Construction and Building Materials, Год журнала: 2024, Номер 444, С. 137728 - 137728
Опубликована: Авг. 10, 2024
Язык: Английский
Construction and Building Materials, Год журнала: 2024, Номер 444, С. 137728 - 137728
Опубликована: Авг. 10, 2024
Язык: Английский
Applied Thermal Engineering, Год журнала: 2023, Номер 224, С. 120088 - 120088
Опубликована: Янв. 23, 2023
Язык: Английский
Процитировано
64Construction and Building Materials, Год журнала: 2024, Номер 416, С. 135108 - 135108
Опубликована: Янв. 28, 2024
Thermal energy storage in building envelopes is critical to promoting renewable energy, implementation of which requires thermal performance enhancement construction materials. In this regard, phase change materials (PCMs) are often incorporated with cement-based composites (CBCs) materials, most commonly used construction. The current article provides a state-of-the-art review PCM-incorporated CBCs (PCM-CBCs) considering various CBCs, incorporation methods, and their challenges solutions. Additionally, evaluation PCM-CBCs carried out based on thermal, mechanical, durability, sustainability, efficiency, resource conservation-based performances. It was identified that terms performance, natural conservation, the research has been well established, they find vast application TES management systems. On other hand, although healthy data available appraisal mechanical PCM-CBCs, more efforts required control detrimental impact PCM make them durable desirable for where must undergo loading. This consolidated perspective researchers, practitioners, educators working practical.
Язык: Английский
Процитировано
30Journal of Energy Storage, Год журнала: 2025, Номер 114, С. 115933 - 115933
Опубликована: Фев. 24, 2025
Язык: Английский
Процитировано
4Renewable and Sustainable Energy Reviews, Год журнала: 2025, Номер 215, С. 115587 - 115587
Опубликована: Март 15, 2025
Язык: Английский
Процитировано
2Materials, Год журнала: 2022, Номер 15(1), С. 335 - 335
Опубликована: Янв. 3, 2022
The use of phase-change materials (PCM) in concrete has revealed promising results terms clean energy storage. However, the negative impact interaction between PCM and on mechanical durability properties limits field applications, leading to a shift research incorporate into using different techniques overcome these issues. storage via significantly supports UN SDG 7 target affordable energy. Therefore, present study focuses three aspects: type, effect properties, connecting outcome composite United Nations sustainable development goals (UN SDGs). compensation reduction strength PCM-contained is possible up some extent with nanomaterials supplementary cementitious materials. As PCM-incorporated categorized type building material, large-scale this material will affect stages associated lifetimes. study, amendments lifetimes after are discussed mapped consideration SDGs 7, 11, 12. current challenges widespread lower thermal conductivity, trade-off PCM, absence link PCM-concrete SDGs. global prospects as part effort attain studied here motivate architects, designers, practicing engineers, researchers accelerate their efforts promote PCM-containing ultimately net zero carbon emissions from infrastructure for future.
Язык: Английский
Процитировано
49Case Studies in Construction Materials, Год журнала: 2022, Номер 17, С. e01537 - e01537
Опубликована: Окт. 7, 2022
Time and cost-efficient techniques are essential to avoid extra conventional experimental studies with large data-set for material characterization of composite materials. This study is aimed at providing a correlation between the structural performance mechanical properties carbon nano-tubes reinforced cementitious composites through efficient predictive Machine Learning (ML) models. The Flexural (FS) Compressive (CS) Strength Carbon Nanotube (CNT)-reinforced were predicted based on data-rich framework provided in literature. Two different ensembled ML methods including Random Forest (RF) Gradient Boosting (GBM) implemented those data predicting CNT-reinforced cement-based composites. Data-set utilized training proposed models employing SciKit-Learn library Python, followed by hyper-parameter tuning k-fold cross-validation method obtaining an optimum model predict target values. It was shown that CS values more accurate than FS counterparts developed GBM has less sensitivity alteration test RF model. Finally, analysis conducted Sobol algorithm parameters highest contribution identified.
Язык: Английский
Процитировано
48Journal of Energy Storage, Год журнала: 2022, Номер 56, С. 105976 - 105976
Опубликована: Окт. 31, 2022
Язык: Английский
Процитировано
40Journal of Energy Storage, Год журнала: 2024, Номер 83, С. 110819 - 110819
Опубликована: Фев. 7, 2024
Язык: Английский
Процитировано
15Journal of Materials in Civil Engineering, Год журнала: 2025, Номер 37(4)
Опубликована: Фев. 5, 2025
Язык: Английский
Процитировано
1Materials Today Proceedings, Год журнала: 2022, Номер 63, С. 685 - 691
Опубликована: Янв. 1, 2022
Язык: Английский
Процитировано
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