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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.

BIM competencies for delivering waste-efficient building projects in a circular economy (2020)
Journal Article
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

Competency measures are increasingly becoming effective ways for construction organizations to measure their ability to deliver waste-efficient projects. Despite the ongoing efforts in achieving the goals of the circular economy through BIM adoption,... Read More about BIM competencies for delivering waste-efficient building projects in a circular economy.

Offsite construction for emergencies: A focus on Isolation Space Creation (ISC) measures for the COVID-19 pandemic (2020)
Journal Article
Gbadamosi, A., Oyedele, L., Olawale, O., & Abioye, S. (2020). Offsite construction for emergencies: A focus on Isolation Space Creation (ISC) measures for the COVID-19 pandemic. Progress in Disaster Science, 8, https://doi.org/10.1016/j.pdisas.2020.100130

The outbreak of a pandemic of global concern, the Corona Virus Disease 2019 (COVID-19) has tested the capacity of healthcare facilities to the brim in many developed countries. In a minacious fashion of rapid spread and extreme transmission rate, COV... Read More about Offsite construction for emergencies: A focus on Isolation Space Creation (ISC) measures for the COVID-19 pandemic.

Big data for design option 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 option 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 option 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.

Life cycle assessment approach for renewable multi-energy system: A comprehensive analysis (2020)
Journal Article
Luo, X., Oyedele, L. O., Owolabi, H. A., Bilal, M., Ajayi, A. O., & Akinade, O. O. (2020). Life cycle assessment approach for renewable multi-energy system: A comprehensive analysis. Energy Conversion and Management, 224, https://doi.org/10.1016/j.enconman.2020.113354

In response to the gradual degradation of natural sources, there is a growing interest in adopting renewable resources for various building energy supply. In this study, a comprehensive life cycle assessment approach is proposed for a renewable multi... Read More about Life cycle assessment approach for renewable multi-energy system: A comprehensive analysis.

Deep learning model for demolition waste prediction in a circular economy (2020)
Journal Article
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

An essential requirement for a successful circular economy is the continuous use of materials. Planning for building materials reuse at the end-of-life of buildings is usually a difficult task because limited time are usually made available for build... Read More about Deep learning model for demolition waste prediction in a circular economy.

Genetic algorithm-determined deep feedforward neural network architecture for predicting electricity consumption in real buildings (2020)
Journal Article
Luo, X., 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.

Comparative study of machine learning-based multi-objective prediction framework for multiple building energy loads (2020)
Journal Article
Luo, X. J., Oyedele, L. O., Ajayi, A. O., & Akinade, O. O. (2020). Comparative study of machine learning-based multi-objective prediction framework for multiple building energy loads. Sustainable Cities and Society, 61, https://doi.org/10.1016/j.scs.2020.102283

Buildings are one of the significant sources of energy consumption and greenhouse gas emission in urban areas all over the world. Lighting control and building integrated photovoltaic (BIPV) are two effective measures in reducing overall primary ener... Read More about Comparative study of machine learning-based multi-objective prediction framework for multiple building energy loads.

Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings (2020)
Journal Article
Luo, X. J., Oyedele, L. O., Ajayi, A. O., Akinade, O. O., Owolabi, H. A., & Ahmed, A. (2020). Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings. Renewable and Sustainable Energy Reviews, 131, https://doi.org/10.1016/j.rser.2020.109980

Accurate forecast of energy consumption is essential in building energy management. Owing to the variation of outdoor weather condition among different seasons, year-round historical weather profile is needed to investigate its feature thoroughly. Da... Read More about Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings.

Big data innovation and diffusion in projects teams: Towards a conflict prevention culture (2020)
Journal Article
Oyedele, A., Owolabi, H. A., Oyedele, L. O., & Olawale, O. A. (2020). Big data innovation and diffusion in projects teams: Towards a conflict prevention culture. Developments in the Built Environment, 3, https://doi.org/10.1016/j.dibe.2020.100016

Despite the enormous literature on how team conflicts can be managed and resolved, this study diverges, by examining factors that facilitate conflict prevention culture in project teams, especially when introducing Big Data Technology. Relying on fin... Read More about Big data innovation and diffusion in projects teams: Towards a conflict prevention culture.

Project reputation in construction: A process-based perspective of construction practitioners in the UK (2020)
Journal Article
Olawale, O., Oyedele, L., Owolabi, H., Gbadamosi, A., & Kusimo, H. (in press). Project reputation in construction: A process-based perspective of construction practitioners in the UK. International Journal of Construction Management, https://doi.org/10.1080/15623599.2020.1783598

The overall aim of this study is to elicit the perspective of practitioners (e.g., architects, civil engineers, building engineers, structural engineers and quantity surveyors) on the process-related factors influencing the project reputation of cons... Read More about Project reputation in construction: A process-based perspective of construction practitioners in the UK.

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.

Two-stage capacity optimization approach of multi-energy system considering its optimal operation (2020)
Journal Article
Luo, X., Oyedele, L. O., Akinade, O. O., & Ajayi, A. O. (2020). Two-stage capacity optimization approach of multi-energy system considering its optimal operation. Energy and AI, 1, https://doi.org/10.1016/j.egyai.2020.100005

With the depletion of fossil fuel and climate change, multi-energy systems have attracted widespread attention in buildings. Multi-energy systems, fuelled by renewable energy, including solar and biomass energy, are gaining increasing adoption in com... Read More about Two-stage capacity optimization approach of multi-energy system considering its optimal operation.

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.

3D pattern identification approach for cooling load profiles in different buildings (2020)
Journal Article
Luo, X. J., Oyedele, L. O., Akinade, O., & Ajayi, A. O. (2020). 3D pattern identification approach for cooling load profiles in different buildings. Journal of Building Engineering, 31, https://doi.org/10.1016/j.jobe.2020.101339

© 2020 Elsevier Ltd Building energy conservation has gained increasing concern owing to its large portion of energy consumption and great potential of energy saving. In-depth understanding of representative patterns of daily cooling load profile will... Read More about 3D pattern identification approach for cooling load profiles in different buildings.

Construction practitioners’ perception of key drivers of reputation in mega-construction projects (2020)
Journal Article
Olawale, O. A., Oyedele, L. O., & Owolabi, H. A. (2020). Construction practitioners’ perception of key drivers of reputation in mega-construction projects. Journal of Engineering, Design and Technology, 18(6), 1571-1592. https://doi.org/10.1108/JEDT-10-2019-0255

Purpose: The purpose of this study is to commence the discourse on the non-inclusiveness of the dynamics of reputation within the construction industry by identifying and examining the key product and process drivers of reputation in mega-constructio... Read More about Construction practitioners’ perception of key drivers of reputation in mega-construction projects.

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.