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Purpose This study aims to introduce a new methodology for generating synthetic images for facility management purposes. The method starts by leveraging the existing 3D open-source BIM models and using them inside a graphic engine to produce a photorealistic representation of indoor spaces enriched with facility-related objects. The virtual environment creates several images by changing lighting conditions, camera poses or material. Moreover, the created images are labeled and ready to be trained in the model. Design/methodology/approach This paper focuses on the challenges characterizing object detection models to enrich digital twins with facility management-related information. The automatic detection of small objects, such as sockets, power plugs, etc., requires big, labeled data sets that are costly and time-consuming to create. This study proposes a solution based on existing 3D BIM models to produce quick and automatically labeled synthetic images. Findings The paper presents a conceptual model for creating synthetic images to increase the performance in training object detection models for facility management. The results show that virtually generated images, rather than an alternative to real images, are a powerful tool for integrating existing data sets. In other words, while a base of real images is still needed, introducing synthetic images helps augment the model’s performance and robustness in covering different types of objects. Originality/value This study introduced the first pipeline for creating synthetic images for facility management. Moreover, this paper validates this pipeline by proposing a case study where the performance of object detection models trained on real data or a combination of real and synthetic images are compared.

PurposeThe assessment of the Real Estate (RE) prices depends on multiple factors that traditional evaluation methods often struggle to fully understand. Housing prices, in particular, are the foundations for a better knowledge of the Built Environment and its characteristics. Recently, Machine Learning (ML) techniques, which are a subset of Artificial Intelligence, are gaining momentum in solving complex, non-linear problems like house price forecasting. Hence, this study deployed three popular ML techniques to predict dwelling prices in two cities in Italy.Design/methodology/approachAn extensive dataset about house prices is collected through API protocol in two cities in North Italy, namely Brescia and Varese. This data is used to train and test three most popular ML models, i.e. ElasticNet, XGBoost and Artificial Neural Network, in order to predict house prices with six different features.FindingsThe models' performance was evaluated using the Mean Absolute Error (MAE) score. The results showed that the artificial neural network performed better than the others in predicting house prices, with a MAE 5% lower than the second-best model (which was the XGBoost).Research limitations/implicationsAll the models had an accuracy drop in forecasting the most expensive cases, probably due to a lack of data.Practical implicationsThe accessibility and easiness of the proposed model will allow future users to predict house prices with different datasets. Alternatively, further research may implement a different model using neural networks, knowing that they work better for this kind of task.Originality/valueTo date, this is the first comparison of the three most popular ML models that are usually employed when predicting house prices.

The decarbonization strategies for the built environment that policy-makers face today from the EU mandate risk being made with incomplete or insufficient information. The consequence of this could be ineffective choices, thus slowing down the ongoing ecological transition, or their high cost, whether borne by the state or citizens. The progressive and unstoppable digitization of the built environment offers information collection and previously unthinkable management opportunities. The construction sector, traditionally lagging behind other industrial sectors, is beginning to produce large quantities of data that can be exploited thanks to the most modern techniques derived from the information technology sector. Among the most promising data sources are energy performance certificates for buildings, which provide a snapshot of the characteristics of buildings, their fabric and plant components, and design forecasts of their energy performances. Analyzing the energy performance certificates through Artificial Intelligence techniques proves the effectiveness of using big data in the construction sector. In particular, in this study, unsupervised machine learning techniques led to an in-depth knowledge of a stock of buildings approaching two hundred thousand units distributed over an almost twenty-four thousand square kilometers area in northern Italy.

