含粉煤灰骨料混凝土的抗渗性评估及模型预测

G. Lalitha, Chilukala Ritvik Reddy
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摘要

混凝土是建筑行业使用最多的合成材料,其成分之一就是骨料。在混凝土制备过程中使用天然骨料会消耗大量不可再生资源和能源,对环境造成严重影响。为了保护自然保护区,寻求废物处理问题的解决方案,以及通过利用废料降低建筑成本,已经开展了多个研究项目。FA(粉煤灰)骨料就是这样一种可以替代天然骨料的材料。本研究对使用粉煤灰(FA)骨料作为细骨料替代品的混凝土耐久性参数进行了研究。在这项研究中,使用粉煤灰骨料制备了 5 种混凝土混合料,每种混合料的替代率分别为 0%、10%、20%、30% 和 40%。水泥用量、压实度、养护率、混凝土覆盖率和孔隙率都会影响混凝土的耐久性。对混凝土的压缩强度、耐磨性和半电池电位等属性进行了研究。试样的耐久性参数在 90 天养护后进行了测试。结果显示,含 30% FA 骨料的混凝土抗压强度最高,耐磨性更好,半电池电位值最小。实验数据通过应用支持向量机(SVM)算法建立了综合预测模型。SVM 模型分析 R2 值的准确率超过 97%。因此,我们可以利用 SVM 在建筑领域有效地执行预测建模。
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Impermeability Evaluation Of Concrete With Fly Ash Aggregate And Prediction With Modelling
Concrete is the most synthesized material in construction sector which it has aggregate as one of its components. The use of natural aggregate in concrete preparation uses a significant amount of non-renewable resources and energy, having a significant environmental impact. Multiple research projects have been conducted to safeguard natural reserves, seeking a solution to the waste disposal issue, and reduce construction costs by utilizing waste materials. FA(Fly Ash) aggregate is one such material that can be a replacement for natural aggregate. Durability parameters of concrete with Fly Ash (FA) aggregate are studied in this work as an alternative for fine aggregate. In this study, 5 concrete mixes were prepared utilizing FA aggregate in percentage substitution of 0%, 10%, 20%, 30%, and 40% for each. The quantity of cement, compaction, curing rate, concrete cover, and porosity all influence the durability of the concrete. Concrete attributes such as strength in compression, retaliation to abrasion and half-cell potential were investigated. Durability parameters of the specimens were tested after 90-day curing. The results revealed that concrete with 30% FA aggregate had the highest compressive strength, improved resistance towards abrasion and least half cell potential values. Experimentation data were used to develop comprehensive prediction models by applying support vector machine (SVM) algorithm. The SVM model analyses R2 values with an accuracy of over 97%. As a result, we can use SVM to efficiently execute prediction modelling in construction area.
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