All Outputs (34)

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.

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.

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, Article 113354. 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.

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, Article 100015. 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.

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, Article 109980. 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, Article 100016. 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. (2022). Project reputation in construction: A process-based perspective of construction practitioners in the UK. International Journal of Construction Management, 22(12), 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.

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., Owolabi, H., Akanbi, L., & Delgado, J. M. D. (2020). Optimised Big Data analytics for health and safety hazards prediction in power infrastructure operations. Safety Science, 125, Article 104656. 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.

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.

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.

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/%28ASCE%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.

Investigating profitability performance of construction projects using big data: A project analytics approach (2019)
Journal Article
Bilal, M., Oyedele, L. O., Kusimo, H. O., Owolabi, H. A., Akanbi, L. A., Ajayi, A. O., …Davila Delgado, J. M. (2019). Investigating profitability performance of construction projects using big data: A project analytics approach. Journal of Building Engineering, 26, Article 100850. https://doi.org/10.1016/j.jobe.2019.100850

© 2019 The Authors The construction industry generates different types of data from the project inception stage to project delivery. This data comes in various forms and formats which surpass the data management, integration and analysis capabilities... Read More about Investigating profitability performance of construction projects using big data: A project analytics approach.

Stimulating the attractiveness of PFI/PPPs using public sector guarantees (2019)
Journal Article
Owolabi, H., Oyedele, L., Alaka, H., Bilal, M., Ajayi, S., Akinade, O., & Agboola, A. (2019). Stimulating the attractiveness of PFI/PPPs using public sector guarantees. World Journal of Entrepreneurship, Management and Sustainable Development, 15(3), 239-258. https://doi.org/10.1108/WJEMSD-05-2018-0055

Purpose: Although the UK Guarantee Scheme for Infrastructures (UKGSI) was introduced in 2012 to address the huge financing gap for critical infrastructures, PFI sponsors have so far guaranteed only few projects. Many stakeholders in the project finan... Read More about Stimulating the attractiveness of PFI/PPPs using public sector guarantees.

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.

Predicting completion risk in PPP projects using big data analytics (2018)
Journal Article
Owolabi, H., Bilal, M., Oyedele, L., Alaka, H. A., Ajayi, S. O., & Akinade, O. (2020). Predicting completion risk in PPP projects using big data analytics. IEEE Transactions on Engineering Management, 67(2), 430-453. https://doi.org/10.1109/TEM.2018.2876321

Accurate prediction of potential delays in public private partnerships (PPP) projects could provide valuable information relevant for planning and mitigating completion risk in future PPP projects. However, existing techniques for evaluating completi... Read More about Predicting completion risk in PPP projects using big data analytics.

Public private partnerships (PPP) in the developing world: Mitigating financiers’ risks (2018)
Journal Article
Owolabi, H. A., Oyedele, L., Alaka, H., Ebohon, O. J., Ajayi, S., Akinade, O., …Olawale, O. (2019). Public private partnerships (PPP) in the developing world: Mitigating financiers’ risks. World Journal of Science, Technology and Sustainable Development, 16(3), 121-141. https://doi.org/10.1108/WJSTSD-05-2018-0043

Purpose – A major challenge for foreign lenders in financing public private partnerships (PPP) infrastructure projects in an emerging market (EM) is the bankability of country-related risks. Despite existing studies on country risks in international... Read More about Public private partnerships (PPP) in the developing world: Mitigating financiers’ risks.

A Big Data analytics approach for construction firms failure prediction models (2018)
Journal Article
Alaka, H., Oyedele, L., Owolabi, H., Akinade, O., Bilal, M., & Ajayi, S. (2019). A Big Data analytics approach for construction firms failure prediction models. IEEE Transactions on Engineering Management, 66(4), 689-698. https://doi.org/10.1109/TEM.2018.2856376

Using 693,000 datacells from 33,000 sample construction firms that operated or failed between 2008 and 2017, failure prediction models were developed using artificial neural network (ANN), support vector machine (SVM), multiple discriminant analysis... Read More about A Big Data analytics approach for construction firms failure prediction models.

Designing out construction waste using BIM technology: Stakeholders’ expectations for industry deployment (2018)
Journal Article
Akinade, O., Oyedele, L., Ajayi, S., Bilal, M., Alaka, H. A., Owolabi, H., & Arawomo, O. (2018). Designing out construction waste using BIM technology: Stakeholders’ expectations for industry deployment. Journal of Cleaner Production, 180, 375-385. https://doi.org/10.1016/j.jclepro.2018.01.022

© 2018 The Authors The need to use Building Information Modelling (BIM) for Construction and Demolition Waste (CDW) minimisation is well documented but most of the existing CDW management tools still lack BIM functionality. This study therefore asses... Read More about Designing out construction waste using BIM technology: Stakeholders’ expectations for industry deployment.

A framework for big data analytics approach to failure prediction of construction firms (2018)
Journal Article
Alaka, H. A., Oyedele, L. O., Owolabi, H. A., Bilal, M., Ajayi, S. O., & Akinade, O. O. (2020). A framework for big data analytics approach to failure prediction of construction firms. Applied Computing and Informatics, 16(1/2), 207-222. https://doi.org/10.1016/j.aci.2018.04.003

This study explored use of big data analytics (BDA) to analyse data of a large number of construction firms to develop a construction business failure prediction model (CB-FPM). Careful analysis of literature revealed financial ratios as the best for... Read More about A framework for big data analytics approach to failure prediction of construction firms.

The application of web of data technologies in building materials information modelling for construction waste analytics (2017)
Journal Article
Bilal, M., Oyedele, L. O., Munir, K., Ajayi, S. O., Akinade, O. O., Owolabi, H. A., & Alaka, H. A. (2017). The application of web of data technologies in building materials information modelling for construction waste analytics. Sustainable Materials and Technologies, 11, 28-37. https://doi.org/10.1016/j.susmat.2016.12.004

© 2017 Elsevier B.V. Predicting and designing out construction waste in real time is complex during building waste analysis (BWA) since it involves a large number of analyses for investigating multiple waste-efficient design strategies. These analyse... Read More about The application of web of data technologies in building materials information modelling for construction waste analytics.