1,721,049 research outputs found
Life Cycle Assessment through BIM-based advanced Calculation Virtual Environment workflows
Full text linkThe current built stock in Europe is large and energetically inefficient due to material decay caused by lifelong use and lack of maintenance, combined. New buildings are commissioned and delivered everyday with an environmental cost, calculated using the Life Cycle Assessment methodology. The LCA is now widely requested for new assets, while existing ones are left for demolition and consequent carbon emissions released into the atmosphere, increasing the environmental crisis. In an effort to increase built stock's energy efficiency rates and decrease carbon emission, the recovery of these assets is depicted as the strategic next step for the construction sector. Both goals are achieved using data generated by dedicated construction software and processed in Calculation Virtual Environment, according to the specific needed data. The use of BIM for refurbishment design projects allows virtual construction separated by phases to simulate existing conditions of the envelope and its improvement with insulation material options. The use of one single file with clear phase definitions ensures data extraction and transference for all material options. The process is not entirely automated nevertheless it allows to save, use and share data whenever needed. The connection between data sources and databases in the cloud provides timesaving and regular updates. The research findings demonstrate the positive outcomes of modeling existing structures for energy simulation oriented to use in LCA, increasing the potential of BIM use in sensitive constructions while delivering appropriate results based on model and enriched geometry, with cost evaluation potential enabling scenario comparison for better decision making
Long-Term Techno-Economic Performance Monitoring to Promote Built Environment Decarbonisation and Digital Transformation—A Case Study
Full text linkBuildings’ 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
Energy and comfort management of the educational spaces through IoT network for IAQ assessment in the eLUX lab
Full text linkIoT networks for data gathering in the buildings allow to control and manage the operational phase of the systems for ventilation and IAQ, optimizing the energy flows and the indoor comfort conditions. The concept of Cognitive Building steers the implementation of such networks in the assets considering the sensors as scattered systems to inform and actuate the adaptation strategies which are crucial when variables have to be included in the process management. Variables as weather, occupancy flows during the day, energy production by renewable energies, energy storage strategies, affect the indoor conditions, the rate of use of the HVAC systems and the energy management of the used/storage resources. The eLUX lab at the Smart Campus of the University of Brescia is a pilot building in the field and it has been monitoring since 2017. The indoor conditions monitoring could unveil critical situations defined by temperature, humidity and indoor air quality (IAQ) in the educational spaces and envisage strategies and scenarios related to energy demand defined by the occupancy stream. The IoT network collects data about indoor air quality in the different spaces and it is used to verify and increase the accuracy on occupancy estimation. The HVAC management referred to the effective occupancy can enable an energy management process based on user-centred approach empowering an increment of the comfort hours facing critical situations and it is possible to promote actuation strategies preserving energy efficiency and IAQ (e.g. increase ventilation to decrease the CO2 concentration, decrease temperature and control relative humidity in the indoor spaces by window opening or modulation of the fans and dehumidification systems activation). The educational spaces have been adopted as case studies to analyse the actual indoor conditions and come up with a detailed description of the profiles of use (i.e. occupancy, lighting, equipment, HVAC, CO2) supporting effective management policies. The paper describes the analyses on the data collected to understand when and how the indoor conditions can be improved to preserve the learning performance of the users. The research addresses one of the main topics of the eLUX living lab
IoT network-based ANN for ventilation pattern prediction and actuation to optimize IAQ in educational spaces
Full text linkNowadays, in a user centered design approach, one of the main parameters for assessing the well-being of building spaces is Indoor Air Quality (IAQ), which can assure a crucial level of comfort and optimal conditions to preserve users' productivity and cognitive performance. Research works in this direction mention that with 1000 ppm of CO2 concentration, a reduction of the users' cognitive performance about 11-23% is reported and, for a concentration of 2500 ppm, the decrease reaches 44-94% compared to the performance at 600 ppm. Consequently, a correct buildings ventilation is crucial. The use of mechanical systems seems possibly to avoid the problem but indeed the existing buildings often have outdated and not flexible systems to face changing needs. Thereby, the ventilation rates are not related to people density and the static setup of HVAC systems might be an issue to maintain an acceptable level of CO2 concentration. Moreover, in school buildings, mechanical ventilation is not diffusely adopted and insufficient rates of fresh air supplied to the classrooms are connected with inappropriate IAQ, occurrence of SBS symptoms among pupils. Current technology provides easy measurement of CO2 through dedicated sensors networks. The present research uses the pilot educational building eLUX, located in the Smart Campus of the University of Brescia, to investigate the possibility to integrate IAQ data generated by IoT sensors to improve the estimation of occupancy rate in the educational spaces. The aim is to underline the relevance of the parameter to regulate properly the HVAC systems and to define opening/closing patterns for automated windows to enhance IAQ. The data collected during the monitoring phase are useful to train an Artificial Neural Network (ANN) that through an IoT communication protocol could actuate the ventilation rate control
