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Deep learning with small datasets: Using autoencoders to address limited datasets in construction management (2021)
Journal Article
Davila Delgado, M., & Oyedele, L. (2021). Deep learning with small datasets: Using autoencoders to address limited datasets in construction management. Applied Soft Computing, 112, https://doi.org/10.1016/j.asoc.2021.107836

Large datasets are necessary for deep learning as the performance of the algorithms used increases as the size of the dataset increases. Poor data management practices and the low level of digitisation of the construction industry represent a big hur... Read More about Deep learning with small datasets: Using autoencoders to address limited datasets in construction management.

Digital Twins for the built environment: Learning from conceptual and process models in manufacturing (2021)
Journal Article
Davila Delgado, J. M., & Oyedele, L. (2021). Digital Twins for the built environment: Learning from conceptual and process models in manufacturing. Advanced Engineering Informatics, 49, https://doi.org/10.1016/j.aei.2021.101332

The overall aim of this paper is to contribute to a better understanding of the Digital Twin (DT) paradigm in the built environment by drawing inspiration from existing DT research in manufacturing. The DT is a Product Life Management information con... Read More about Digital Twins for the built environment: Learning from conceptual and process models in manufacturing.

Deep learning and Boosted trees for injuries prediction in power infrastructure projects (2021)
Journal Article
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

Electrical injury impacts are substantial and massive. Investments in electricity will continue to increase, leading to construction project complexities, which undoubtedly contribute to injuries and associated effects. Machine learning (ML) algorith... Read More about Deep learning and Boosted trees for injuries prediction in power infrastructure projects.

Cloud computing in construction industry: Use cases, benefits and challenges (2020)
Journal Article
Bello, S. A., Oyedele, L. O., Akinade, O. O., Bilal, M., Davila Delgado, J. M., Akanbi, L. A., …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

Cloud computing technologies have revolutionised several industries (such as aerospace, manufacturing, automobile, retail, etc.) for several years. Although the construction industry is well placed to also leverage these technologies for competitive... Read More about Cloud computing in construction industry: Use cases, benefits and challenges.

IoT for predictive assets monitoring and maintenance: An implementation strategy for the UK rail industry (2020)
Journal Article
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,

With about 100% increase in rail service usage over the last 20 years, it is pertinent that rail infrastructure continues to function at an optimal level to avoid service disruptions, cancellations or delays due to unforeseen asset breakdown. In an e... Read More about IoT for predictive assets monitoring and maintenance: An implementation strategy for the UK rail industry.

Estimating occupancy levels in enclosed spaces using environmental variables: A fitness gym and living room as evaluation scenarios (2020)
Journal Article
Vela, A., Alvarado-Uribe, J., Davila Delgado, M., Hernandez-Gress, N., & Ceballos, H. G. (2020). Estimating occupancy levels in enclosed spaces using environmental variables: A fitness gym and living room as evaluation scenarios. Sensors, 20(22), 6579. https://doi.org/10.3390/s20226579

The understanding of occupancy patterns has been identified as a key contributor to achieve improvements in energy efficiency in buildings since occupancy information can benefit different systems, such as HVAC (Heating, Ventilation, and Air Conditio... Read More about Estimating occupancy levels in enclosed spaces using environmental variables: A fitness gym and living room as evaluation scenarios.

Big data for design options repository: Towards a DFMA approach for offsite construction (2020)
Journal Article
Gbadamosi, A., Oyedele, L., Mahamadu, A., Kusimo, H., Bilal, M., Davila Delgado, J. M., & Muhammed-Yakubu, N. (2020). Big data for design options repository: Towards a DFMA approach for offsite construction. Automation in Construction, 120, https://doi.org/10.1016/j.autcon.2020.103388

A persistent barrier to the adoption of offsite construction is the lack of information for assessing prefabrication alternatives and the choices of suppliers. This study integrates three aspects of offsite construction, including BIM, DFMA and big d... Read More about Big data for design options repository: Towards a DFMA approach for offsite construction.

Deep learning in the construction industry: A review of present status and future innovations (2020)
Journal Article
Akinosho, T. D., Oyedele, L. O., Bilal, M., Ajayi, A. O., Delgado, M. D., Akinade, O. O., & Ahmed, A. A. (2020). Deep learning in the construction industry: A review of present status and future innovations. Journal of Building Engineering, 32, https://doi.org/10.1016/j.jobe.2020.101827

The construction industry is known to be overwhelmed with resource planning, risk management and logistic challenges which often result in design defects, project delivery delays, cost overruns and contractual disputes. These challenges have instigat... Read More about Deep learning in the construction industry: A review of present status and future innovations.

Genetic algorithm-determined deep feedforward neural network architecture for predicting electricity consumption in real buildings (2020)
Journal Article
Luo, X. J., Oyedele, L. O., Ajayi, A. O., Akinade, O. O., Delgado, J. M. D., Owolabi, H. A., & Ahmed, A. (2020). Genetic algorithm-determined deep feedforward neural network architecture for predicting electricity consumption in real buildings. Energy and AI, 2, https://doi.org/10.1016/j.egyai.2020.100015

A genetic algorithm-determined deep feedforward neural network architecture (GA-DFNN) is proposed for both day-ahead hourly and week-ahead daily electricity consumption of a real-world campus building in the United Kingdom. Due to the comprehensive r... Read More about Genetic algorithm-determined deep feedforward neural network architecture for predicting electricity consumption in real buildings.

