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Thermal Behavior and Energy Efficiency of Modified Concretes in the Tropical Climate: A Systemic Review

Author

Listed:
  • Yeong Huei Lee

    (Department of Civil and Construction Engineering, Faculty of Engineering and Science, Curtin University, CDT 250, Miri 98009, Sarawak, Malaysia)

  • Mugahed Amran

    (Department of Civil Engineering, College of Engineering, Prince Sattam Bin Abdulaziz University, Alkharj 11942, Saudi Arabia
    Department of Civil Engineering, Faculty of Engineering and IT, Amran University, Amran 9677, Yemen)

  • Yee Yong Lee

    (Department of Civil Engineering, Faculty of Engineering, Universiti Malaysia Sarawak, Kota Samarahan 94300, Sarawak, Malaysia)

  • Ahmad Beng Hong Kueh

    (Department of Civil Engineering, Faculty of Engineering, Universiti Malaysia Sarawak, Kota Samarahan 94300, Sarawak, Malaysia)

  • Siaw Fui Kiew

    (Curtin Malaysia Research Institute, Sarawak Biovalley Pilot Plant, Curtin University, Sarawak Malaysia, CDT 250, Miri 98009, Sarawak, Malaysia)

  • Roman Fediuk

    (Polytechnic Institute, Far Eastern Federal University, 690922 Vladivostok, Russia)

  • Nikolai Vatin

    (Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, Russia)

  • Yuriy Vasilev

    (Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, Russia)

Abstract

Concrete remains the most utilised construction material for building envelopes, which regulate the indoor temperature to achieve human thermal comfort. Often, the energy consumption for building performance appraisal is related to the thermal behaviour of building materials as heating, ventilation, and air conditioning systems all variously contribute to human comfort. Following the development of concrete technology, many types of concrete have been invented to serve several purposes in the construction industry. To clearly understand the concrete type tailored for the specifics of a construction project, the local climate, concrete mechanical properties, and concrete thermal behaviours should be primarily identified to achieve energy efficiency, which also suits the sustainability of global materials. This paper, therefore, reviews the modified concrete thermal behaviours in the tropical climate for more systematic city planning in order to achieve better energy efficiency. Urban heat islands in the tropics and contributing factors, as well as heat transfer mechanisms, are first highlighted. The requirements of concrete thermal behaviour for building envelopes are then discussed through specific heat capacity, thermal conductivity, thermal diffusivity, time lag, and decrement factor in the context of applications and energy consumption in the tropical regions. With a case study, it is found that concrete thermal behaviours directly affect the energy consumption attributed mainly to the use of cooling systems in the tropics. The study can be a reference to mitigating the urban heat island phenomenon in the planning of urban development.

Suggested Citation

  • Yeong Huei Lee & Mugahed Amran & Yee Yong Lee & Ahmad Beng Hong Kueh & Siaw Fui Kiew & Roman Fediuk & Nikolai Vatin & Yuriy Vasilev, 2021. "Thermal Behavior and Energy Efficiency of Modified Concretes in the Tropical Climate: A Systemic Review," Sustainability, MDPI, vol. 13(21), pages 1, October.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:21:p:11957-:d:667677
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    References listed on IDEAS

