Professor Lukman Akanbi

Professor of Applied Artificial Intelligence and Big Data Analytics

Email:: lukman.akanbi@bcu.ac.uk

Professor Lukman Akanbi is a Professor of Applied Artificial Intelligence and Big Data Analytics with over 18 years of teaching and research experience both in the UK and overseas. His research areas include Big Data Analytics and Artificial Intelligence applications in diverse areas with focus on sustainability and circular economy.

Professor Lukman has substantial research experience and track records in managing and delivering large-scale research projects. He has successfully led and participated in the delivery of a number of innovative R&D projects that include (i) £800K Innovate UK funded project on the development of an AI System for Predicting Embodied Carbon of Infrastructure Projects, (ii) £1.86million EPSRC Grant on Conversational-AI with BIM and Augmented Reality (AR) system for onsite construction assemblage (iii) £800K EPSRC/Innovate UK grant for the Development of a Cloud-based Deconstruction and Recovery Information Modelling Platform (DRIM) among others. Through these projects, Prof Lukman has collaborated effectively with many UK multinationals and SMEs that include Costain, Balfour Beatty, Winvic, Morgan Sindall, Waste Plan Solutions and Sustainable Directions Ltd among others.

Professor Lukman’s research has attracted more than £3.6million of grant funding as PI and Co-I from the UK Research and Innovation (UKRI) and Higher Education Innovation Fund (HEIF). He has published over 40 articles in high impact journals with over 2500 citations.

