015) and p63 (HR\u2009=\u20092.659, p\u2009=\u20090.016) were the only two clinico-pathological features significantly associated with a higher overall mortality at the multivariate analysis. We did not find different levels of mhTERT between MCPyV (+) and (-) cases (21 vs 14, p\u2009=\u20090.554); furthermore, mhTERThigh was strongly associated with older age (80.5 vs 72 years, p\u2009=\u20090.026), no angioinvasion (40.7% vs 71.0%, p\u2009=\u20090.015), lower Ki67 (50 vs 70%, p\u2009=\u20090.005), and PD-L1 expressions in both tumor (0 vs 3%, p\u2009=\u20090.021) and immune cells (0 vs 10%, p\u2009=\u20090.002). mhTERT is a frequently involved epigenetic mechanism and a relevant prognostic factor in MCC. In addition, it belongs to the shared oncogenic pathways of MCC (MCPyV and UV-radiations) and it could be crucial, together with other epigenetic and genetic mechanisms as gene amplification, in determining the final levels of hTERT mRNA and telomerase activity in these patients

Secondo classificato DIGITAL&BIM Award Research Category

La tesi ha come obiettivo quello di indagare, costruire e sperimentare metodi ed approcci che rispondano alle moderne sfide proposte della sempre più necessaria digitalizzazione dei processi industriali, intesa come un vero e proprio management digitale, che unito alle potenzialità sempre crescenti dell’Intelligenza Artificiale e del Machine Learning, permetta di gestire, ottimizzare ed automatizzare le fasi dei processi costruttivi, con particolare riguardo alla fase di gestione dei processi legati al ciclo di vita dell’ambiente costruito. La parola chiave, quindi, è proprio gestione: in particolare la gestione di una porzione di città, nel caso specifico di un comparto residenziale composto da 16 edifici, mirata a costruire processi ed ecosistemi totalmente digitali basati su modelli informativi tridimensionali che siano in grado di replicare oggetti fisici, come ad esempio gli edifici, e soprattutto che sappiano gestire e monitorare le loro interazioni con la realtà. Le crescenti potenzialità offerte dall’utilizzo di approcci basati sull ICT (Information Comunication Technology) aprono scenari rinnovati per la gestione dei processi nell’ambiente costruito. Infatti, tramite la realizzazione di modelli tridimensionali tramite approcci integrati BIM (Building Information Modeling) e GIS (Geographic Information Systems) è possibile costruire veri e propri database geometrici microscopici e macroscopici, contenenti dati statici, dinamici, geometrici e semantici che rappresentino il nucleo informativo per la gestione efficiente del ciclo di vita di sistemi verticali ed orizzontali. L’elaborazione di dati ed informazioni solide ed omogenee riguardanti il ciclo di vita degli asset all’interno del suo gemello digitale garantisce un notevole supporto ai processi di gestione dell'ambiente costruito. E’ solo mediante l'integrazione di tali modelli con sistemi di Intelligenza Artificiale che diventa possibile aumentare l’ottimizzazione e progressiva automazione funzionale delle attività interconnesse al ciclo di vita degli asset, ed in particolare sono analizzati e sperimentati gli ambiti di seguito descritti. ENERGY MANAGEMENT La sperimentazione riguarda la configurazione di una smart grid energetica orientata all'autogestione per l'ottimizzazione dei consumi e della produzione di energia. Sfruttando le potenzialità del machine learning, è possibile infatti creare un sistema automatizzato virtuoso di gestione della produzione e del consumo dell'energia elettrica e termica tramite algoritmi di calcolo dell'energia massima producibile (mediante l’integrazione di un sistema geotermico, solare termico e solare fotovoltaico) prevedendo i consumi degli edifici sulla base di dati storici trasmessi da sensori e integrati con il calcolo giornaliero dei carichi termici effettuati dal digital twin (gemello digitale). SECURITY MANAGEMENT Il digital twin realizzato prevede la configurazione di un sistema digitale integrato per la gestione della sicurezza che prescinda dalla componente umana (unmanned security) mediante l’utilizzo di sistemi di autoapprendimento e Intelligenza Artificiale basati sul riconoscimento di immagini, che aumentino i livelli di sicurezza del comparto minimizzando i rischi di aggressioni, furti, intrusioni e forzature, assembramenti etc. OPERATION & MAINTENANCE Le operazioni di gestione e manutenzione di un comparto residenziale possono beneficiare della configurazione di sistemi di manutenzione predittiva mirati alla riduzione dei costi di gestione e dei malfunzionamenti, mediante un vero e proprio modello di management digitale della manutenzione, che utilizzi sistemi intelligenti basati su sensori e dati storici in continuo aggiornamento tramite il digital twin. Lo sviluppo del digital twin ha quindi inizio da un modello informativo integrato, nel caso degli edifici si tratta del cosiddetto BIM, in grado di contenere dati e informazioni utili al processo, il quale, in comunicazione con dati provenienti da sensori, diventa un gemello digitale dotato di capacità di apprendimento, in grado di elaborare le informazioni ricevute. Il caso di studio analizza quindi le potenzialità del Digital Twin che, unite a sistemi di Intelligenza Artificiale, possano