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Lukman Akanbi

Researcher at University of the West of England

Publications -  32
Citations -  1222

Lukman Akanbi is an academic researcher from University of the West of England. The author has contributed to research in topics: Computer science & Building design. The author has an hindex of 11, co-authored 27 publications receiving 424 citations. Previous affiliations of Lukman Akanbi include University of the West & Obafemi Awolowo University.

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Robotics and automated systems in construction: Understanding industry-specific challenges for adoption

TL;DR: In this article, the authors present an investigation into the industry-specific factors that limit the adoption of robotics and automated systems in the construction industry, focusing on three focus groups with 28 experts and an online questionnaire were conducted.
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Salvaging building materials in a circular economy: A BIM-based whole-life performance estimator

TL;DR: In this paper, the authors developed a BIM-based Whole-life Performance Estimator (BWPE) for appraising the salvage performance of structural components of buildings right from the design stage.
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Cloud computing in construction industry: Use cases, benefits and challenges

TL;DR: It is found that cloud computing is an innovation delivery enabler for other emerging technologies (building information modelling, internet of things, virtual reality, augmented reality, big data analytics) in the construction industry.
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Disassembly and deconstruction analytics system (D-DAS) for construction in a circular economy

TL;DR: The results show that building information modelling software capabilities can be extended to provide a platform for assessing the performance of building designs in respect of the circular economy principle of keeping the embodied energy of materials perpetually in an economy.
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Artificial intelligence in the construction industry: A review of present status, opportunities and future challenges

TL;DR: A critical review of available literature on AI applications in the construction industry such as activity monitoring, risk management, resource and waste optimisation was conducted in this paper, where the opportunities and challenges of AI applications were identified and presented in this study.