LCA evaluation and energy performance of a housing building in different technological scenarios
Full text linkReaching nearly Zero Energy Building (nZEB) standards through retrofit can be achieved by adopting controlled procedures and assessment tools. The main issue of operational energy consumption before and after refurbishment should be calculated with reliable predictive models to support investment payback time evaluations. For that purpose, dynamic simulation can reduce the performance gap between simulated and actual performance, however, multiple issues are involved. In the EU Directives, the nZEB framework addresses the operational consumption, which traditionally was the main portion in the building life cycle. However, where nZEB is concerned, the running energy is strongly reduced and embedded energy and disposal assume a higher contribution to the energy life cycle cost. It is worthy to note that LC-ZEBs (Life Cycle Zero Energy Buildings) have been conceptualized more than 10 years ago and the LCA (Life Cycle Assessment) approach is now integrated into the most advanced CVEs (Calculation Virtual Environments) to enable a broader evaluation of building energy during the lifespan. The paper presents LCA scenarios for a housing case study located in Italy and Norway where energy saving is regulated and suitable solutions are strongly connected to materials and energy supply contexts
Cognitive Digital Twin Framework for Life Cycle Assessment Supporting Building Sustainability
No full textIL BIM PER LE SCUOLE. Analisi del patrimonio scolastico e strategie di intervento
No full textLa modellazione informativa si sta affermando come metodologia per la gestione integrata del processo edilizio e come supporto decisionale alle scelte strategiche e alla definizione delle priorità per la programmazione degli interventi per le grandi committenze. In questo volume si focalizza l'attenzione sullo stato attuale del patrimonio scolastico italiano e le politiche attive del MIUR per l'innovazione e la riqualificazione degli edifici esistenti. Vengono di seguito definiti i parametri prestazionali per la definizione della qualità degli spazi didattici e di socialità, anche in relazione alle nuove metodologie di apprendimento e alla didattica innovativa. Viene analizzata la struttura dell'anagrafica dell'edilizia scolastica utilizzata per censire il patrimonio, proponendo un sistema di digitalizzazione che permetta non solo di recuperare le informazioni inserite in termini di dati ma anche di archiviare lo storico e tutti gli interventi futuri, creando così un archivio digitale implementabile e costantemente aggiornato. Segue la presentazione della modellazione informativa con particolare attenzione ai vantaggi che ne può trarre la Committenza, nelle varie fasi del processo. Viene quindi presentato il BIM per la gestione del patrimonio scolastico esistente, supportato dal report di un caso di studio significativo. Il volume è destinato principalmente alle Committenze, private e pubbliche, ai gestori di patrimoni immobiliari, ai progettisti, consulenti e operatori del settore delle costruzioni, che intendono indagare la metodologia BIM intesa principalmente come innovazione di processo
SCAN-TO-BIM EFFICIENT APPROACH TO EXTRACT BIM MODELS FROM HIGH PRODUCTIVE INDOOR MOBILE MAPPING SURVEY
Full text linkBuilding Information Modeling represents one of the most interesting developments in construction fields in the last 20 years. BIM process supports the creation of intelligent data that can be used throughout the life cycle of a construction project. Where a project involves a pre-existing structure, reality capture can provide the most critical information. The purpose of this paper is to describe an efficient approach to extract 3D models using high productive indoor Mobile Mapping Systems (iMMS) and an optimized scan-to-BIM workflow. The scan-to-BIM procedure allows reconstructing several elements within a digital environment preserving the features and reusing them in the development of the BIM project. The elaboration of the raw data acquired from the iMMS starts with the software HERON® Desktop where a SLAM algorithm runs and a 3D point cloud model is produced. The model is translated in the Gexcel Reconstructor® point cloud post processing software where a number of deliverables as orthophotos, blueprints and a filtered and optimized point cloud are obtained. In the proposed processing workflow, the data are introduced to Autodesk ReCap®, where the model can be edited and the final texturized point cloud model extracted. The identification and modeling of the 3D objects that compose the BIM model is realized in ClearEdge3D EdgeWiseTM and optimized in Autodesk Revit®. The data elaboration workflow implemented shows how an optimized data processing workflow allows making the scan-to-BIM procedure automatic and economically sustainable
- …