costituire un ecosistema di controllo e gestione del comparto, in particolare indagando tali opportunità rispetto a tre diversi ambiti di management: il primo riguarda l’utilizzo di sistemi digitali per la gestione e la simulazione energetica, orientati a definire una smart grid finalizzata all’ottimizzazione dei consumi e della produzione; il secondo riguarda la configurazione di un sistema completamente digitale e integrato per la gestione della sicurezza del comparto, che punti a prescindere totalmente dalla componente umana, definendo un sistema di unmanned security; infine riguardo alle operazioni di gestione e manutenzione del comparto, l’obiettivo è la riduzione dei costi di gestione e soprattutto dei malfunzionamenti, configurando sistemi digitali per la manutenzione predittiva mirando alla progressiva eliminazione degli interventi a guasto. L’esperienza evidenzia dunque come l’integrazione di algoritmi di Intelligenza Artificiale permetta al Digital Twin di sviluppare capacità predittive e in ultimo di prendere ed attuare decisioni autonome in base alle analisi effettuate, rappresentando un approccio metodologico replicabile nei più svariati ambiti di utilizzo

Linee guida per l'efficientamento energetico degli edifici residenziali della Regione Lazio

La crescente consapevolezza dei limiti naturali imposti dall’ecosistema ha fatto emergere un progressivo consenso in merito a una nuova visione di sviluppo, nella quale i principi di sostenibilità ed efficientamento energetico sono integrati nei quadri di riferimento strategici nazionali e regionali e nelle politiche di settore. I cambiamenti climatici rappresentano infatti un fenomeno attuale di consistente entità e negli ultimi decenni è aumentata, in misura sempre più importante, l’attenzione verso l’ambiente e, più in dettaglio, per il riscaldamento globale, causato dall’enorme quantitativo di emissioni di gas serra rilasciate nell’atmosfera e derivanti da un’attività umana ricca di sprechi e di inefficienze. Il problema ambientale è strettamente legato a quello energetico e quest’ultimo, considerato uno dei settori maggiormente responsabile delle emissioni di gas climalteranti, risulta uno dei nodi che deve essere affrontato e risolto in tempi brevi, per contenere i danni recati al nostro pianeta. Risulta fondamentale trovare modelli di sviluppo più sostenibili e investire in risorse e tecnologie per ridurre le emissioni di gas a effetto serra. Per far ciò è necessario delineare percorsi e azioni che promuovano l’efficienza energetica e l’utilizzo di fonti rinnovabili, da compiere sia individualmente che collettivamente. Secondo dati recenti più della metà della popolazione mondiale vive all’interno degli insediamenti urbani. Le proiezioni di crescita degli agglomerati urbani, secondo le previsioni dei World Urbanization Prospects, portano a stimare che, nel 2030, il 60% della popolazione totale (stimata intorno a 8,5 miliardi di persone) sarà urbanizzata fino a raggiungere il livello del 70% nel 2050. Assume sempre maggiore importanza, quindi, la problematica della stretta interdipendenza tra città e ambiente globale che era tipica essenzialmente dei paesi più industrializzati quali l’Italia ma, che ora, si è estesa anche ai paesi più popolosi della terra con conseguenze facilmente immaginabili sugli impatti ambientali. Si stima infatti che il 40-50% delle emissioni di gas serra siano ormai da attribuire al settore edile contro il 25% dovuto ai trasporti e il restante 25% ascrivibile al settore industriale. È quindi nei luoghi in cui tali attività si concentrano – gli agglomerati urbani – che bisogna indirizzare gli sforzi congiunti a livello mondiale per realizzare le azioni di protezione e tutela dell’ambiente e del clima globale. Da questa consapevolezza è derivato il termine di “architettura sostenibile” che vuole rappresentare una nuova concettualizzazione dell’architettura rivista in coerenza con il pensiero di uno sviluppo sostenibile inteso come “meetings the need of the present without compromising the ability of the future generations to meet their own needs”. Oggi, comunque, bisogna già rivedere l’approccio all’edilizia sostenibile che in passato era concentrato prevalentemente sugli aspetti ecologici per finalizzare gli sforzi sulle problematiche della conservazione dell’energia e della sostenibilità ambientale in tutte le fasi del processo edilizio per studiare e mettere a punto regole, criteri e tecnologie integrate nel rispetto dei più recenti documenti programmatici internazionali. Oltre alla problematica energetica, infatti, nei vari trattati transnazionali si evidenzia come i punti critici relativi alla “sostenibilità” implichino una attenzione particolare all’utilizzo razionale di altre fondamentali risorse per il pianeta quali acqua, materiali e suolo. I cinque principi cardine da utilizzare per la salvaguardia delle risorse ambientali sono dunque: - riusare; - rinnovare; - riciclare; - proteggere; - conservare. Il campo principale individuato per la messa in relazione di sistemi di produzione moderni ed efficienti con l’utilizzo sostenibile delle risorse è proprio quello dell’edilizia; ciò deve essere fatto nella progettazione dei nuovi edifici ma anche nell’adeguamento di quelli esistenti che