Buildings’ long-term techno-economic performance monitoring is critical for benchmarking in order to reduce costs and environmental impact while providing adequate services. Reliable building stock performance data provide a fundamental knowledge foundation for evidence-based energy efficiency interventions and decarbonisation strategies. Simply put, an adequate understanding of building performance is required to reduce energy consumption, as well as associated costs and emissions. In this framework, Variable-base degree-days-based methods have been widely used for weather normalisation of energy statistics and energy monitoring for Measurement and Verification (M & V) purposes. The base temperature used to calculate degree-days is determined by building thermal characteristics, operation strategies, and occupant behaviour, and thus varies from building to building. In this paper, we develop a variable-base degrees days regression model, typically used for energy monitoring and M & V, using a “proxy” variable, the cost of energy services. The study’s goal is to assess the applicability of this type of model as a screening tool to analyse the impact of efficiency measures, as well as to understand the evolution of performance over time, and we test it on nine public schools in the Northern Italian city of Seregno. While not as accurate as M & V techniques, this regression-based approach can be a low-cost tool for tracking performance over time using cost data typically available in digital format and can work reasonably well with limited resolution, such as monthly data. The modelling methodology is simple, scalable and can be automated further, contributing to long-term techno-economic performance monitoring of building stock in the context of incremental built environment digitalization.

The built environment is responsible for roughly 40% of global greenhouse emissions, making the sector a crucial factor for climate change and sustainability. Meanwhile, other sectors (like manufacturing) adopted Artificial Intelligence (AI) to solve complex, non-linear problems to reduce waste, inefficiency, and pollution. Therefore, many research efforts in the Architecture, Engineering, and Construction community have recently tried introducing AI into building asset management (AM) processes. Since AM encompasses a broad set of disciplines, an overview of several AI applications, current research gaps, and trends is needed. In this context, this study conducted the first state-of-the-art research on AI for building asset management. A total of 578 papers were analyzed with bibliometric tools to identify prominent institutions, topics, and journals. The quantitative analysis helped determine the most researched areas of AM and which AI techniques are applied. The areas were furtherly investigated by reading in-depth the 83 most relevant studies selected by screening the articles’ abstracts identified in the bibliometric analysis. The results reveal many applications for Energy Management, Condition assessment, Risk management, and Project management areas. Finally, the literature review identified three main trends that can be a reference point for future studies made by practitioners or researchers: Digital Twin, Generative Adversarial Networks (with synthetic images) for data augmentation, and Deep Reinforcement Learning.

Scholars and researchers worldwide are shifting the focus from the well-being in the internal spaces of buildings to the well-being in the external spaces, especially in large cities where pollution and heat island effect threaten the comfort and health of citizens. Calculation models aimed at studying comfort in outdoor spaces of cities are becoming more and more efficient and effective. However, all require large amounts of data that can only be collected automatically using the potential of the most innovative technologies available today. Among these, computer vision techniques are used in this study to extract some of the information needed to model the heat island phenomenon. This study introduces a convolutional neural network architecture to recognize the materials and colors of buildings’ facade automatically. Despite some limitations, mainly due to the number of images used to train the algorithm, the results are promising and show how computer vision can be a valuable tool to obtain automatic information about the built environment.

Digitisation of the built environment is seen as a significant factor for innovation in the Architecture, Engineering, Construction and Operation sector. However, lack of data and information in as-built digital models considerably limits the potential of Building Information Modelling in Facility Management. Therefore, optimisation of data collection and management is needed, all the more so now that Industry 4.0 has widened the use of sensors into buildings and infrastructures. A literature review on the two main pillars of digitalisation in construction, Building Information Modelling and Internet of Things, is presented, along with a bibliographic analysis of two citations and abstracts databases focusing on the operations stage. The bibliographic research has been carried out using Web of Science and Scopus databases. The article is aimed at providing a detailed analysis of BIM–IoT integration for Facility Management (FM) process improvements. Issues, opportunities and areas where further research efforts are required are outlined. Finally, four key areas of further research development in FM management have been proposed, focusing on optimising data collection and management.

