Optimization of adobe clay bricks based on the raw material properties (mathematical analysis)
Apstrakt
This research studies the effects of composition and granulometry analysis of 139 heavy clays on the important characteristics of wet and adobe clay bricks. ANN models were obtained with high prediction accuracy in training cycles (r(2)): 0.580-0.907. Standard score analysis (SS) is performed to evaluate the optimal content of raw materials to gain adobe bricks. Optimal macro-oxides content was 53-66% SiO2, 4.6-7.5% Fe2O3, 12.5-18.2% Al2O3, 0.9-8.8% CaO, 1.2-3.6% MgO. The optimal quantity of alevrolite-sized particles varied between 46 and 65%, and clay-sized particles contents ranged from 20.4 to 40.6%.
Ključne reči:
Plasticity / Optimization / Drying sensitivity / Adobe clay brickIzvor:
Construction and Building Materials, 2020, 244Izdavač:
- Elsevier Sci Ltd, Oxford
Finansiranje / projekti:
- Razvoj i primena multifunkcionalnih materijala na bazi domaćih sirovina modernizacijom tradicionalnih tehnologija (RS-MESTD-Integrated and Interdisciplinary Research (IIR or III)-45008)
- Osmotska dehidratacija hrane - energetski i ekološki aspekti održive proizvodnje (RS-MESTD-Technological Development (TD or TR)-31055)
DOI: 10.1016/j.conbuildmat.2020.118342
ISSN: 0950-0618
WoS: 000527410200052
Scopus: 2-s2.0-85079191816
Institucija/grupa
Institut za opštu i fizičku hemijuTY - JOUR AU - Vasić, Milica V. AU - Pezo, Lato AU - Radojević, Zagorka PY - 2020 UR - https://riofh.iofh.bg.ac.rs/handle/123456789/755 AB - This research studies the effects of composition and granulometry analysis of 139 heavy clays on the important characteristics of wet and adobe clay bricks. ANN models were obtained with high prediction accuracy in training cycles (r(2)): 0.580-0.907. Standard score analysis (SS) is performed to evaluate the optimal content of raw materials to gain adobe bricks. Optimal macro-oxides content was 53-66% SiO2, 4.6-7.5% Fe2O3, 12.5-18.2% Al2O3, 0.9-8.8% CaO, 1.2-3.6% MgO. The optimal quantity of alevrolite-sized particles varied between 46 and 65%, and clay-sized particles contents ranged from 20.4 to 40.6%. PB - Elsevier Sci Ltd, Oxford T2 - Construction and Building Materials T1 - Optimization of adobe clay bricks based on the raw material properties (mathematical analysis) VL - 244 DO - 10.1016/j.conbuildmat.2020.118342 UR - conv_841 ER -
@article{ author = "Vasić, Milica V. and Pezo, Lato and Radojević, Zagorka", year = "2020", abstract = "This research studies the effects of composition and granulometry analysis of 139 heavy clays on the important characteristics of wet and adobe clay bricks. ANN models were obtained with high prediction accuracy in training cycles (r(2)): 0.580-0.907. Standard score analysis (SS) is performed to evaluate the optimal content of raw materials to gain adobe bricks. Optimal macro-oxides content was 53-66% SiO2, 4.6-7.5% Fe2O3, 12.5-18.2% Al2O3, 0.9-8.8% CaO, 1.2-3.6% MgO. The optimal quantity of alevrolite-sized particles varied between 46 and 65%, and clay-sized particles contents ranged from 20.4 to 40.6%.", publisher = "Elsevier Sci Ltd, Oxford", journal = "Construction and Building Materials", title = "Optimization of adobe clay bricks based on the raw material properties (mathematical analysis)", volume = "244", doi = "10.1016/j.conbuildmat.2020.118342", url = "conv_841" }
Vasić, M. V., Pezo, L.,& Radojević, Z.. (2020). Optimization of adobe clay bricks based on the raw material properties (mathematical analysis). in Construction and Building Materials Elsevier Sci Ltd, Oxford., 244. https://doi.org/10.1016/j.conbuildmat.2020.118342 conv_841
Vasić MV, Pezo L, Radojević Z. Optimization of adobe clay bricks based on the raw material properties (mathematical analysis). in Construction and Building Materials. 2020;244. doi:10.1016/j.conbuildmat.2020.118342 conv_841 .
Vasić, Milica V., Pezo, Lato, Radojević, Zagorka, "Optimization of adobe clay bricks based on the raw material properties (mathematical analysis)" in Construction and Building Materials, 244 (2020), https://doi.org/10.1016/j.conbuildmat.2020.118342 ., conv_841 .