In the Architecture, Engineering, construction and Operations (AEcO) there is a growing interest in the use of the building Information modelling (bIm). Through integration of information and processes in a digital model, bIm can optimise resources along the lifecycle of a physical asset. Despite the potential savings are much higher in the operational phase, bIm is nowadays mostly used in design and construction stages and there are still many barriers hindering its implementation in Facility management (Fm). Its scarce integration with live data, i.e. data that changes at high frequency, can be considered one of its major limitations in Fm. The aim of this research is to overcome this limit and prove that buildings or infrastructures operations can benefit from a digital model updated with live data. The scope of the research concerns the optimisation of Fm operations. The optimisation of operations can be further enhanced by the use of maintenance smart contracts allowing a better integration between users' behaviour and maintenance implementation. In this case study research, the Image recognition (Imr), a type of Artificial Intelligence (AI), has been used to detect users' movements in an office building, providing real time occupancy data. This data has been stored in a bIm model, employed as single reliable source of information for Fm. This integration can enhance maintenance management contracts if the bIm model is coupled with a smart contract. Far from being a comprehensive case study, this research demonstrates how the transition from bIm to the Asset Information model (AIm) and, finally, to the Digital Twin (i.e. a near-real-time digital clone of a physical asset, of its conditions and processes) is desirable because of the outstanding benefits that have already been measured in other industrial sectors by applying the principles of Industry 4.0.
Artificial Intelligence (AI) has a great impact on increasing productivity and economic growth in many sectors. However, in the construction industry, AI is still limited to research and few pilot projects. This study aims to depict the current rate of AI adoption in the industry and understand the obstacles that are hindering the required changes in the companies' business models. The data are collected through a tailored questionnaire sent to experts and practitioners in the field. The results show that labourskilled shortage, data quality, cost-benefit and lack of case studies and standards have been identified as major issues. The findings help to understand the needs of construction practitioners and propose possible solutions.
EU building sector consists mainly of outdated and inefficient properties with high energy consumption. Hence, building retrofit is being emphasized as a feasible alternative for addressing existing challenges, taking lots of time, effort, resources, and expertise in its traditional form. Conventional case-based retrofit scenarios fail to deliver quick and objective solutions for massive datasets. This research benefits from Artificial Intelligence, particularly clustering techniques, to enhance strategic decision-making for building retrofit and solve the shortcomings of conventional methods. It connects the dispersed Italian databases (CENED and TABULA) and determines desired building technology and retrofit strategy to obtain an optimum energy label.
Building Information Modelling (BIM) is becoming increasingly present in every stage of assets' lifecycle. More and more, the BIM approach is related to the sustainability assessment protocols which play an important role in reducing the impact on the environment. In the design and construction phase, the products' sustainability certification is a key issue to be managed and controlled, to achieve higher efficiency in operations. Through this research, a BIM-based methodology to automate the sustainability certification process in construction phase has been developed. According to the proposed method, the contractor proposes a building component to the work supervisor, by uploading the related technical datasheet in a Common Data Environment (CDE). If the component meets the performance requirements defined for the construction stage and agreed with the involved parties (work supervisor, client and sustainability accredited professional), it is validated and uploaded in the BIM model by the BIM manager. The methodology has been tested in a case study, confirming the effectiveness of the proposed approach. However, it should be further validated. Moreover, it can be improved and a higher level of automation can be achieved, in order to cope with product dictionaries and templates under development in CEN technical committee 442.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.