Purpose: This paper aims to investigate the integration of a QS role and practice within the BIM process to enable better implementation of 5D BIM technology. It proposes the use of a 'level of development and level of detail' (LoDs) to provide a structured approach for quantity surveyors' integration within the BIM process for an improved implementation of 5D BIM technology. Design/methodology/approach: This paper uses semi-structured interviews with quantity surveyors from academic and industry in the UK. A total of 20 face-to-face semi-structured interviews with two groups (Industrial and Academic) of 10 participants from the West Midlands region in the United Kingdom. The interview questions have focused 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, inflexibility of standardising costs reduce reliability in 5D BIM and implementing of 5D BIM needs an understanding of workflow and complexity of information. Research limitations/implications: Although the findings reveal many of the complexities that face quantity surveyors within the BIM process as well as the shortfalls of 5D BIM Technology, the results may lack generalisability. 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 the face quantity surveyors 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 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.
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.
Three-dimensional (3-D) route-planning support offers a promising solution to overcome problems with wayfinding in complex indoor environments. An experiment was conducted to test the effect of 3-D route-planning support in a realistic setting, a large hospital building, during normal operation. Forty participants performed navigation tasks either with (n = 20) or without (n = 20) 3-D route-planning support. Support resulted in faster navigation, more use of artwork specifically installed to aid wayfinding, fewer navigation errors, less disorientation and less anxiety. In addition, participants used different strategies for wayfinding: without navigation support they used signs and route colour, but with navigation support they used not only the artwork, but also the existing furniture and other landmarks. The acceptance of 3-D route-planning support was high. Overall, the results support the value of 3-D route-planning support.
Purpose Building information modelling (BIM) creates a golden thread of information of the facility, which proves useful to those with the malicious intent of breaching the security of the facility. A cyber-attack incurs adverse implications for the facility and its managing organisation. Hence, this paper aims to unravel the impact of a cybersecurity breach, by developing a BIM-facilities management (FM) cybersecurity-risk-matrix to portray what a cybersecurity attack means for various working areas of FM. Design/methodology/approach This study commenced with exploring cybersecurity within various stages of a BIM project. This showcased a heightened risk of cybersecurity at the post-occupancy phase. Hence, thematic analysis of two main domains of BIM-FM and cybersecurity in the built environment led to the development of a matrix that illustrated the impact of a cybersecurity attack on a BIM-FM organisation. Findings Findings show that the existing approaches to the management of cybersecurity in BIM-FM are technology-dependent, resulting in an over-reliance on technology and a lack of cybersecurity awareness of aspects related to people and processes. This study sheds light on the criticality of cyber-risk at the post-occupancy phase, highlighting the FM areas which will be compromised as a result of a cyber-attack. Originality/value This study seeks to shift focus to the people and process aspects of cybersecurity in BIM-FM. Through discussing the interconnections between the physical and digital assets of a built facility, this study develops a cyber-risk matrix, which acts as a foundation for empirical investigations of the matter in future research.
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