Citizens in dense built environments are susceptible to the simultaneous occurrence of Slow Onset Disaster (SLOD) events, being particularly prone to increasing temperatures and air pollution. Previous research works have assessed these events&rsquo, arousal separately and have identified when their intensity is critical. However, few have integrated their analysis, possibly limited by the quality and granularity of available data, the accessibility and distribution of sensors, and measurements not emulating the surroundings of a pedestrian. Thus, this work performed an outdoor meso-scale multi-hazard-based risk analysis to study the aggregated effects of the SLODs mentioned above. The study was carried out to narrow down the time-frames within 2019 in which these two events could have affected citizens&rsquo, health the most. A weighted fuzzy logic was applied to superimpose climatic (temperature, humidity, wind speed, and solar irradiance) and air quality (particulate matter, ozone, and ammonium) distress (true risk) on an hourly basis, allocated using set healthy and comfortable ranges for a specific dense urban climate context within Milan (Italy), processing data from Milano via Juvara station. The findings show that sensitive groups were at risk of high temperature and pollution separately during 26% and 29% of summer and mid-season hours, respectively, while multi-hazard risk would arise during 10.93% of summer and mid-season hours, concentrated mainly between 14:00 and 20:00.

The approach to the built environment as a container of human activities has been severely outdated with a new vision that puts the user, his/her well-being and his/her experience, at the centre, shifting from the old “building as a product” to the newer “building as a service” and the newest “building as an experience” concept. Nevertheless, the sick building syndrome, mainly documented in office buildings, is a widespread problem in many public buildings and, among these, in educational facilities. In these categories we can list school and university buildings where the users are not only workers but also students, sharing anyway the same problem: an artificial environment which is not supporting their productivity in the office or learning performance in the classrooms. Carbon dioxide concentration, due to the natural human breathing process, is a diffuse parameter strongly influencing the indoor air quality and, thus, the users’ wellbeing. An acceptance threshold of 1000 ppm in indoor spaces has been derived for this pollutant. Although allowed, the CO2 concentration in indoor space shouldn’t reach this threshold because in such a condition a 11–23% reduction of the users’ cognitive performance has been measured and, when the level increases reaching 2500 ppm, the reported drop is dramatic (44–94%). Extensive researches set the optimal threshold at 600 ppm, a level defined as fresh air. Then, how is it possible to encompass a natural process as CO2 production by humans? Basically with the correct ventilation rate, depending on room geometry, number of people and ventilation system (i.e. natural or mechanical or mixed). A modulating mechanical ventilation system could adapt the ventilation rate according to people density and changing indoor conditions nevertheless many existing buildings have outdated systems not providing this option. Sometimes their air handling units (AHUs) have few regulation options to control the parameters and tuning procedures during the building lifecycle are required. CO2 concentration is convenient to measure indoor air quality because it is easily quantifiable through a sensors network. Thus, it may be adopted as an indicator to assess suitable indoor conditions to human activities and used to trigger manual or automatic procedures to preserve wellbeing thresholds. The article presents a research work depicting the integration of indoor air quality data gathered by internet of things (IoT) sensors to activate the control of the indoor conditions according to the occupancy rate in the educational building eLUX lab, located in the Smart Campus of the University of Brescia. The challenge is to directly regulate the heating, ventilation and air conditioning (HVAC) systems and define opening/closing patterns for windows to improve the indoor air quality and protect the learning performance of users in dynamic use conditions. The embraced methodology suggests the training of an artificial neural network (ANN) with the actual monitored data and to trigger the ventilation rate control through an IoT communication protocol.

The current perspective about urban development expects 70% of energy consumption will be concentrated in the cities in 2050. In addition, a growing density of people in the urban context leads to the need for increased security and safety for citizens, which imply a better lighting infrastructure. Smart solutions are required to optimize the corresponding energy effort. In developing countries, the cities&rsquo, lighting is limited and the lighting world map is strongly significant about the urban density of the different areas. Nevertheless, in territories where the illumination level is particularly high, such as urban contexts, the conditions are not homogenous at the microscale level and the perceived security is affected by artificial urban lighting. As an example, 27.2% of the families living in the city of Milan, ombardy Region, Italy, consider critical the conditions of lighting in the city during the night, although the region has diffused infrastructure. The paper aims to provide a local illuminance geographic information system (GIS) mapping at the neighborhood level that can be extended to the urban context. Such an approach could unveil the need to increase lighting to enhance the perceived safety and security for the citizens and promote a higher quality of life in the smart city. Lighting mapping can be matched with car accident mapping of cities and could be extended to perceived security among pedestrians in urban roads and green areas, also related to degradation signs of the built environment. In addition, such an approach could open new scenarios to the adaptive street lighting control used to reduce the energy consumption in a smart city: the perceived security of an area could be used as an additional index to be considered during the modulation of the level of the luminosity of street lighting. An example of a measurement set-up is described and tested at the district level to define how to implement an extensive monitoring campaign based on an extended research schema.

