Prediction and Performance Investigation of Polyurethane Foam as Thermal Insulation Material for Roofing Sheet Using Artificial Neural Network


  • V. B. Essien Department of Mechanical Engineering, Covenant University, Ota, Ogun State, Nigeria
  • Christian A. Bolu Department of Mechatronics Engineering School of Science and Technology, Pan-Atlantic University, Lagos State, Nigeria
  • Imhade P. Okokpujie Department of Mechanical Engineering, Covenant University, Ota, Ogun State, Nigeria
  • Joseph Azeta Department of Mechanical Engineering, Covenant University, Ota, Ogun State, Nigeria



Artificial Neural Network, Polyurethane Foam, Residential Roofing System, Thermal Insulation Material


The prediction and application of Polyurethane Foam in developing roofing sheets cannot be over-emphasized when considering the environmental changes coursed by thermal radiation. This paper presents an artificial neural network application to model and predict the indoor temperature resistance of polyurethane (PU) roofing in residential buildings. The study employed a data logger to measure the indoor and outdoor temperatures for three simulation environments (i.e., morning, afternoon, and evening) for two hours each. Furthermore, the authors employed the Levenberg-Marquardt algorithm to transform and predict the indoor temperature obtained in the residential building's polyurethane roofing house. The result shows that the PU roofing system could absorb the heat and reduce the house model's temperature with 6.9% in the morning, afternoon 15.8%, and 6.8% in the evening when compared with the temperature outdoor environment. The ANN was also able to train, test, and validate the experimental temperature results with 92.86%, 93.92%, and 95%, respectively. The mean square error and a testing error occurs at 0.1707 and 0.1689. Therefore, this study concluded that ANN's application in predicting the thermal insulation material such as the PU roofing system is highly efficient and will increase the manufacturer's performance evaluation. It has also created significant awareness of the community in employing the PU roofing system for residential buildings, which will reduce the rate of energy consumption in buildings.


Judkoff, Ron, and Joel Neymark. International Energy Agency building energy simulation test (BESTEST) and diagnostic method. No. NREL/TP--472-6231. National Renewable Energy Lab., 1995.

Sierra-Pérez, Jorge, Jesús Boschmonart-Rives, and Xavier Gabarrell. "Environmental implications in the substitution of non-renewable materials by renewable materials." SETAC Europe 2015 (2015).

Miezis, Martins, Kristaps Zvaigznitis, Nicholas Stancioff, and Lars Soeftestad. "Climate change and buildings energy efficiency–the key role of residents." Environmental and Climate Technologies 17, no. 1 (2016): 30-43.

Aditya, Lisa, T. M. I. Mahlia, B. Rismanchi, H. M. Ng, M. H. Hasan, H. S. C. Metselaar, Oki Muraza, and H. B. Aditiya. "A review on insulation materials for energy conservation in buildings." Renewable and sustainable energy reviews 73 (2017): 1352-1365.

Olesen, Bjarne W., and Gail S. Brager. "A better way to predict comfort: The new ASHRAE standard 55-2004." (2004).

Ong, Kok Seng. "Temperature reduction in attic and ceiling via insulation of several passive roof designs." Energy Conversion and Management 52, no. 6 (2011): 2405-2411.

Kumar, Rajesh, R. K. Aggarwal, and J. D. Sharma. "Energy analysis of a building using artificial neural network: A review." Energy and Buildings 65 (2013): 352-358.

Bre, Facundo, Juan M. Gimenez, and Víctor D. Fachinotti. "Prediction of wind pressure coefficients on building surfaces using artificial neural networks." Energy and Buildings 158 (2018): 1429-1441.

Deng, Zhipeng, and Qingyan Chen. "Artificial neural network models using thermal sensations and occupants’ behavior for predicting thermal comfort." Energy and Buildings 174 (2018): 587-602.

Xu, Xiaodong, Wei Wang, Tianzhen Hong, and Jiayu Chen. "Incorporating machine learning with building network analysis to predict multi-building energy use." Energy and Buildings 186 (2019): 80-97.

Mohandes, Saeed Reza, Xueqing Zhang, and Amir Mahdiyar. "A comprehensive review on the application of artificial neural networks in building energy analysis." Neurocomputing 340 (2019): 55-75.

Kim, Mansu, Sungwon Jung, and Joo-won Kang. "Artificial neural network-based residential energy consumption prediction models considering residential building information and user features in South Korea." Sustainability 12, no. 1 (2020): 109.