rappresentano la stragrande maggioranza del patrimonio edilizio italiano ed europeo. Nel recupero degli edifici esistenti, che sono difficilmente adattabili ai nuovi canoni di progettazione, si deve comunque scegliere di intervenire utilizzando - ove possibile - sistemi di produzione di energie da fonte rinnovabile e al contempo contenendo i consumi con un efficientamento dell’involucro, dopo una attenta analisi delle prestazioni energetiche e dei caratteri tipologici dell’esistente (diagnosi energetica). L’originalità dell’approccio di ricerca presente nel volume riguarda essenzialmente il lavoro iniziale sulle tipologie edilizie esistenti nella regione Lazio, di analisi sullo stato dell’arte di sistemi e componenti preferibilmente prefabbricati e/o plug and play esistenti sul mercato internazionale e nazionale e sul loro livello di diffusione per seguire la logica oramai cogente del concetto di edilizia off-site con un particolare riferimento ai materiali di origine locale. Il risultato finale è stata quindi la redazione di linee guida funzionali alle attività dei tecnici di settore operanti sul territorio della regione Lazio al fine di supportare nel loro compito di valutare, progettare e seguire gli interventi di efficientamento energetico degli edifici. La scelta è stata orientata verso prodotti che garantiscano elevate performance sia energetiche che antisismiche e caratterizzati da idonei parametri prestazionali termo-fisici e di sicurezza strutturale che consentano inoltre di correlare la loro applicabilità alle diverse zone climatiche. A supporto delle analisi di cui sopra, nel volume sono riportati materiali e tecnologie desunte da alcune best practices del Progetto Enerselves, esemplificative dei sistemi standardizzati adottati per arrivare allo sviluppo uniformato di quegli elementi/sistemi/processi che necessitano di adeguamento ai nuovi requisiti prestazionali previsti dalle normative vigenti e che non risultano sufficientemente diffusi nell’ottica di standardizzazione del processo produttivo e realizzativo. L’applicazione di alcune di queste tipologie di intervento in due casi studio (residenziale e patrimonio pubblico) gestiti in logica BIM2 permette di valutarne l’efficacia nonché di apprezzare al contempo le potenzialità introducibili dalle logiche di digitalizzazione applicate al processo edilizio. Partendo quindi dalle tipologie costruttive selezionate e dall’individuazione dei nodi strutturali ad esse correlati gli autori hanno individuato, in funzione delle diverse componenti/partizioni, tempistiche e tecniche di montaggio dei singoli sistemi e componenti, fino ad arrivare all’individuazione di un abaco di elementi/moduli/tecnologie prefabbricati standard in grado di rispondere alle diverse esigenze climatiche, geometrico/dimensionali e tecnico/prestazionali che si possono riscontrare nella realtà costruttiva abitativa italiana

Digital methods and tools in construction processes for efficient project management workflows: case histories

Buildings are becoming far more than walls, roofs and masonry: thanks to Artificial Intelligence (AI), building systems are becoming able to autonomously integrate the proliferation of data from IoT devices and occupant behavior to apply learning, optimize performance and improve environmental efficiency. As AI is integrated with building systems and Internet of Things (IoT) devices, it has the potential to improve occupant experience, increase operational efficiency and optimize space and asset utilization. A vast array of information from digital devices provides insights about the operations, use and condition of everything from the building’s infrastructure, physical environment, climate, water and energy usage, to an occupant’s experience and satisfaction, then IoT and platforms embedded with Artificial Intelligence and machine learning make it possible to develop innovative new services for engaging with building occupants. These systems have the potential to radically reduce costs through automation and optimization of operations. By taking advantage of powerful analytics and Artificial Intelligence for example, building owners can significantly cut energy consumption and achieve ambitious cost-saving targets. After equipment performance information is collected through sensors and meters, a library of benchmark data is applied, analytics are performed and potential operational improvements are identified. Analytics can also be used to prevent energy waste by isolating inefficient energy use. Sensor-controlled systems can monitor dispensing and water use. Cognitive maintenance systems can help preserve the health of critical building equipment and assets by anticipating asset failure and guiding timely interventions and so on. A comprehensive building optimization system leverages all aspects of building and facility management. These types of systems allow for monitoring the use of space, water and the usage and allocation of energy. Taking this monitoring one step further, building equipment data collected from IoT sensors that is tagged by location or asset type and associated with business rules can trigger algorithms to not only detect but also predict and respond to anomalies. These optimized ecosystems of building