Search results

Occupational Safety and Health Administration (OSHA) of the U.S. government ensures that all health and safety regulations, protecting the workers, are enforced. OSHA officers conduct inspections and assess fines for non-compliance and regulatory violations. Literature discussion on the economic impact of OSHA inspections with COVID-19 related citations for the construction sector is lacking. This study aims to investigate the relationships between the number of COVID-19 cases, construction employment and OSHA citations and it further evaluates the total and monthly predicted cost impact of OSHA citations associated with COVID-19 violations.

An application of multiple regression analysis, a supervised machine learning linear regression model, based on K-fold cross validation sampling and a probabilistic risk-based cost estimate Monte Carlo simulation were utilized to evaluate the data. The data were collected from numerous websites including OSHA, Centers for Disease Control and the World Health Organization.

The results show that as the monthly construction employment increased, there was a decrease in OSHA citations. Conversely, the cost impact of OSHA citations had a positive relationship with the number of COVID-19 cases. In addition, the monthly cost impact of OSHA COVID-19 related citations along with the total cost impact of citations were predicted and analyzed.

The application of the two models on cost analysis provides a thorough comparison of predicted and overall cost impact, which can assist the contractors to better understand the possible cost ramifications. Based on the findings, it is suggested that the contractors include contingency fees within their contracts, hire safety managers to implement specific safety protocols related to COVID-19 and request a safety action plan when qualifying their subcontractors to avoid potential fines and citations.

The paper clarifies research gaps and future directions in building information modeling (BIM) research by analyzing research trends and publication patterns. It aims to (1) systematically categorize the vast array of BIM literature into coherent main topics, (2) identify the most and least explored areas and (3) propose directions for future research based on identified research gaps.

This study uses the Latent Dirichlet Allocation (LDA) method to manage large datasets and uncover hidden patterns in academic journals and conference articles. To clarify the scholarly focus, the main topics in BIM research are categorized into three groups: (1) primary areas of focus, (2) moderately explored topics and (3) least investigated topics.

The findings revealed 10 main topics (MTs) and 57 subtopics (STs), identifying key areas such as project design and management (20%), innovative construction technology (14%) and sustainable construction/life cycle management (14%). Conversely, it also highlighted underexplored areas like Facility/safety management and urban data development, suitable for future research.

While this work provides a structured overview of the BIM domain, it reveals opportunities for further exploring the complexity of the interrelation among interdisciplinary topics.

The novelty of this study is its extensive scope, analyzing over fifteen thousand BIM articles from 2013 to 2023, which significantly expands the literature scale previously reviewed. This comprehensive approach maps BIM research trends and gaps and also shows the hierarchical trend line of publications in each main topic, setting a benchmark for future studies.

Despite the opportunities of digital twins (DTs) for smart buildings, limited research has been conducted regarding the facility management stage, and this is explained by the high complexity of accurately representing and modelling the physics behind the DTs process. This study thus organises and consolidates the fragmented literature on DTs implementation for smart buildings at the facility management stage by exploring the enablers, applications and challenges and examining the interrelationships amongst them.

A systematic literature review approach is adopted to analyse and synthesise the existing literature relating to the subject topic.

The study revealed six main categories of enablers of DTs for smart building at the facility management stage, namely perception technologies, network technologies, storage technologies, application technologies, knowledge-building and design processes. Three substantial categories of DTs application for smart buildings were revealed at the facility management stage: efficient operation and service monitoring, efficient building energy management and effective smart building maintenance. Subsequently, the top four major challenges were identified as being “lack of a systematic and comprehensive reference model”, “real-time data integration”, “the complexity and uncertainty nature of real-time data” and “real-time data visualisation”. An integrative framework is finally proposed by examining the interactive relationship amongst the enablers, the applications and the challenges.

The findings could guide facility managers/engineers to fairly understand the enablers, applications and challenges when DTs are being implemented to improve smart building performance and achieve user satisfaction at the facility management stage.

This study contributes to the knowledge body on DTs by extending the scope of the existing studies to identify the enablers and applications of DTs for smart buildings at the facility management stage and the specific challenges.