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The calculation of buildings’ carbon footprint (CFP) is an important basis for formulating energy-saving and emission-reduction plans for building. As an important building type, there is currently no model that considers the time factor to accurately calculate the CFP of hospital building throughout their life cycle. This paper aims to establish a CFP calculation model that covers the life cycle of hospital building and considers time factor.

On the basis of field and literature research, the basic framework is built using dynamic life cycle assessment (DLCA), and the gray prediction model is used to predict the future value. Finally, a CFP model covering the whole life cycle has been constructed and applied to a hospital building in China.

The results applied to the case show that the CO₂ emission in the operation stage of the hospital building is much higher than that in other stages, and the total CO₂ emission in the dynamic and static analysis operation stage accounts for 83.66% and 79.03%, respectively; the difference of annual average emission of CO₂ reached 28.33%. The research results show that DLCA is more accurate than traditional static life cycle assessment (LCA) when measuring long-term objects such as carbon emissions in the whole life cycle of hospital building.

This research established a carbon emission calculation model that covers the life cycle of hospital building and considered time factor, which enriches the research on carbon emission of hospital building, a special and extensive public building, and dynamically quantifies the resource consumption of hospital building in the life cycle. This paper provided a certain reference for the green design, energy saving, emission reduction and efficient use of hospital building, obviously, the limitation is that this model is only applicable to hospital building.

Energy analysis (EA) within a building information modelling (BIM) enables consistent data integration in central repositories and eases information exchange, reducing rework. However, data loss during information exchange from different BIM uses or disciplines is frequent. Therefore, a holistic approach for different BIM uses enables a coherent life cycle information flow. The life cycle information flow drives the reduction of data loss and model rework and enhances the seamless reuse of information. The latter requires a specification of the EA key performance indicators (KPIs) and integrating those in the process.

The paper presents a set of KPIs extracted from the developed EA process maps and interviews with expert stakeholders. These KPIs stem from the literature review and link to the benefits of EA through industry expert review. The study includes (1) development and validation of EA process maps adjusted to requirements from different stakeholders. (2) KPIs aligned with the EA process map, (3) identification of the drivers that can facilitate life cycle information exchange and (4) opportunities and obstacles for EA within BIM-enabled projects.

This paper depicts a viable alternative for EA process maps and KPIs in a BIM-enabled AEC design industry. The findings of this paper showcase the need for an EA within BIM with these KPIs integrated for a more effective process conforming to the current Open BIM Alliance guidance and contributing towards sustainable life cycle information flow.

The limitation of the research is the challenge of generalising the developed EA process maps; however, it can be adjusted to fit defined organisational use. The findings deduced from the developed EA process map only show KPIs to have the ability to facilitate adequate information flow during EA.

The AEC industry will benefit from the findings of this primary research as the industry will be able to contrast its process maps and KPIs to those developed in the paper.

This paper benefits the societal values in EA for the built environment in the design stages. The subsequent life cycle information flow will help achieve a consistent information set and decarbonised built environment.

The paper offers a practical overview of process maps and KPIs to embed EA into BIM, reducing the information loss and rework needed in the practice of this integration. The applicability of the solution is contrasted by consultation with experts and literature.

Information management workflow in building information modelling (BIM)-based collaboration is based on using a common data environment (CDE). The basic premise of a CDE is exposing all relevant data as a single source of truth and facilitating continuous collaboration between stakeholders. A multitude of tools can be used as a CDE, however, it is not clear how the tools are used or if they fulfil the users’ needs. Therefore, this paper aims to investigate current practices of using CDEs for information management during the whole built asset’s life cycle, through a state-of-the-art literature review and an empirical study.

Literature data is collected according to the PRISMA 2020 guideline for reporting systematic reviews. This paper includes 46 documents in the review and conduct a bibliometric and thematic analysis to identify the main challenges of digital information management. To understand the current practice and the views of the stakeholders using CDEs in their work, this paper used an empirical approach including semi-structured interviews with 15 BIM experts.

The results indicate that one of the major challenges of CDE adoption is project complexity and using multiple CDEs simultaneously leading to data accountability, transparency and reliability issues. To tackle those challenges, the use of novel technologies in CDE development such as blockchain could be further investigated.

The research explores the major challenges in the practical implementation of CDEs for information management. To the best of the authors’ knowledge, this is the first study on this topic combining a systematic literature review and fieldwork.

The impacts of COVID-19 on construction projects have attracted much attention in the construction management research community. Nevertheless, a systematic review of these studies is still lacking. The purpose of this paper is to systematically analyze the impacts of COVID-19 on the different stages of a project life-cycle, and comprehensively sort out the epidemic response measures adopted by project participants. In addition, the study also attempts to explore the challenges and opportunities faced by project management practitioners under the context of COVID-19.

This study comprehensively demonstrates the systematic review process of COVID-19 related research in the construction industry, systematically summarizes the research status of the impact of COVID-19 on construction projects, and defines the strategies to deal with COVID-19 in project management; and through the visualization research, determines the current key research topics and future research trends.