Digital Asset Management is a key discipline enabling a sustainable and high-quality built environment. The physical asset is nowadays more and more integrated within the digital environment, therefore it produces a great amount of information during its life cycle. This information should be used to improve process management during the use phase of the asset, according to a servitised and cross-disciplinary approach. Accordingly, a methodological framework for asset management business processes reengineering is here presented. Through the application of the proposed set methods and procedures, it is possible to leverage innovative Information and Communication technologies (ICTs) for the development of improved information management practices in digital built environment management. The case studies developed demonstrated the possibility to effectively implement innovative Digital Asset Management processes and address different core areas of the discipline.

Regeneration of existing buildings and abandoned areas is a governmental and European key objective. This research focuses on Poveglia: an abandoned island in the Venetian lagoon, Italy. The underpinning idea concerns the upgrade of Poveglia to a zero energy Cognitive University Campus, connected to other educational institutions in the lagoon. The vision aims at reusing abandoned islands combining architecture, learning environments and digital networks, to demonstrate the potential of new technologies for the transformation of abandoned areas. The case study demonstrates the effectiveness of employing improved and revised systems and tools, developed in eLUX lab at the University of Brescia, for the regeneration of the island. The inclusion of the island in a smart campus network results in triggering positive synergies both enhancing energy efficiency and the learning environment. A further development concerns the setup of strategies for historical preservation, energy efficiency and renewable energy production.

Climate change and the request for better assets’ energy performances are forcing the construction sector towards passive or even active buildings: assets with a positive balance between energy production and consumption. Building insulations play a crucial role in reducing energy demand. One of the most used technology to insulate new and refurbished buildings are the External Thermal Insulation Composite Systems (ETICS). Despite their thermal advantages, low cost, and ease of application, these systems may have serious problems if not well designed. Cracks on the coating layer are among the most common pathologies during ETICS service life. They have been studied using a Finite Element Method (FEM) analysis on two different types of ETICS in order to understand how cracks and the mechanical characteristics of materials used in the ETICS are related. The results show that errors during ETICS installation impacts on cracks occurrence as much as the mechanical performances of some layers. The FEM analysis have been complemented by the experimental study of the mechanical behavior of the coating layer under the effect of rainwater, in order to better understand ETICS behavior over their service life.

Regeneration of existing buildings and abandoned areas is a governmental and European key objective. This research focuses on Poveglia: an abandoned island in the Venetian lagoon, Italy. The underpinning idea concerns the upgrade of Poveglia to a zero energy Cognitive University Campus, connected to other educational institutions in the lagoon. The vision aims at reusing abandoned islands combining architecture, learning environments and digital networks, to demonstrate the potential of new technologies for the transformation of abandoned areas. The case study demonstrates the effectiveness of employing improved and revised systems and tools, developed in eLUX lab at the University of Brescia, for the regeneration of the island. The inclusion of the island in a smart campus network results in triggering positive synergies both enhancing energy efficiency and the learning environment. A further development concerns the setup of strategies for historical preservation, energy efficiency and renewable energy production.

This chapter summarizes a long list of research activities aimed at defining a method to assess the retrofit potential of school buildings, based on maintenance needs, energy-saving potential, and the life cycle cost of the retrofitted building. New concepts are introduced as the gained comfort cost (GCC) as well as new methods are suggested as a probabilistic approach to describe users’ behavior. Moreover, innovative methods as artificial neural networks have been employed to predict school buildings’ energy performances. The GCC is a new key performance indicator employed to compare different retrofit strategies, focusing on a single classroom. Furthermore, the retrofit potential is evaluated also for the whole school building, exploiting building information modelling (BIM) to collect and transfer information to the building energy model (BEM). This method to analyze energy savings associated with the retrofit of a school building is combined with a method to manage and forecast the running costs of building stocks. The cost forecasting method has been validated through 11 case studies. Eventually, the scale is widened to all the school buildings in Regione Lombardia and the potential energy savings are computed by artificial neural networks (ANN) and Geographical Information Systems (GIS). These methods allow to evaluate energy retrofit potential of school buildings and their life cycle costs at different scales of intervention, from the single classroom to all the buildings in a region, allowing the public decision-maker to choose the best policy for retrofitting his school building stock.