A research agenda for augmented and virtual reality in architecture, engineering and construction (2020)
Journal Article
Davila Delgado, M., Oyedele, L., Demian, P., & Beach, T. (2020). A research agenda for augmented and virtual reality in architecture, engineering and construction. Advanced Engineering Informatics, 45, https://doi.org/10.1016/j.aei.2020.101122

This paper presents a study on the usage landscape of augmented reality (AR) and virtual reality (VR) in the architecture, engineering and construction sectors, and proposes a research agenda to address the existing gaps in required capabilities. A s... Read More about A research agenda for augmented and virtual reality in architecture, engineering and construction.

Augmented and virtual reality in construction: Drivers and limitations for industry adoption (2020)
Journal Article
Davila Delgado, J. M., Oyedele, L., Beach, T., & Demian, P. (2020). Augmented and virtual reality in construction: Drivers and limitations for industry adoption. Journal of Construction Engineering and Management, 146(7), https://doi.org/10.1061/%28ASCE%29CO.1943-7862.0001844

Augmented and virtual reality have the potential to provide a step-change in productivity in the construction sector; however, the level of adoption is very low. This paper presents a systematic study of the factors that limit and drive adoption in a... Read More about Augmented and virtual reality in construction: Drivers and limitations for industry adoption.

BIM data model requirements for asset monitoring and the circular economy (2020)
Journal Article
Davila Delgado, J. M., & Oyedele, L. O. (in press). BIM data model requirements for asset monitoring and the circular economy. Journal of Engineering, Design and Technology, 18(5), 1269-1285. https://doi.org/10.1108/JEDT-10-2019-0284

© 2020, Emerald Publishing Limited. Purpose: The purpose of this paper is to review and provide recommendations to extend the current open standard data models for describing monitoring systems and circular economy precepts for built assets. Open sta... Read More about BIM data model requirements for asset monitoring and the circular economy.

Optimised Big Data analytics for health and safety hazards prediction in power infrastructure operations (2020)
Journal Article
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

© 2020 Elsevier Ltd Forecasting imminent accidents in power infrastructure projects require a robust and accurate prediction model to trigger a proactive strategy for risk mitigation. Unfortunately, getting ready-made machine learning algorithms to e... Read More about Optimised Big Data analytics for health and safety hazards prediction in power infrastructure operations.

Deep learning models for health and safety risk prediction in power infrastructure projects (2019)
Journal Article
Ajayi, A., Oyedele, L., Owolabi, H., Akinade, O., Bilal, M., Davila Delgado, J. M., & Akanbi, L. (2020). Deep learning models for health and safety risk prediction in power infrastructure projects. Risk Analysis, 40(10), 2019-2039. https://doi.org/10.1111/risa.13425

Inappropriate management of Health and safety (H&S) risk in power infrastructure projects can result in occupational accidents and equipment damage. Accidents at work have detrimental effects on workers, company, and the general public. Despite the a... Read More about Deep learning models for health and safety risk prediction in power infrastructure projects.

Big data analytics system for costing power transmission projects (2019)
Journal Article
Delgado, J. M. D., 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, 146(1), https://doi.org/10.1061/%28ASCE%29CO.1943-7862.0001745

© 2019 American Society of Civil Engineers. Inaccurate cost estimates have significant impacts on the final cost of power transmission projects and erode profits. Methods for cost estimation have been investigated thoroughly, but they are not used wi... Read More about Big data analytics system for costing power transmission projects.

Robotics and automated systems in construction: Understanding industry-specific challenges for adoption (2019)
Journal Article
Davila Delgado, J. M., 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, 26, https://doi.org/10.1016/j.jobe.2019.100868

© 2019 The Authors The construction industry is a major economic sector, but it is plagued with inefficiencies and low productivity. Robotics and automated systems have the potential to address these shortcomings; however, the level of adoption in th... Read More about Robotics and automated systems in construction: Understanding industry-specific challenges for adoption.

Vision network: Augmented reality and virtual reality for digital built Britain (2019)
Report
Davila Delgado, J. M. (2019). Vision network: Augmented reality and virtual reality for digital built Britain

The Vision Network, a mix of academics and industry experts, conducted a study into the levels of adoption of Augmented Reality (AR) and Virtual Reality (VR) technologies in the UK’s Architecture, Engineering, and Construction (AEC) sectors. A mixed... Read More about Vision network: Augmented reality and virtual reality for digital built Britain.

Disassembly and deconstruction analytics system (D-DAS) for construction in a circular economy (2019)
Journal Article
Akanbi, L. A., Oyedele, L. O., Omoteso, K., Bilal, M., Akinade, O. O., Ajayi, A. O., …Owolabi, H. A. (2019). Disassembly and deconstruction analytics system (D-DAS) for construction in a circular economy. Journal of Cleaner Production, 223, 386-396. https://doi.org/10.1016/j.jclepro.2019.03.172

© 2019 Despite the relevance of building information modelling for simulating building performance at various life cycle stages, Its use for assessing the end-of-life impacts is not a common practice. Even though the global sustainability and circula... Read More about Disassembly and deconstruction analytics system (D-DAS) for construction in a circular economy.

Design optimisation using convex programming: Towards waste-efficient building designs (2019)
Journal Article
Bilal, M., Oyedele, L. O., Akinade, O. O., Delgado, J. M. D., Akanbi, L. A., Ajayi, A. O., & Younis, M. S. (2019). Design optimisation using convex programming: Towards waste-efficient building designs. Journal of Building Engineering, 23, 231-240. https://doi.org/10.1016/j.jobe.2019.01.022

© 2019 The Authors A non-modular building layout is amongst the leading sources of offcut waste, resulting from a substantial amount of onsite cutting and fitting of bricks, blocks, plasterboard, and tiles. The field of design for dimensional coordin... Read More about Design optimisation using convex programming: Towards waste-efficient building designs.