Purpose: This paper aims to investigate the integration of a quantity surveyor (QS) role and practice within the building information modelling (BIM) process to enable better implementation of five-dimensional building information modelling (5D BIM) (D is a result of the data/information dimension integrated in a BIM model). It proposes the use of a “level of development and level of detail” to provide a structured approach for QSs’ integration within the BIM process for an improved implementation of 5D BIM. Design/methodology/approach: This paper uses semi-structured interviews with QSs from academic and industry in the UK. A total of 20 face-to-face semi-structured interviews were conducted with two groups (industrial and academic) of ten participants from the West Midlands region in the UK. The interview questions have focussed on gaining perspectives on BIM, BIM and government protocols for the QS profession, expectations and challenges when implementing 5D BIM. Findings: The findings show that BIM is perceived differently, and limitation of information obtained from the 3D models in BIM reduce the reliability of 5D BIM for QSs and implementing of 5D BIM needs an understanding of BIM workflow and information requirements. Research limitations/implications: Although the findings reveal many of the complexities that face QSs within the BIM process and the shortfalls of 5D BIM technology, the results may lack generalisation. Thus, future research seeks to test the proposed framework further. Practical implications: This paper elicits implications of shortfalls that impact the implementation of 5D BIM technology and the complexities that face QSs within the BIM process. Originality/value: This paper reveals the need to understand the process of integrating stakeholders and their information requirements for better implementation of technologies within BIM.

Purpose: This paper aims to Test the capabilities/accuracies of four deep learning pre trained convolutional neural network (CNN) models to detect and classify types of highway cracks, as well as developing a new CNN model to maximize the accuracy at different learning rates. Design/methodology/approach: A sample of 4,663 images of highway cracks were collected and classified into three categories of cracks, namely, “vertical cracks,” “horizontal and vertical cracks” and “diagonal cracks,” subsequently, using “Matlab” to classify the sample to training (70%) and testing (30%) to apply the four deep learning CNN models and compute their accuracies. After that, developing a new deep learning CNN model to maximize the accuracy of detecting and classifying highway cracks and testing the accuracy using three optimization algorithms at different learning rates. Findings: The accuracies result of the four deep learning pre-trained models are above the averages between top-1 and top-5 and the accuracy of classifying and detecting the samples exceeded the top-5 accuracy for the pre-trained AlexNet model around 3% and by 0.2% for the GoogleNet model. The accurate model here is the GoogleNet model as the accuracy is 89.08% and it is higher than AlexNet by 1.26%. While the computed accuracy for the new created deep learning CNN model exceeded all pre-trained models by achieving 97.62% at a learning rate of 0.001 using Adam’s optimization algorithm. Practical implications: The created deep learning CNN model will enable users (e.g. highway agencies) to scan a long highway and detect types of cracks accurately in a very short time compared to traditional approaches. Originality/value: A new deep learning CNN-based highway cracks detection was developed based on testing four pre-trained CNN models and analyze the capabilities of each model to maximize the accuracy of the proposed CNN.

Purpose: The purpose of this paper is to investigate human behaviour under a situation of fire in high-rise residential buildings and identify the factors that motivate people to evacuate. Design/methodology/approach: A literature review was conducted to identify different factors of human behaviour during a situation of fire and identify challenges during the evacuation. Through a mixed research method approach, the paper identifies human background, experience and knowledge with fire safety. The paper discusses the challenges occupants face during evacuation based on previous evacuation experience and what occupants were doing during the fire alarm. Findings: The paper has identified the challenges and the factors that affect occupants’ decision during fire emergency in high-rise residential buildings. It is clear from the findings that occupants have limited knowledge and skills on how to deal with fire emergencies. Occupants tend to depend on other evacuation routes. Occupants tend to ignore the fire alarm and usually they investigate if it is true or false. Originality/value: The paper provides the knowledge and findings of occupants during fire emergency to fire engineers, facility managers, owners, and other professionals to assist during the design phase, and modify designs based on this findings of this research.

Given the heightened importance of revolutionising 4D BIM-based construction scheduling in modular construction, it has become vital to explore how 4D-BIM could be integrated with the lean concept. Therefore, this research aims to develop a lean-integrated process model to revolutionise the 4D BIM-based construction scheduling in modular construction projects. A case study approach was used to obtain the data. The data was obtained using semi-structured interviews with construction scheduling professionals, site observations, and extracts from the BIM model used within the selected case in the UK. Findings showed that conventional (component/object based) 4D BIM supersedes conventional scheduling methods in terms of foreseeing potential implications during design and construction. The findings also showed that lean-integrated 4D BIM in modular construction have different considerations when compared with component/object-based scheduling. A lean-integrated 4D BIM process model was developed from the analysis and it was validated using an interactive workshop with eight participants from two UK construction companies and two modular construction manufacturers. The developed process model identified a number of considerations for 4D BIM in modular projects including constructability, operations, health and safety risks and time. This study suggested the further potential of 4D BIM in scheduling for modular construction projects.

The Architecture, Engineering and Construction (AEC) sector faces severe sustainability and efficiency challenges. In recent years, various initiatives have demonstrated how artificial intelligence can effectively address these challenges and improve sustainability and efficiency in the sector. In the context of retrofit projects, there is a continual rising interest in the deployment of Artificial Intelligence (AI) techniques and applications, but the complex nature of such projects requires critical insight into data, processes, and applications so that value can be maximised. This study aims to review AI applications and techniques that have been used in the context of retrofit projects. A review of existing literature on the use of artificial intelligence in retrofit projects within the construction industry was carried out through a thematic analysis. The analysis revealed the potential advantages and difficulties associated with employing AI techniques in retrofit projects, and also identified the commonly utilised techniques, data sources, and processes involved. This study provides a pathway to realise the broad benefits of AI applications for retrofit projects. This study adds to the AI body of knowledge domain by synthesizing the state-of-the-art of AI applications for Retrofit and revealing future research opportunities in this field to enhance the sustainability and efficiency of the AEC sector.

Value management (VM) and its integration in the whole life cycle (WLC) have become huge concepts for construction projects to provide additional value of an asset for the end user or client. However, the role of VM and its integration as part of the WLC in a construction project remain reactive, and highly impacted by nature of the project, and this has become more challenging with the epidemic impact of COVID-19. This research aims to investigate the mechanisms that delivers value management as part of the “re-invent” strategy proposed by the Construction Leadership Council in the UK government to improve WLC for buildings. In addition to existing secondary data from the literature, primary data were attained using a focus group with six quantity surveyors from different cost consultancies in the UK to gather qualitative evidence using their experiences, perceptions, and key challenges they face when integrating VM. Findings revealed that value management is primarily being used as a cost-cutting tool, the majority of quantity surveyors lack knowledge of what it encompasses, hence the industry needs a more proactive strategy towards it. Analysis revealed that value management is primarily implemented as a cost-cutting solution, key stakeholders (e.g., facility managers) need to be integrated, and there is no standardised process to incorporate value management in projects. The study proposes a four-dimensional (governance and policies, sustainability, industry’s best practice, and innovation and technology) strategy to facilitate more holistic considerations of value management post COVID-19. Future work looks into evaluating the strategy proposed while acknowledging different procurement routes.