Bui, Dac-Khuong, Tuan Ngoc Nguyen, Tuan Duc Ngo, and H. Nguyen-Xuan. "An artificial neural network (ANN) expert system enhanced with the electromagnetism-based firefly algorithm (EFA) for predicting the energy consumption in buildings." Energy 190 (2020): 116370.

Mba, Leopold, Pierre Meukam, and Alexis Kemajou. "Application of artificial neural network for predicting hourly indoor air temperature and relative humidity in modern building in humid region." Energy and Buildings 121 (2016): 32-42.

Attoue Nivine, Shahrour Isam, and Younes Rafic. "Smart Building: Use of the ANN Approach for Indoor Temperature Forecasting." Energies 11 (2018).

Okokpujie, Imhade P., O. S. I. Fayomi, and R. O. Leramo. "The role of research in economic development." In IOP Conference Series: Materials Science and Engineering, vol. 413, no. 1, p. 012060. IOP Publishing, 2018.

Oyekunle, J. A. O., J. O. Dirisu, Imhade P. Okokpujie, and A. A. Asere. "Determination of Heat Transfer Properties of Various PVC and Non-PVC Ceiling Materials Available in Nigerian Markets." International Journal of Mechanical Engineering and Technology (IJMET) 9, no. 8 (2018): 963-973.

Dunmade, Israel, Mfon Udo, Tunde Akintayo, Sunday Oyedepo, and Imhade P. Okokpujie. "Lifecycle impact assessment of an engineering project management process–a SLCA approach." In IOP conference series: materials science and engineering, vol. 413, no. 1, p. 012061. IOP Publishing, 2018.

Lee, Do-Hun, and Doo-Sun Kang. "The application of the artificial neural network ensemble model for simulating streamflow." Procedia engineering 154 (2016): 1217-1224.

Wang, Yu-Ren, and G. Edward Gibson Jr. "A study of preproject planning and project success using ANNs and regression models." Automation in Construction 19, no. 3 (2010): 341-346.

Gazzaz, Nabeel M., Mohd Kamil Yusoff, Ahmad Zaharin Aris, Hafizan Juahir, and Mohammad Firuz Ramli. "Artificial neural network modeling of the water quality index for Kinta River (Malaysia) using water quality variables as predictors." Marine pollution bulletin 64, no. 11 (2012): 2409-2420.

Murphy, Kevin R., Brett Myors, and Allen Wolach. Statistical power analysis: A simple and general model for traditional and modern hypothesis tests. Routledge, 2014.

Feng, Chow Yi, Nur Ilya Farhana Md Noh, and Ramez Al Mansob. "Study on The Factors and Effects of Noise Pollution at Construction Site in Klang Valley." Journal of Advanced Research in Applied Sciences and Engineering Technology 20, no. 1 (2020): 18-26.

Budiyanto, Muhammad Arif, and Nadhilah Suheriyanto. "Analysis of the Effect of Inlet Velocity on Cooling Speed in a Refrigerated Container using CFD simulations." CFD Letters 12, no. 12 (2020): 55-62.

Yusefi, Mostafa, Kamyar Shameli, and Ahmad Faris Jumaat. "Preparation and Properties of Magnetic Iron Oxide Nanoparticles for Biomedical Applications: A Brief Review." Journal of Advanced Research in Materials Science 75, no. 1 (2020): 10-18.

Taib, Norhidayah Mat, Mohd Radzi Abu Mansor, and Wan Mohd Faizal Wan Mahmood. "Simulation of Hydrogen Fuel Combustion in Neon-oxygen Circulated Compression Ignition Engine." Journal of Advanced Research in Numerical Heat Transfer 3, no. 1 (2020): 25-36.

Ohijeagbon, Idehai O., Adekunle A. Adeleke, Vincent T. Mustapha, John A. Olorunmaiye, Imhade P. Okokpujie, and Peter P. Ikubanni. "Development and characterization of wood-polypropylene plastic-cement composite board." Case Studies in Construction Materials 13 (2020): e00365.

Oleolo, Ibrahim, Hayati Abdullah, Maziah Mohamad, Mohammad Nazri Mohd Jaafar, and Akmal Baharain. "Model Selection for the Control of Temperature in a Centralized Air Conditioning System." Journal of Advanced Research in Applied Mechanics 74, no. 1 (2020): 10-20.




How to Cite

Essien, V. B. ., Bolu , C. A. ., Okokpujie, I. P., & Azeta , J. (2021). Prediction and Performance Investigation of Polyurethane Foam as Thermal Insulation Material for Roofing Sheet Using Artificial Neural Network. Journal of Advanced Research in Fluid Mechanics and Thermal Sciences, 82(1), 113–125.