Purpose The purpose of this paper is to present a model for building information modelling (BIM) implementation at small and medium-sized construction contractor organisations (SMOs). The proposed BIM adoption model assesses BIM implementation benefits, costs and challenges faced by SMOs. Correlation between BIM adoption in SMOs and the associated impacting factors, including knowledge support and BIM adoption motivation, is captured through the model. Design/methodology/approach A literature review of BIM adoption in construction was first presented. Research data, collected from 80 SMOs in Australia through a conducted survey, are then analysed. Descriptive analysis and structural equation modelling were used to investigate SMOs’ understanding of BIM, and to qualify the correlations among the proposed latent variables impacting BIM implementation at SMOs, respectively. Additionally, this study used χ2 test to compare differences between BIM users and non-BIM users regarding BIM understanding, interested applications and attitudes towards implementation benefits and challenges. Findings Potential benefits associated with BIM implementation are a major motivation factor when it comes to BIM adoption at SMOs. In addition, existing staff’s capability in using BIM tools positively affects the establishment of an organisational knowledge-support system, which determines the decision of adopting BIM eventually. Ultimately, there is a need for further emphasis on staff engagement in the implementation process. Research limitations/implications The results presented in this paper are applicable to SMOs in the building sector of construction. BIM implementation at organisations involved in non-building activities, including civil works and infrastructure, needs to be assessed in the future. Practical implications The results indicate that rather than placing the focus mainly on benefits of BIM implementation, successful implementation of BIM in practice requires adequate effort to assess implementation problems, establish knowledge support and engage staff in using BIM. Originality/value Results of this study provide an insight into the adoption challenges of BIM in SMOs, given that the focus of previous studies has been mostly placed on BIM adoption in architectural firms and large contractors.

Annually, millions of dollars are spent to carry out defect detection in key infrastructure including roads, bridges, and buildings. The aftermath of natural disasters like floods and earthquakes leads to severe damage to the urban infrastructure. Maintenance operations that follow for the damaged infrastructure often involve a visual inspection and assessment of their state to ensure their functional and physical integrity. Such damage may appear in the form of minor or major cracks, which gradually spread, leading to ultimate collapse or destruction of the structure. Crack detection is a very laborious task if performed via manual visual inspection. Many infrastructure elements need to be checked regularly and it is therefore not feasible as it will require significant human resources. This may also result in cases where cracks go undetected. A need, therefore, exists for performing automatic defect detection in infrastructure to ensure its effectiveness and reliability. Using image processing techniques, the captured or scanned images of the infrastructure parts can be analyzed to identify any possible defects. Apart from image processing, machine learning methods are being increasingly applied to ensure better performance outcomes and robustness in crack detection. This paper provides a review of image-based crack detection techniques which implement image processing and/or machine learning. A total of 30 research articles have been collected for the review which is published in top tier journals and conferences in the past decade. A comprehensive analysis and comparison of these methods are performed to highlight the most promising automated approaches for crack detection.

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Since the 1980s, smart buildings have aroused the interest of researchers. However, there is still no consensus on what the intelligence of a building is, and what enhances that intelligence. The purpose of this paper is to identify and correlate the main drivers and systems of smart buildings, by associating them with the main beneficiaries: users, owners, and the environment. To identify the main drivers and systems of these buildings, we carried out a comprehensive, detailed, and interpretative literature search. From the selected articles, we sorted the information, extracted the main concepts and knowledge, and, finally, identified the set of potential drivers and systems. Results showed eleven drivers and eight systems, and these can be enhanced by more than one driver. By analyzing the main beneficiaries, we grouped the drivers into three categories: users, owners, and the environment. Given the lack of consensus on the key drivers that make buildings smarter, this article contributes to filling this gap by identifying them, together with the key systems. It is also relevant for detecting the relationships between drivers and systems, and pointing out which drivers have the greatest potential to affect a particular system, keeping in mind the main beneficiary.