technologies identify opportunities for efficiency controls through predictive maintenance. They identify possible root causes, so actions can be prioritized, assigned, monetized and prevented, as recommendations that appear on dashboards or adjustments can be routed directly to the IoT device for action. AI is able to capture data from day-to-day building operations to enable new levels of automation, which enables buildings to “think,” engage and learn. These buildings can autonomously monitor and predict their own maintenance needs. Data transmitted from connected assets, such as boilers, pumps, chillers and elevators, is analyzed and enriched to identify anomalies, such as equipment operating outside of normal parameters. Potential failure modes are identified from tolerance and business rules, and devices are automatically instructed to take corrective action. The building memorizes the result of the intervention so it can improve the accuracy of detection and resolution of future incidents. The integration of cognitive analytics, sensors and existing building systems can also significantly improve occupant experience. Envision going to work in a building that works for you. While you’re there, IoT sensors are constantly monitoring your movement and the temperature. It turns lights on and off for you, adjusts the flow of water in restrooms and listens for your voice commands. Even breaths are monitored for carbon dioxide concentration in case an airflow adjustment is needed. And when the building detects that people have left their assigned workspaces, it turns on the lights in the parking garage, places the building systems into rest mode and checks tomorrow’s weather. This kind of approach to problem solving is related to simulation modelling, which aims at reproducing in a virtual environment the behavior of a non-linear dynamic system. It serves as a digital testbed where one can assess ex-ante different strategies over a simulated time-horizon. The development of the Digital Twin therefore begins with an integrated Information Model, in the case of buildings this is the so-called BIM (Building Information Model), able to contain data and information useful to simulate the process, which in communication with data from sensors becomes a Digital Twin with learning capabilities, able to process the information received. Artificial Intelligence algorithms then allow the Digital Twin to develop predictive capabilities and finally to make and implement autonomous decisions based on the analysis performed. The project aims to investigate, build and test methods and approaches that respond to the challenges proposed by the increasingly necessary digitization of industrial processes, as a true digital management, which combined with the growing potential of Artificial Intelligence and machine learning, allows to manage, optimize and automate the phases of construction processes, with particular regard to the management phase of processes related to the life cycle of the built environment. As already mentioned, the key word is management: in particular it is explored a case history about the Digital Twin-based management of a portion of city, in the specific case of a residential area located in Rome and composed of 16 buildings. The goal is to build a digital process and ecosystem based on three-dimensional information models that are able to replicate physical objects, such as buildings, and especially manage and monitor their interactions with reality. The Digital Twin of a residential system can therefore be a key tool for the storage, visualization, analysis and creation of data useful for the management of urban life and, considering the absolute centrality of data in the realization of digital processes, an important development consists in the integrated use of GIS and BIM, aimed at the information management and processing of information both at the scale of the building and at the geographical and territorial scale. BIM and GIS, although they share the essential concept of description of the real world through the combination of visual representations and information, are conceived and developed as belonging to different domains, and therefore have differences also and especially in the level of detail. BIM can be used to create, manage and share life-cycle data for vertical structures, such as buildings, while GIS can store, manage and analyze data describing the urban environment, distributed horizontally. Therefore, Digital Twins of buildings reproduce their geometric, but above all, informative characteristics, and they appear as 3D models but they have the particularity of being configured and structured as real time three-dimensional databases, where data are contained within objects equipped with specific parameters and attributes that describe the characteristics of the components themselves, thus enclosing useful information for the definition and simulation of processes. The Digital Twin gradually becomes able to improve and enrich its knowledge and improves data, receiving inputs and signals from sensors constantly monitoring the buildings, developing self-learning and above all predictivity capabilities, through the integration with Artificial Intelligence algorithms