The digital transformation in management of the built environment is more and more evident. While the benefits of location data, from Building Information Modelling or Geographical Information Systems, have been explored separately, their combination - GeoBIM - in asset management has never been explored. Data collection for condition assessment is challenging due to quantity, types, frequency and quality of data. We first describe the opportunities and challenges of GeoBIM for condition assessment. The theoretical approach is then validated developing an integrated GeoBIM model of the digital built environment, for a neighbourhood in Milan, Italy. Data are collected, linked, processed and analysed, through multiple software platforms, providing relevant information for asset management decision making. Good results are achieved in rapid massive data collection, improved visualisation, and analysis. While further testing and development is required, the case study outcomes demonstrated the innovation and the mid-term service-oriented potential of the proposed approach.

Purpose This paper provides a critical review of developments in the adaptability of buildings. The purpose of this paper is to determine the current “state-of-the-art”, describe current thinking and trends in research and practice, and identify issues and gaps that further research can address. It provides a basis for a scientific and practical understanding of the interdependencies across different design criterion. This paper increases the awareness of architects, engineers, clients and users on the importance of adaptability and its role in lowering impacts over the lifecycle of buildings as part of the infrastructure system. Design/methodology/approach This paper draws mainly from the literature as its source of evidence. These were identified from established databases and search engines (e.g. Scopus, ISI Web of Knowledge and Google Scholar) using keywords such as adaptability, adaptable, adaptation, and flexibility. Over 80 sources including books, journal papers, conference proceedings, research reports and doctoral theses covering the period 1990 to 2017 were reviewed and categorised. An inductive approach was used to critically review and categorise these publications and develop a framework for analysis. Findings The concept of adaptability includes many dimensions which can broadly fall into two categories: changes to buildings and user adaptations to buildings. However, previous research has mostly focussed on the former, with many attempts to identify building attributes that facilitate adaptability, and some considerations for its assessment. Key areas that have not been adequately addressed and which require further research include: user/occupant adaptations, cost, benefits and implications of various adaptability measures, and the development of a standardised assessment methodology that could aid in decision making in the design stage of buildings. Research limitations/implications The adaptability strategies considered in this review focussed mainly on building components and systems, and did not include the contribution of intelligent and smart/biological systems. The coverage is further limited in scope due to the period considered (1990-2017) and the exclusion of terms such as “retrofit” and “refurbishment” from the review. However, the findings provide a solid basis for further research in the areas identified above. It identifies research issues and gaps in knowledge between the defined needs and current state-of-the-art on adaptive building for both research and practice. Originality/value This paper is a review of research into a highly topical subject, given the acknowledged need to adapt buildings over their lifecycle to environmental, economic or social changes. It provides further insights on the dimensions of adaptability and identifies areas for further research that will contribute to the development of robust tools for the assessment of building adaptability, which will enhance the decision-making process of building design and the development of a more sustainable built environment.

Purpose The aim of this research is to give to construction industry stakeholders some Key Performance Indicators (KPIs) able to help them making the best decisions when acquiring, operating, maintaining and repairing a building. These KPIs are intended to be included inside a Building Condition Assessment procedure developed by the authors. Approach This work is mainly based on two types of KPIs: one, here called Technical index, to assess building degradation and maintenance, so to have a measure of how the asset is getting older; and another, here called Documents index, to measure the quality and quantity of available building documents and thus to know if the building fulfils its legal requirements. Findings The proposed KPIs give a picture of the asset current condition, a measure of how it is maintained, the list of its pathologies and also an indication of missing documents. Research limitations The KPIs developed are meant to help survey an asset with only visual inspections. In case one or more serious problem are detected, a specific analysis may be required, no matter the final value of the KPIs. Practical implications The knowledge about built assets given by these KPIs will help stakeholders in making the best decision when operating or deciding to buy an asset. Originality KPIs and Building Condition Assessment procedure are the outcome of an original research that had the purpose of developing instruments for a reliable but quick evaluation of assets condition, to be performed before acquiring them or making major decisions about their refurbishment.

