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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...%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, 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, 101339. 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. (in press). Construction practitioners’ perception of key drivers of reputation in mega-construction projects. Journal of Engineering, Design and Technology, https://doi.org/10.1108/JEDT-10-2019-0255

© 2020, Emerald Publishing Limited. 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 drive... 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.

Big Data with deep learning for benchmarking profitability performance in project tendering (2020)
Journal Article
Bilal, M., & Oyedele, L. O. (2020). Big Data with deep learning for benchmarking profitability performance in project tendering. Expert Systems with Applications, 147, https://doi.org/10.1016/j.eswa.2020.113194

© 2020 A reliable benchmarking system is crucial for the contractors to evaluate the profitability performance of project tenders. Existing benchmarks are ineffective in the tender evaluation task for three reasons. Firstly, these benchmarks are most... Read More about Big Data with deep learning for benchmarking profitability performance in project tendering.

Guidelines for applied machine learning in construction industry—A case of profit margins estimation (2019)
Journal Article
Bilal, M., & Oyedele, L. (2020). Guidelines for applied machine learning in construction industry—A case of profit margins estimation. Advanced Engineering Informatics, 43, 101013. https://doi.org/10.1016/j.aei.2019.101013

© 2019 Elsevier Ltd The progress in the field of Machine Learning (ML) has enabled the automation of tasks that were considered impossible to program until recently. These advancements today have incited firms to seek intelligent solutions as part of... Read More about Guidelines for applied machine learning in construction industry—A case of profit margins estimation.

Risk mitigation in PFI/PPP project finance: A framework model for financiers’ bankability criteria (2019)
Journal Article
Owolabi, H., Oyedele, L., Alaka, H., Ajayi, S., Bilal, M., & Akinade, O. (2020). Risk mitigation in PFI/PPP project finance: A framework model for financiers’ bankability criteria. Built Environment Project and Asset Management, 10(1), 28-49. https://doi.org/10.1108/BEPAM-09-2018-0120

Purpose: Earlier studies on risk evaluation in private finance initiative and public private partnerships (PFI/PPP) projects have focussed more on quantitative approaches despite increasing call for contextual understanding of the bankability of risk... Read More about Risk mitigation in PFI/PPP project finance: A framework model for financiers’ bankability criteria.

Design for deconstruction using a circular economy approach: Barriers and strategies for improvement (2019)
Journal Article
Akinade, O., Oyedele, L., Oyedele, A., Davila Delgado, J. M., Bilal, M., Akanbi, L., …Owolabi, H. (in press). Design for deconstruction using a circular economy approach: Barriers and strategies for improvement. Production Planning and Control, https://doi.org/10.1080/09537287.2019.1695006

This study explores the current practices of Design for Deconstruction (DfD) as a strategy for achieving circular economy. Keeping in view the opportunities accruable from DfD, a review of the literature was carried out and six focus group interviews... Read More about Design for deconstruction using a circular economy approach: Barriers and strategies for improvement.

Deep learning models for health and safety risk prediction in power infrastructure projects (2019)
Journal Article
Delgado, J. M. D., Oyedele, L., Ajayi, A., Owolabi, H., Akinade, O., Bilal, M., …Akanbi, L. (in press). Deep learning models for health and safety risk prediction in power infrastructure projects. Risk Analysis, 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...%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.

Critical success factors for ensuring bankable completion risk in PFI/PPP megaprojects (2019)
Journal Article
Owolabi, H. A., Oyedele, L. O., Alaka, H. A., Ajayi, S. O., Akinade, O. O., & Bilal, M. (2020). Critical success factors for ensuring bankable completion risk in PFI/PPP megaprojects. Journal of Management in Engineering, 36(1), https://doi.org/10.1061...%29ME.1943-5479.0000717

© 2019 American Society of Civil Engineers. This study investigates project financiers' perspectives on the bankability of completion risk in private finance initiative and public-private partnership (PFI/PPP) megaprojects. Using a mixed methodology... Read More about Critical success factors for ensuring bankable completion risk in PFI/PPP megaprojects.


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