This study identifies 11 construction activities in the project management life cycle that are affected by COVID-19 and finds that the COVID-19 epidemic has the greatest impact on construction workers, construction standards, construction contracts and construction performance. The study further summarizes the six main epidemic countermeasures and mitigation measures taken within the construction industry following the arrival of the epidemic. In addition, the results of this study identify opportunities and future trends in intelligent construction technology, rapid manufacturing engineering and project management in the construction industry in the post-epidemic era through literature results, which also provide ideas for related research.

COVID-19 has brought severe challenges to society. It is of great significance for the future sustainable development of the construction industry to identify the impact of COVID-19 on all phases of the project and to promote the development of coping strategies by project stakeholders.

First of all, there is little study comprehensively reviewing the impacts of COVID-19 on the different stages of construction projects and the strategies to deal with the negative impacts. In addition, from a life cycle perspective, the used articles in this study were grouped into different categories based on project stages. This promotes an integrated and comprehensive understanding of historical studies. Moreover, on the basis of a comprehensive review, this paper puts forward future research directions to promote the sustainable development of the construction sector.

Crucial transition of the Indian residential building sector into a low-emission economy require an in-depth understanding of the potentials for retrofitting the existing building stock. There are, however, limited studies that have recognised the interdependencies and trade-offs in the embodied energy and life cycle impact assessment of retrofit interventions. This research appraises the life cycle assessment and embodied energy output of a residential building in India to assess the environmental implications of selected retrofit scenarios.

This study utilises a single case study building project in South India to assess the effectiveness and impact of three retrofit scenarios based on life cycle assessment (LCA) and embodied energy (EE) estimates. The LCA was conducted using SimaPro version 9.3 and with background data from Ecoinvent database version 3.81. The EE estimates were calculated using material coefficients from relevant databases in the published literature. Monte Carlo Simulation is then used to allow for uncertainties in the estimates for the scenarios.

The three key findings that materialized from the study are as follows: (1) the retrofitting of Indian residential buildings could achieve up to 20% reduction in the life cycle energy emissions, (2) the modification of the building envelope and upgrading of the building service systems could suffice in providing optimum operational energy savings, if the electricity from the grid is sourced from renewable plants, and (3) the production of LEDs and other building services systems has the highest environmental impacts across a suite of LCA indicators.

The retrofitting of residential buildings in India will lead to better and improved opportunities to meet the commitments in the Paris Climate Change Agreement and will lead to enhanced savings for building owners.

Perceived benefits of building information modelling (BIM) have been discussed for some time, but cost–benefit benchmarking has been inconsistent. The purpose of this paper is to investigate BIM feasibility and evaluate investment worth to elucidate and develop the current understanding of BIM merit. The aim of the study is to propose a research agenda towards a more holistic perspective of BIM use incorporating quantifying investment return.

An in-depth examination of research patterns has been conducted to identify challenges in the assessment of the investment value and return on investment (ROI) for BIM in the construction industry. A total of 75 research articles were considered for the final literature review. An evaluation of the literature is conducted using a combination of bibliometric analysis and systematic reviews.

This study, which analysed 75 articles, unveils key findings in quantifying BIM benefits, primarily through ROI calculation. Two major research gaps are identified: the absence of a standardized BIM ROI method and insufficient exploration of intangible benefits. Research focus varies across phases, emphasizing design and construction integration and exploring post-construction phases. The study categorizes quantifiable factors, including productivity, changes and rework reduction, requests for information reduction, schedule efficiency, safety, environmental sustainability and operations and facility management. These findings offer vital insights for researchers and practitioners, enhancing understanding of ’BIM’s financial benefits and signalling areas for further exploration in construction.

The ’study’s outcomes offer the latest insights for researchers and practitioners to create effective approaches for quantifying ’BIM’s financial benefits. Additionally, the proposed research agenda aims to improve the current limited understanding of BIM feasibility and investment worth evaluation. Results of the study could assist practitioners in overcoming limitations associated with BIM investment and economic evaluations in the construction industry.

Digital transformation (DT) innovation is a monumental contribution that has had a profound effect on several worldwide industries. The aim of this research is to evaluate the current and future trends in DT specifically focusing in construction industry.

This study adopts a qualitative analysis approach grounded on descriptive and bibliometric analyses. In total, 283 papers from Scopus between January 2015 and April 2023 were retrieved in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) review methodology. This study examines the publishing trends, most productive nation, university, publications and authors. Keyword co-occurrence analysis and thematic evolution were analyzed through Vosviewer and Biblioshiny.

The results illustrate a growing desire to use digital technologies in the construction industry, which shows the topic's power and expanding popularity. This research reveals various emerging themes based on technology usage in construction sector. Out of 14 themes, occupational health and safety, mass customization, virtual reality and artificial intelligence were identified as isolated themes. Further, this study elaborates the difficulties encountered by the construction industry while employing digital technologies and examines the interrelationships among various keywords in DT and reveals the paradoxes and hotspots.