Measurement of performances in asset management is an enabling factor to define an effective strategy. The scope of this research concerns the investigation on the Facility Condition Index (FCI): one of the most common metrics related to the built environment. The research aims at providing a critical review concerning the FCI and its use. The research methodology has been developed according to three steps: bibliographic research, bibliometric analysis and critical review. Most of the sources agree that FCI provides a reliable economic measurement of maintenance needs, though it must be tied to a strong condition assessment methodology. Moreover, different methodologies for computation of the deferred maintenance and the current replacement value can be found in literature and many researchers emphasised the limits of FCI when dealing with components characterised by a considerable difference in replacement costs. In conclusion, FCI is included in the wider framework of asset management business process.

Effective Facility Management services are a key issue for a high-quality built environment, both in the public and private sectors. Within this context, an information management framework for the use phase of assets has been developed with the aim to streamline real estate management processes. The framework allows users to access and update at any time technical, administrative and maintenance data both at the building and at the district level. This allows for the integrated management of physical assets and enhances the risk prevention strategy.

Available data about asset condition and performances can be conveyed into different Key Performance Indicators (KPIs). Many KPIs measuring technical, functional and economic/financial asset performances can be found in literature. Nevertheless, they are often strictly related to a specific scope, thus they provide an incomplete depiction of the whole assets performances. The objective of this research is to provide facility managers and asset owners with an easy instrument to prioritize maintenance. In order to reduce costs related to its use, the instrument, developed in the form of a Decision Support System (DSS), is based on existing and reliable performances metrics and leverages new technologies like Building Information Modelling (BIM). Accordingly, the Facility Condition Index (FCI) is combined with the D index, a KPI related to the age of building components, developed by the authors. The joint use of the FCI and the D index, allows facility managers to make more conscious decisions. The proposed DSS helps in the definition of the best maintenance plan, providing a ranking of building components which require more urgent maintenance interventions. Although the DSS should be tested measuring its ability to preserve buildings and their performances on a long term, the first results are positive, as confirmed by the application to a case study on an office building in Italy. Moreover, the usability of the instrument has been appreciated by the users in a medium size Italian company.

The adaptability of buildings addresses the responses of buildings to the changing needs of owners/users and the demands of external factors, over their lifecycle. An understanding of these changes is therefore key to the creation of adaptable buildings. This paper reports research which was aimed at modelling building changes to better understand the challenges for their adaptability. An in-depth study of the changes in selected case studies was conducted to understand the nature, characteristics, and implications of these changes for buildings and their ability to adapt. The findings from these case studies were analyzed against theoretical models reported in the literature on change and adaptability. As a result, a model was developed that identified and categorized a wide spectrum of changes to the building fabric within the broad remit of adaptability that are triggered by many factors, which are sometimes external to the building or organization. In the cases investigated, it was found that the factors that lead to actual changes to buildings were not necessarily due to the ease of making those changes, but rather the organizational will and means to make the changes. Similarly, changes were made not because the building systems were obsolete, but because of non-building factors. The timings of changes therefore did not correspond to the assumed lifespan models of different building layers, suggesting that a new way of predicting and/or categorizing building changes is needed. Furthermore, the interrelationships and nature of the triggers for change suggest that the adaptability of buildings is not just about building systems, but also about non-building factors. Thus, the further exploration of non-building triggers and enablers for change using the developed model presented here, will further enhance the creation of more adaptable buildings.

