1,721,022 research outputs found
Data-Driven Process Mining Framework for Risk Management in Construction Projects
Full text linkConstruction 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
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
Decision Support for existing buildings: An LCC-based proposal for facade retrofitting technological choices
Full text linkThe 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
Full text linkEarthworks 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
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
A Bibliometric Analysis on Costs Estimation of Building Retrofit
Full text linkBuildings are responsible for approximatively 40% of energy consumptions and 36% of CO2 emissions in the EU. In developed countries, any intervention carried out for buildings' sustainability improvement is related to energy retrofit. Energy retrofit can be considered as a subset of sustainability management and is one of the key issues to be taken into account for the setup of an effective asset and portfolio management strategy. Among asset management core functions, sustainability management is one of those which must be encompassed in a strategic framework for effectively reaching the goals of the organisation. Within this context, sustainability of buildings should be evaluated according to the environmental, economic and social point of view. These different issues require specific assessment methodologies and metrics. Therefore, in this article, a bibliometric analysis on costs estimation is presented, focusing on Life Cycle Costing methodology for energy retrofit interventions. Articles have been investigated through bibliometric, trend and cluster analysis on a sample of 167 articles. The research has been carried out on one of the most acknowledged databases as Scopus and allowed to identify main trends and dynamics of the scientific literature
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
Sustainability certification of construction products through BIM
Full text linkSustainability certification of construction products is a key issue to be managed and controlled during the construction phase, in order to implement design choices and reduce buildings impact on the environment. Within this context, sustainability assessment protocols play an important role, since they provide a systematic approach for the sustainability rating of the building. The aim of this research is to define a BIM-based methodology to automate the sustainability certification process in construction phase. According to the proposed methodology, the contractor proposes a building component whose technical data are uploaded to a Document Management System (DMS) used as Common Data Environment (CDE). If the component passes a set of semi-automated authorisation steps, compliant with the work supervisor's, client's, and sustainability accredited professional's needs, then it is uploaded to the BIM Model. The case study (an office building in Italy) confirmed that the proposed methodology allows to achieve a higher efficiency, minimizing the certification times and efforts. Nevertheless, this methodology should be validated in further case studies. Moreover, it may be improved and further automated to cope with product dictionaries and templates under development in CEN technical committee 442
Maintenance service optimization in smart buildings through ultrasonic sensors network
No full textOccupancy monitoring in smart buildings has great potential to improve their operational performance. One of the most common applications concerns the dynamic adaptation of indoor conditions according to the occupancy variation. However, other implementations are possible. Occupancy data could also enhance maintenance smart contracts management, especially if coupled with a contracts’ management system as blockchain through which it is possible to achieve higher reliability and trust in transactions. In this article, a methodology to monitor occupancy data with a low-cost network, composed by a set of ultrasonic sensors, is presented. To ensure the collection of consistent data, different tests were performed for defining a convenient configuration for their installation. Following the proposed methodology, gathered data are processed and stored into a digital asset model associated with the building maintenance plan. Once a predefined threshold is reached, the system triggers a maintenance alert to the contractor to activate cleaning operations. The proposed approach enables an enhancement of the automation of maintenance management operations in a cost-effective manner. However, further validation trials are required to test the flexibility of its application in different space types
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