This research adds to the body of literature as it identifies the research areas and gaps in the existing DT domain in construction industry. The integration of technology in this sector has an intense positive future vision as various subareas have immense potential for technology application.

This paper aims to develop predictive methods, based on recurrent neural networks, useful to support facility managers in building maintenance tasks, by collecting information coming from a computerized maintenance management system (CMMS).

This study applies data-driven and text-mining approaches to a CMMS data set comprising more than 14,500 end-users’ requests for corrective maintenance actions, collected over 14 months. Unidirectional long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM) recurrent neural networks are trained to predict the priority of each maintenance request and the related technical staff assignment. The data set is also used to depict an overview of corrective maintenance needs and related performances and to verify the most relevant elements in the building and how the current facility management (FM) relates to the requests.

The study shows that LSTM and Bi-LSTM recurrent neural networks can properly recognize the words contained in the requests, thus correctly and automatically assigning the priority and predicting the technical staff to assign for each end-user’s maintenance request. The obtained global accuracy is very high, reaching 93.3% for priority identification and 96.7% for technical staff assignment. Results also show the main critical building elements for maintenance requests and the related intervention timings.

This work shows that LSTM and Bi-LSTM recurrent neural networks can automate the assignment process of end-users’ maintenance requests if trained with historical CMMS data. Results are promising; however, the trained LSTM and Bi-LSTM RNN can be applied only to different hospitals adopting similar categorization.

The data-driven and text-mining approaches can be integrated into the CMMS to support corrective maintenance management by facilities management contractors, i.e. to properly and timely identify the actions to be carried out and the technical staff to assign.

The improvement of the maintenance of the health-care system is a key component of improving health service delivery. This work shows how to reduce health-care service interruptions due to maintenance needs through machine learning methods.

This study develops original methods and tools easily integrable into IT workflow systems (i.e. CMMS) in the FM field.

Building information modeling (BIM) is a striking development in the architecture, engineering and construction (AEC) industry, which provides in-depth information on different stages of the building lifecycle. Real estate valuation, as a fully interconnected field with the AEC industry, can benefit from 3D technical achievements in BIM technologies. Some studies have attempted to use BIM for real estate valuation procedures. However, there is still a limited understanding of appropriate mechanisms to utilize BIM for valuation purposes and the consequent impact that BIM can have on decreasing the existing uncertainties in the valuation methods. Therefore, the paper aims to analyze the literature on BIM for real estate valuation practices.

This paper presents a systematic review to analyze existing utilizations of BIM for real estate valuation practices, discovers the challenges, limitations and gaps of the current applications and presents potential domains for future investigations. Research was conducted on the Web of Science, Scopus and Google Scholar databases to find relevant references that could contribute to the study. A total of 52 publications including journal papers, conference papers and proceedings, book chapters and PhD and master's theses were identified and thoroughly reviewed. There was no limitation on the starting date of research, but the end date was May 2022.

Four domains of application have been identified: (1) developing machine learning-based valuation models using the variables that could directly be captured through BIM and industry foundation classes (IFC) data instances of building objects and their attributes; (2) evaluating the capacity of 3D factors extractable from BIM and 3D GIS in increasing the accuracy of existing valuation models; (3) employing BIM for accurate estimation of components of cost approach-based valuation practices; and (4) extraction of useful visual features for real estate valuation from BIM representations instead of 2D images through deep learning and computer vision.

This paper contributes to research efforts on utilization of 3D modeling in real estate valuation practices. In this regard, this paper presents a broad overview of the current applications of BIM for valuation procedures and provides potential ways forward for future investigations.

As laser scanning technology becomes readily available and affordable, there is an increasing demand of using point cloud data collected from a laser scanner to create as-built building information modeling (BIM) models for quality assessment, schedule control and energy performance within construction projects. To enhance the as-built modeling efficiency, this study explores an integrated system, called Auto-Scan-To-BIM (ASTB), with an aim to automatically generate a complete Industry Foundation Classes (IFC) model consisted of the 3D building elements for the given building based on its point cloud without requiring additional modeling tools.

ASTB has been developed with three function modules. Taking the scanned point data as input, Module 1 is built on the basis of the widely used region segmentation methodology and expanded with enhanced plane boundary line detection methods and corner recalibration algorithms. Then, Module 2 is developed with a domain knowledge-based heuristic method to analyze the features of the recognized planes, to associate them with corresponding building elements and to create BIM models. Based on the spatial relationships between these building elements, Module 3 generates a complete IFC model for the entire project compatible with any BIM software.

A case study validated the ASTB with an application with five common types of building elements (e.g. wall, floor, ceiling, window and door).

First, an integrated system, ASTB, is developed to generate a BIM model from scanned point cloud data without using additional modeling tools. Second, an enhanced plane boundary line detection method and a corner recalibration algorithm are developed in ASTB with high accuracy in obtaining the true surface planes. At last, the research contributes to develop a module, which can automatically convert the identified building elements into an IFC format based on the geometry and spatial relationships of each plan.