Prioritization of Operation and Maintenance (O&M) and renewal interventions is a key issue for policy making at the urban level. This paper aims at involving citizens in the urban degradation survey process, in order to support strategic decisions of the public administration, through a streamlined process. This contribution belongs to a wider research, aiming at providing a more inclusive approach also in design and use phase of public spaces and assets. Therefore, it has been developed a web-based decision making platform fostering community participation, conceived as a modular system involving both technical surveys on physical urban degradation and perceived urban degradation assessment provided by citizens. The scope of the research concerns the involvement of citizens in urban O&M and regeneration prioritization, through advanced IT tools. The methodology exploits the functionalities of web-based survey tools feeding a digital district model: a modular information system employed as the participation platform among different stakeholders involved in the prioritization of operations in the urban environment. Data are stored and managed thanks to a Data Base Management Systems (DBMS) jointly with Geographical Information Systems (GIS). Outputs are thematic maps, used by the public administration to make strategic decisions on how to invest for improvement of urban deficiencies. In this paper is presented the possibility to integrate the technical survey module, with the community involvement module. In conclusion can be stated that, the proposed methodology is a useful framework for implementation of strategies for improvement of urban quality, involving citizens' participation. Moreover, partially automates andfosters the selection of most relevant precincts where to implement interventions.

Urban Facility Management (UFM) is an effective framework to support management of the complexity of the city environment, to optimise resources, enhance quality and guarantee citizenOs safety. Nevertheless, it can be observed a lack of standardised objective tools for urban quality assessment of existing neighbourhoods. In this paper, a procedure for urban sustainability assessment exploiting Geographic Information Systems (GIS), Database Management Systems (DBMS) and mobile devices is presented. A system called District Information Model (DIM) is employed for this purpose, in order to detect the anomalies and enable evaluation of urban componentsO average conservation status. The results of the survey supported the implementation of an UFM service in an Italian Neighbourhood, near Milan. UFM can be intended as a framework to pursuit urban sustainability objectives, in the wider context of Europe 2020 strategy. Thus, the process leading to urban sustainability reporting through the DIM has been investigated, through a case study research. In conclusion, the system can be seen as a risk prevention tool both for citizens and professionals working for management of built environment; and as a sustainability assessment tool compliant with most recognised Neighbourhood Sustainability Assessment (NSA) protocols.

A National Institute of Standards and Technology (NIST) study shows that all stakeholders in the capital facilities industry waste a huge amount of money looking for, validating, and/or recreating facility information that should be readily available. The total cost of these activities within the capital facilities industries in the United States was conservatively estimated at $15.8 billion in 2002, with two-thirds of that cost occurring during the facilities’ operations and maintenance phase. This is confirmed by the International Facility Management Association (IFMA) Maintenance Survey in 2009, which states that the loss of information generates an added cost of 12.4% of total annual mean OM (2) management of mechanical equipment status; (3) qualitative condition assessment of buildings; and (4) appraisal of the service life of building components and systems according to ISO 15686-8. These four tools allow to compute and control some key indicators of the asset, with also the possibility to compare them inside a portfolio. This paper presents the four tools, paying also attention to the workflows to be followed and to the different data needs of the stakeholders involved.

The increased awareness on sustainability matters is contributing to the evolution of energy and environmental policies for the building sector at the EU level, oriented toward resource efficiency. There exist today several possible strategies to model building performance through the life cycle. The increase of available computational capacity and of data acquisition capability is opening new scenarios for practical applications, which can contribute to the reduction of the gap usually encountered between simulated and measured energy performance. This article aims to investigate an approach for probabilistic building performance simulation to be used across life cycle phases, employing reduced-order models for performance monitoring and energy management. The workflow proposed aims to establish a continuity among design and operation phases. Design phase simulation is generally subject to relevant temporal and economic constraints and a successful workflow should incorporate elements from current design practices but should also add new features, which have to be reasonably automated to reduce additional effort. Therefore, the workflow proposed is automated and tested for robustness using Monte Carlo technique. In the design phase, the approach can be used for identifying probabilistic performance bounds suitable for risk analysis in energy efficiency investments, employing cost-optimal or life cycle cost accounting methodologies. In the operation phase, it can be used for performance monitoring and energy management based on daily energy consumption analysis, similarly to other multivariate regression-based methods at the state of the art, addressing the problem of maintaining energy consumption and related costs constantly under control.