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This study aims to present a state-of-the-art review of building information modelling (BIM) in the Swedish construction practice with a focus on wood construction. It focuses on examining the extent, maturity and actual practices of BIM in the Swedish wood construction industry, by analysing practitioners’ perspectives on the current state of BIM and its perceived benefits.

A qualitative approach was selected, given the study’s exploratory character. Initially, an extensive review was undertaken to examine the current state of BIM utilisation and its associated advantages within the construction industry. Subsequently, empirical data were acquired through semi-structured interviews featuring open-ended questions, aimed at comprehensively assessing the prevailing extent of BIM integration within the Swedish wood construction sector.

The research concluded that the wood construction industry in Sweden is shifting towards BIM on different levels, where in some cases, the level of implementation is still modest. It should be emphasised that the wood construction industry in Sweden is not realising the full potential of BIM. The industry is still using a combination of BIM and traditional methods, thus, limiting the benefits that full BIM implementation could offer the industry.

This study provided empirical evidence on the current perceptions and state of practice of the Swedish wood construction industry regarding BIM maturity.

Predictive digital twin technology, which amalgamates digital twins (DT), the internet of Things (IoT) and artificial intelligence (AI) for data collection, simulation and predictive purposes, has demonstrated its effectiveness across a wide array of industries. Nonetheless, there is a conspicuous lack of comprehensive research in the built environment domain. This study endeavours to fill this void by exploring and analysing the capabilities of individual technologies to better understand and develop successful integration use cases.

This study uses a mixed literature review approach, which involves using bibliometric techniques as well as thematic and critical assessments of 137 relevant academic papers. Three separate lists were created using the Scopus database, covering AI and IoT, as well as DT, since AI and IoT are crucial in creating predictive DT. Clear criteria were applied to create the three lists, including limiting the results to only Q1 journals and English publications from 2019 to 2023, in order to include the most recent and highest quality publications. The collected data for the three technologies was analysed using the bibliometric package in R Studio.

Findings reveal asymmetric attention to various components of the predictive digital twin’s system. There is a relatively greater body of research on IoT and DT, representing 43 and 47%, respectively. In contrast, direct research on the use of AI for net-zero solutions constitutes only 10%. Similarly, the findings underscore the necessity of integrating these three technologies to develop predictive digital twin solutions for carbon emission prediction.

The results indicate that there is a clear need for more case studies investigating the use of large-scale IoT networks to collect carbon data from buildings and construction sites. Furthermore, the development of advanced and precise AI models is imperative for predicting the production of renewable energy sources and the demand for housing.

This paper makes a significant contribution to the field by providing a strong theoretical foundation. It also serves as a catalyst for future research within this domain. For practitioners and policymakers, this paper offers a reliable point of reference.

While stress, anxiety and depression rank as the second leading cause of work-related ill health in the UK construction sector, there exists a scarcity of empirical studies explicitly focused on investigating the sources of occupational stress among construction workers and professionals at both the construction project and supply chain levels. This study seeks to identify and investigate the primary stressors (sources of stress) in UK construction projects and to propose effective strategies for preventing or reducing stress in this context.

The study adopted a qualitative multi-methods research approach, comprising the use of a comprehensive literature review, case study interviews and a focus group. It utilised an integrated deductive-inductive approach theory building using NVivo software. In total, 19 in-depth interviews were conducted as part of the case-study with a well-rounded sample of construction professionals and trade supervisors, followed by a focus group with 12 policy influencers and sector stakeholders to evaluate the quality and transferability of the findings of the study.

The results reveal seven main stressors and 35 influencing factors within these 7 areas of stress in a UK construction project, with “workflow interruptions” emerging as the predominant stressor. In addition, the results of the focus-group, which was conducted with a sample of 12 prominent industry experts and policy influencers, indicate that the findings of the case study are transferrable and could be applicable to other construction projects and contexts. It is, therefore, recommended that these potential stressors be addressed by the project team as early as possible in construction projects. Additionally, the study sheds empirical light on the limitations of the critical path method and identifies “inclusive and collaborative planning” as a proactive strategy for stress prevention and/or reduction in construction projects.

The findings of this study are mainly based on the perspectives of construction professionals at managerial and supervisory levels. It is, therefore, suggested that future studies are designed to focus on capturing the experiences and opinions of construction workers/operatives on the site.

The findings from this study have the potential to assist decision-makers in the prevention of stress within construction projects, ultimately enhancing workforce performance. It is suggested that the findings could be adapted for use as Construction Supply Chain Management Standards to improve occupational stress management and productivity in construction projects. The study also provides decision-makers and practitioners with a conceptual framework that includes a list of effective strategies for stress prevention or reduction at both project and organisational levels. It also contributes to practice by offering novel ideas for incorporating occupational stress and mental health considerations into production planning and control processes in construction.

To the best of the authors’ knowledge, this is the first, or one of the very few studies, to explore the concept of occupational stress in construction at the project and supply chain levels. It is also the first study to reveal “workflow” as a predominant stressor in construction projects. It is, therefore, suggested that both academic and industry efforts should focus on finding innovative ways to enhance workflow and collaboration in construction projects, to improve the productivity, health and well-being of their workforce and supply chain. Further, it is suggested that policymakers should consider the potential for incorporating “workflow” into the HSE's Management Standards for stress prevention and management.

The purpose of this paper was to study the possibility of using smartphone roughness measurements for developing pavement roughness regression models as a function of pavement age, traffic loading and traffic volume variables. Also, the effects of patching and pavement distresses on pavement roughness were investigated. The work focused on establishing pavement roughness prediction models and applying these models to pavement management systems (PMS) to help decision-makers choose the best maintenance and rehabilitation (M&R) options by using cost-effective methods.

Signal processing techniques including filtering and processing techniques were used to obtain the International Roughness Index (IRI) from raw acceleration data collected from smartphone accelerometer sensors. The obtained IRI values were inputted as a dependent variable in analytical regression models as well as several independent variables with proper transformations.

According to the study results, several regression models were developed with a big variation in the coefficients of determination (R²). However, the best models included pavement age, accumulated traffic volume (∑TV) and construction quality factor (CQF) with R² equal to 0.63. It was also found that the effects of pavement distresses and patching was significant at a-level < 0.05. The patching effect on pavement roughness was found higher than the effect of other pavement distresses.

The presented results and methods in this paper could be used in the future predictions of pavement roughness and help the decision-makers to estimate M&R needs. The work focused on establishing IRI prediction models and applying these models to the PMS to help decision-makers choose the best M & R options.

To develop sound pavement roughness models, it is essential to collect roughness data using automated procedures. However, applying these procedures in developing countries faces several difficulties such as the high price and operation costs of roughness equipment and lack of technical experience. The advantage of using IRI values taken from smartphones is that the roughness evaluation survey may be expanded to cover the full road network at a cheaper cost than with automated instruments. Therefore, if the roughness survey covers more roads, the prediction model’s accuracy will be improved.