    as
    1. Zingre, Kishor T. & Wan, Man Pun & Tong, Shanshan & Li, Hua & Chang, Victor W.-C. & Wong, Swee Khian & Thian Toh, Winston Boo & Leng Lee, Irene Yen, 2015. "Modeling of cool roof heat transfer in tropical climate," Renewable Energy, Elsevier, vol. 75(C), pages 210-223.
    2. Maytham S. Ahmed & Azah Mohamed & Raad Z. Homod & Hussain Shareef, 2016. "Hybrid LSA-ANN Based Home Energy Management Scheduling Controller for Residential Demand Response Strategy," Energies, MDPI, vol. 9(9), pages 1-20, September.
    3. Jain, Rishee K. & Smith, Kevin M. & Culligan, Patricia J. & Taylor, John E., 2014. "Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy," Applied Energy, Elsevier, vol. 123(C), pages 168-178.
    4. Saidur Rahaman & Selim Jahangir & Md Senaul Haque & Ruishan Chen & Pankaj Kumar, 2021. "Spatio-temporal changes of green spaces and their impact on urban environment of Mumbai, India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(4), pages 6481-6501, April.
    5. Nusrat Jannat & Aseel Hussien & Badr Abdullah & Alison Cotgrave, 2020. "A Comparative Simulation Study of the Thermal Performances of the Building Envelope Wall Materials in the Tropics," Sustainability, MDPI, vol. 12(12), pages 1-26, June.
    6. Noura Ghabra & Lucelia Rodrigues & Philip Oldfield, 2017. "The impact of the building envelope on the energy efficiency of residential tall buildings in Saudi Arabia," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 12(4), pages 411-419.
    7. Weinan Gan & Yunzhong Cao & Wen Jiang & Liangqiang Li & Xiaolin Li, 2019. "Energy-Saving Design of Building Envelope Based on Multiparameter Optimization," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-11, December.
    8. Li, Ran & Wang, Zhimin & Gu, Chenghong & Li, Furong & Wu, Hao, 2016. "A novel time-of-use tariff design based on Gaussian Mixture Model," Applied Energy, Elsevier, vol. 162(C), pages 1530-1536.
    9. Fathipour, Reza & Hadidi, Amin, 2017. "Analytical solution for the study of time lag and decrement factor for building walls in climate of Iran," Energy, Elsevier, vol. 134(C), pages 167-180.
    10. Sanusi Saheed & Farah N. A. Abd. Aziz & Mugahed Amran & Nikolai Vatin & Roman Fediuk & Togay Ozbakkaloglu & Gunasekaran Murali & Mohammad Ali Mosaberpanah, 2020. "Structural Performance of Shear Loaded Precast EPS-Foam Concrete Half-Shaped Slabs," Sustainability, MDPI, vol. 12(22), pages 1-17, November.
    11. Capozzoli, Alfonso & Gorrino, Alice & Corrado, Vincenzo, 2013. "A building thermal bridges sensitivity analysis," Applied Energy, Elsevier, vol. 107(C), pages 229-243.
    12. Chua, K.J. & Chou, S.K., 2010. "Energy performance of residential buildings in Singapore," Energy, Elsevier, vol. 35(2), pages 667-678.
    13. Drissi, Sarra & Ling, Tung-Chai & Mo, Kim Hung, 2020. "Thermal performance of a solar energy storage concrete panel incorporating phase change material aggregates developed for thermal regulation in buildings," Renewable Energy, Elsevier, vol. 160(C), pages 817-829.
    14. Naji, Sareh & Keivani, Afram & Shamshirband, Shahaboddin & Alengaram, U. Johnson & Jumaat, Mohd Zamin & Mansor, Zulkefli & Lee, Malrey, 2016. "Estimating building energy consumption using extreme learning machine method," Energy, Elsevier, vol. 97(C), pages 506-516.
    15. Kontoleon, K.J. & Eumorfopoulou, E.A., 2008. "The influence of wall orientation and exterior surface solar absorptivity on time lag and decrement factor in the Greek region," Renewable Energy, Elsevier, vol. 33(7), pages 1652-1664.
    16. Fateh Nassim Melzi & Allou Same & Mohamed Haykel Zayani & Latifa Oukhellou, 2017. "A Dedicated Mixture Model for Clustering Smart Meter Data: Identification and Analysis of Electricity Consumption Behaviors," Energies, MDPI, vol. 10(10), pages 1-21, September.
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    1. Yi Xuan Tang & Yeong Huei Lee & Mugahed Amran & Roman Fediuk & Nikolai Vatin & Ahmad Beng Hong Kueh & Yee Yong Lee, 2022. "Artificial Neural Network-Forecasted Compression Strength of Alkaline-Activated Slag Concretes," Sustainability, MDPI, vol. 14(9), pages 1-20, April.

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