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Construction site layout planning methods: An analytical review (2021)
Conference Proceeding
Otukogbe, G., Oyedele, L., Akanbi, L., Manuel Davila-Delgado, M., Owolabi, H., Ganiyu, S., …Kadiri, K. (2021). Construction site layout planning methods: An analytical review. In Proc. of the IDoBE International Conference on Uncertainty in the Built Environment: How can we build a resilient future in the new normal?

Designing an effective construction site layout planning is essential to the successful implementation of construction projects. Construction site layout planning involves the optimal layout of facilities (i.e., fixed, and temporary facilities). Seve... Read More about Construction site layout planning methods: An analytical review.

Integrated life-cycle optimisation and supply-side management for building retrofitting (2021)
Journal Article
Luo, X., & Oyedele, L. O. (2022). Integrated life-cycle optimisation and supply-side management for building retrofitting. Renewable and Sustainable Energy Reviews, 154, Article 111827. https://doi.org/10.1016/j.rser.2021.111827

Building retrofitting is a powerful approach to enhance building energy performance. The net-zero ambition urges the need to renovate building energy system in view of the life-cycle optimal, to address climate and environmental challenges. Existing... Read More about Integrated life-cycle optimisation and supply-side management for building retrofitting.

Rainfall Prediction: A Comparative Analysis of Modern Machine Learning Algorithms for Time-Series Forecasting (2021)
Journal Article
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, Article 100204. https://doi.org/10.1016/j.mlwa.2021.100204

Rainfall forecasting has gained utmost research relevance in recent times due to its complexities and persistent applications such as flood forecasting and monitoring of pollutant concentration levels, among others. Existing models use complex statis... Read More about Rainfall Prediction: A Comparative Analysis of Modern Machine Learning Algorithms for Time-Series Forecasting.

Assessment and optimisation of life cycle environment, economy and energy for building retrofitting (2021)
Journal Article
Luo, X. J., & Oyedele, L. O. (2021). Assessment and optimisation of life cycle environment, economy and energy for building retrofitting. Energy for Sustainable Development, 65, 77-100. https://doi.org/10.1016/j.esd.2021.10.002

Building retrofitting plays a vital role in realising net-zero carbon ambition. Conventional retrofitting solutions are generally based upon decreasing operating energy usage or corresponding costs. However, many of these would increase the embodied... Read More about Assessment and optimisation of life cycle environment, economy and energy for building retrofitting.

Artificial intelligence in the construction industry: A review of present status, opportunities and future challenges (2021)
Journal Article
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, Article 103299. https://doi.org/10.1016/j.jobe.2021.103299

The growth of the construction industry is severely limited by the myriad complex challenges it faces such as cost and time overruns, health and safety, productivity and labour shortages. Also, construction industry is one the least digitized industr... Read More about Artificial intelligence in the construction industry: A review of present status, opportunities and future challenges.

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, Article 107836. 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.

Forecasting building energy consumption: Adaptive long-short term memory neural networks driven by genetic algorithm (2021)
Journal Article
Luo, X., & Oyedele, L. O. (2021). Forecasting building energy consumption: Adaptive long-short term memory neural networks driven by genetic algorithm. Advanced Engineering Informatics, 50, Article 101357. https://doi.org/10.1016/j.aei.2021.101357

The real-world building can be regarded as a comprehensive energy engineering system; its actual energy consumption depends on complex affecting factors, including various weather data and time signature. Accurate energy consumption forecasting and e... Read More about Forecasting building energy consumption: Adaptive long-short term memory neural networks driven by genetic algorithm.

A data-driven life-cycle optimisation approach for building retrofitting: A comprehensive assessment on economy, energy and environment (2021)
Journal Article
Luo, X. J., & Oyedele, L. O. (2021). A data-driven life-cycle optimisation approach for building retrofitting: A comprehensive assessment on economy, energy and environment. Journal of Building Engineering, 43, Article 102934. https://doi.org/10.1016/j.jobe.2021.102934

A novel data-driven life-cycle optimisation approach is proposed for building retrofitting. The innovation points include big-data information, integrated retrofitting design, and life-cycle optimisation through a comprehensive assessment of the econ... Read More about A data-driven life-cycle optimisation approach for building retrofitting: A comprehensive assessment on economy, energy and environment.

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, Article 101332. 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.

IoT technologies for livestock management: A review of present status, opportunities, and future trends (2021)
Journal Article
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), Article 10. https://doi.org/10.3390/bdcc5010010

The world population currently stands at about 7 billion amidst an expected increase in 2030 from 9.4 billion to around 10 billion in 2050. This burgeoning population has continued to influence the upward demand for animal food. Moreover, the managem... Read More about IoT technologies for livestock management: A review of present status, opportunities, and future trends.