194 research outputs found
Impianti idrico-sanitari, di scarico e di raccolta delle acque nell'edilizia residenziale
No full textMonitoring Outdoor Air Quality Using Personal Device to Protect Vulnerable People
No full textWorld Health Organization considered air pollution the most dangerous threat to human health. This paper presents a novel system to mitigate risks derived from polluted air by introducing a portable device for vulnerable people able to detect hazardous pollution concentration and to suggest healthy behaviors. Along with the device, a tailored web server application, called Monitoring Outdoor Quality of Air (MOQA), is developed. The application will make the data collected from the device’s sensors more readable and will provide citizens with air quality information. That information will alert vulnerable people about air pollution hazards and let them take actions consequently. This system can reduce the health risks derived from air pollution for the weakest population in the short-term
Data-Driven Process Mining Framework for Risk Management in Construction Projects
No full textConstruction Projects are exposed to numerous risks due to their complex and uncertain nature, threatening the realization of the project objectives. However, Risk Management (RM) is a less efficient realm in the industry than other knowledge areas given the manual and time-consuming nature of its processes and reliance on experience-based subjective judgments. This research proposes a Process Mining-based framework for detecting, monitoring, and analysing risks, improving the RM processes using evidence-based event logs, such as Risk Registers and Change-Logs within previous projects' documents. Process Mining (PM) is a data- driven methodology, well established in other industries, that benefits from Artificial Intelligence(AI) to identify trends and complex patterns among event logs. It performs well while intaking large amounts of data and predicting future outputs based on historical data. Therefore, this research proposes a Bayesian Network (BN)-based Process Mining framework for graphical representation of the RM processes, intaking the conditional dependence structure between Risk variables, and continuous and automated risk identification and management. A systematic literature review on RM, PM, and AI forms the framework theoretical basis and delineates the integration areas for practical implementation. The proposed framework is applied to a small database of 20 projects as the case study, the scope of which can be tailored to the enterprise requirements. It contributes to creating a holistic theoretical foundation and practical workflow applicable to construction projects and filling the knowledge gap in inefficient and discrete conventional RM methods, which ignore the interdependencies between risk variables and assess each risk isolated
Manutenzione e durata degli edifici e degli impianti
No full textFrutto del lavoro di ricerca condotto all'interno del gruppo di lavoro CIB W080, questa pubblicazione mira a rispondere alla richiesta di informazioni sulla vita utile dei componenti proveniente dal mercato delle costruzioni in due modi: con una parte cartacea in cui sono presentati dei profili di manutenzione per famiglie di componenti edilizi ed impiantistici, e una seconda parte, in formato banca dati elettronica, in cui sono fornite informazioni circa la durata dei componenti edilizi (per un totale di oltre 300 componenti
Synthetic images generation for semantic understanding in facility management
No full textPurposeThis 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/approachThis 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. FindingsThe 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/valueThis 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
Revolutionizing Safety Practices: Integrating Neuroscience into Predictive Analytics for the Construction Site Stress Reduction
No full textThe construction industry's dynamic and hazardous work environment necessitates continuous innovation to improve safety and efficiency. Traditional safety management practices struggle to address the dynamic nature of stressors and hazards as they often rely on static procedures and outdated protocols, which are inadequate for handling the ever-changing risks and complexities of modern construction projects. This is especially important as technology advances and optimization improvements become increasingly necessary to maintain high safety standards. This research aims to develop a novel framework integrating neuroscience principles with advanced predictive safety analytics to proactively anticipate and prevent potential safety issues. To this end, the authors re-identified problems and reviewed established and emerging technologies, thereby proposing the framework focusing on customizable and adaptive integration of data from multiple sources (e.g., Internet of Things (IoT) sensors, surveillance cameras, and biometric sensors). Challenges, such as data integration complexity, privacy concerns, and user acceptance, are addressed, with an emphasis on constructing reliable and interpretable algorithmic models. The framework is expected to benefit construction managers, companies, contractors, regulatory bodies, and technology providers by facilitating more efficient construction site operations and fostering safer work environments. By utilizing neurobiological models, the framework enhances the accuracy and reliability of machine learning models in predicting safety-related incidents. This research contributes to the advancement of construction safety practices by combining neuroscience-based stress detection with predictive analytics, and finally promoting a safer and more efficient construction industry
ACCELERATED LABORATORY TEST PROCEDURES AND CORRELATION BETWEEN LABORATORY TESTS AND SERVICE LIFE DATA - STATE OF THE ART REPORT
No full textDecision Support for existing buildings: An LCC-based proposal for facade retrofitting technological choices
No full textThe goal of this paper is to present a usable and effective tool to evaluate residential façade retrofitting solutions in early stages of design, keeping into account envelope features and installation issues. Decarbonisation goals set for 2050 impose existing building stock renovation and energy retrofit. Several drivers are available in EU Countries to trigger these operations. Nonetheless, the renovation rate in EU Member States remains low: barriers to building retrofit are identified, and a main issue in this sense is the lack of use of Decision Support Systems. DSS exist but are often neglected by building designers or owners, due to different reasons. Existing methodologies do not take into account the quantity and quality of information available at the various stages of building life cycle; furthermore, they mainly focus on energy related aspects, neglecting technological and installation related factors. This paper aims at providing an LCC-based decision framework to help decision makers in early stages of design to choose the most suitable technology for building façade retrofitting. A Utility Function expressing LCC for residential building renovation is provided, focusing on façades renovation and on installation and morphology related aspects. Information and data flow through the phases is presented and discussed, showing how the proposed method can be adapted to different stages, and testing its robustness through sensitivity and uncertainty analyses. Three main categories of renovation technologies are analysed (ventilated façade, ETICS, and prefabricated solutions). The proposed method is applied to a residential case study building. The adaptability of the tool to different stages of design is discussed, and further potential applications are presented
Construction sites’ sustainability enhancement through earthworks optimization using Building Information Modelling
No full textEarthworks are the very beginning of every new construction project, if not well designed and controlled, they may cause time and cost overruns and, overall affect the sustainability of the whole project. Industry 4.0 technologies like high-precision positioning GNSS/RTK and Building Information Modelling may help improving earthworks thus reducing waste, reworks, and energy demand. A novel workflow to integrate data coming from multiple sources within a reliable, BIM-based, digital terrain model is proposed. The proposed method allows professionals, as designers and managers, to receive updated and correct data, for better decision making and, thus, for more sustainable construction processes. Side benefits of the proposed process is an improvement in workers' safety. A case study, a quarry in northern Italy, proved the usability of the method
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