Areas of Expertise

Qualifications

Memberships

Research

Postgraduate Supervision

Publications

Journal articles

Owolabi, H., Oyedele, A.A., Alaka, H., Olawale, O., Akanbi, L. and S.A. Ganiyu (2024). Big data innovation and implementation in projects teams: Towards a SEM approach to conflict prevention. Information Technology and People. https://doi.org/10.1108/ITP-06-2019-0286
Olu-Ajayi, R., Alaka, H., Owolabi, H., Akanbi, L. and Ganiyu, S. (2023), Data-Driven Tools for Building Energy Consumption Prediction: A Review. Energies 16 (6), 2574. https://doi.org/10.3390/en16062574.
Saka, A.B., Oyedele, L.O., Akanbi, L.A., Ganiyu, S.A., Chan, D.W., & Bello, S.A. (2023). Conversational artificial intelligence in the AEC industry: A review of present status, challenges and opportunities. Advanced Engineering Informatics, 55, 101869. https://doi.org/10.1016/j.aei.2022.101869.
Barrera Animas, A., Oladayo Oyedele, L., Bilal, M., Dolapo Akinosho, T., Davila Delgado, J. M., & Adewale Akanbi, L. (2022). Rainfall Prediction: A Comparative Analysis of Modern Machine Learning Algorithms for Time-Series Forecasting. Machine Learning with Applications, 7, https://doi.org/10.1016/j.mlwa.2021.100204
Abioye, S. O., Oyedele, L. O., Akanbi, L., Ajayi, A., Davila Delgado, J. M., Bilal, M., Ahmed, A. (2021). Artificial intelligence in the construction industry: A review of present status, opportunities and future challenges. Journal of Building Engineering, 44, https://doi.org/10.1016/j.jobe.2021.103299
Oyedele, A., Ajayi, A., Oyedele, L. O., Delgado, J. M. D., Akanbi, L., Akinade, O., Bilal, M. (2021). Deep learning and Boosted trees for injuries prediction in power infrastructure projects. Applied Soft Computing, 110(107587), 1 - 14. https://doi.org/10.1016/j.asoc.2021.107587
Akhigbe, B. I., Munir, K., Akinade, O., Akanbi, L., & Oyedele, L. O. (2021). IoT technologies for livestock management: A review of present status, opportunities, and future trends. Big Data and Cognitive Computing, 5(1), https://doi.org/10.3390/bdcc5010010
Bello, S. A., Oyedele, L. O., Akinade, O. O., Bilal, M., Davila Delgado, J. M., Akanbi, L. Ajayi A. O., Owolabi, H. A. (2021). Cloud computing in construction industry: Use cases, benefits and challenges. Automation in Construction, 122, https://doi.org/10.1016/j.autcon.2020.103441
Gbadamosi, A., Oyedele, L., Davila Delgado, J. M., Kusimo, H., Akanbi, L., Olawale, O., & Muhammed-Yakubu, N. (2021). IoT for predictive assets monitoring and maintenance: An implementation strategy for the UK rail industry. Automation in Construction, 122.
Ganiyu, S. A., Oyedele, L. O., Akinade, O., Owolabi, H., Akanbi, L., & Gbadamosi, A. (2020). BIM competencies for delivering waste-efficient building projects in a circular economy. Developments in the Built Environment, 4, 100036. https://doi.org/10.1016/j.dibe.2020.100036.
Akanbi, L. A., Oyedele, A. O., Oyedele, L. O., & Salami, R. O. (2020). Deep learning model for demolition waste prediction in a circular economy. Journal of Cleaner Production, 274, https://doi.org/10.1016/j.jclepro.2020.122843
Ajayi, A., Oyedele, L., Akinade, O., Bilal, M., Akanbi, L., Delgado, J. M. D., & Owolabi, H. (2020). Optimised Big Data analytics for health and safety hazards prediction in power infrastructure operations. Safety Science, 125, https://doi.org/10.1016/j.ssci.2020.104656
Akinade, O., Oyedele, L., Oyedele, A., Davila Delgado, J. M., Bilal, M., Akanbi, L., Ajayi, A. & Owolabi, H. (2020). Design for deconstruction using a circular economy approach: Barriers and strategies for improvement. Production Planning and Control, 31(10), 829-840. https://doi.org/10.1080/09537287.2019.1695006
Davila Delgado, J., Oyedele, L., Bilal, M., Ajayi, A., Akanbi, L., & Akinade, O. (2020). Big Data analytics system for costing power transmission projects. Journal of Construction Engineering and Management
Ajayi, A., Oyedele, L., Owolabi, H., Akinade, O., Bilal, M., Davila Delgado, J. & Akanbi, L. (2019). Deep learning models for health and safety risk prediction in power infrastructure projects. Risk Analysis, https://doi.org/10.11 11/risa.13425
Bilal, M., Oyedele, L. O., Kusimo, H. O., Owolabi, H. A., Akanbi, L. A., Ajayi, A. O., Akinade, O. & Davila Delgado, J. M. (2019). Investigating profitability performance of construction projects using big data: A project analytics approach. Journal of Building Engineering, 26, https://doi.org/10.1016 /j.jobe.2019.100850.
Davila Delgado, J., Oyedele, L., Ajayi, A., Akanbi, L., Akinade, L., Bilal, M., & Owolabi, H. (2019). Robotics and automated systems in construction: understanding industry-specific challenges for adoption. Journal of Building Engineering
Luo, X., Oyedele, L. O., Ajayi, A. O., Monyei, C. G., Akinade, O. O., & Akanbi, L. A. (2019). Development of an IoT-based big data platform for day-ahead prediction of building heating and cooling demands. Advanced Engineering Informatics, 41, 100926. https://doi.org/10.1016/j.aei.2019.100926
Akanbi, L., Oyedele, L., Omoteso, K., Bilal, M., Akinade, O., Ajayi, A., Davila Delgado, J. M. and Owolabi, H. (2019) Disassembly and Deconstruction Analytics System (D-DAS) for construction in a circular economy. Journal of Cleaner Production, 223. pp. 386-396. ISSN 0959-6526. https://doi.org/10.1016/j.jclepro. 2019.03.172.
Bilal, M., Oyedele, L. O., Akinade, O. O., Davila Delgado, J. M., Akanbi, L.A., Ajayi, A.O. and Younis, M. (2019) Design optimisation using convex programming: Towards waste-efficient building designs. Journal of Building Engineering, 23. pp. 231-240. ISSN 2352-7102. https://doi.org/10.1016/j.jobe.2019.01.022

Book Chapters

Akanbi, L. A., Adenuga, K. I., & Owolabi, H. (2023). Supply chain decision-making using artificial intelligence and data analytics. In K. E. K. Vimal, S. Rajak, V. Kumar, R. S. Mor, & A. Assayed (Eds.), Industry 4.0 Technologies: Sustainable Manufacturing Supply Chains (25-34). Springer. https://doi.org/10.1007/978-981-99-4819-2_2.
Owolabi, H., Juryte, L., & Akanbi, L. (2023). Challenges for the adoption of industry 4.0 in the sustainable manufacturing supply chain. In K. E. K. Vimal, S. Rajak, V. Kumar, R. S. Mor, & A. Assayed (Eds.), Industry 4.0 Technologies: Sustainable Manufacturing Supply Chains (175-188). Singapore: Springer. https://doi.org/10.1007/978-981-99-4819-2_12.