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粉煤灰和硅灰石作为可持续胶凝材料的合成与性能评价研究。

A study on the synthesis and performance evaluation of fly ash and alccofine as sustainable cementitious materials.

作者信息

Padavala Siva Shanmukha Anjaneya Babu, Noolu Venkatesh, Paluri Yeswanth, Bijivemula Sudheer Kumar Reddy, Akula Uday Kumar

机构信息

Department of Civil Engineering, Gudlavalleru Engineering College, Gudlavalleru, Andhra Pradesh, India.

Department of Civil Engineering, Sreenidhi Institute of Science and Technology, Hyderabad, India.

出版信息

Sci Rep. 2024 Aug 18;14(1):19115. doi: 10.1038/s41598-024-67519-3.

DOI:10.1038/s41598-024-67519-3
PMID:39155304
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11330979/
Abstract

Construction and global infrastructure depend on cement production. It is one of the biggest carbon emitters, making it an aspect of environmental sustainability and climate change mitigation. Each stage of cement production releases CO and other greenhouse gasses. About 8% of worldwide CO emissions come from the cement sector, making it a major contributor. Different supplementary cementitious materials (SCMs) like fly ash (FA), silica fume (SF), and slag are used to partially replace traditional raw materials like limestone, reducing the environmental impact. This study investigated the use of supplementary cementitious materials, specifically FA and alccofine (AF), as partial replacements for cement in concrete to reduce environmental impact. The study first identified an optimal replacement percentage for FA (20%, 30%, and 40%) by weight of cement. Subsequently, using the optimal FA percentage, AF was added at varying percentages (5%, 10%, and 15%) by weight of cement. The study evaluated the mechanical properties of the concrete mixtures, including workability, compressive strength, split tensile strength, and flexural strength. Durability, measured by water sorptivity and rapid chloride penetrability tests, was also assessed. The microstructural properties of the concrete were analyzed to understand their influence on performance. As a result of the significant environmental implications of producing and using concrete for all activities, an in-depth life cycle assessment (LCA) was conducted. Additionally, artificial neural networks were employed to predict the compressive strength of the concrete. The study concluded that incorporating FA and AF in concrete mixtures is a promising approach to producing more environmentally friendly concrete.

摘要

建筑和全球基础设施依赖于水泥生产。它是最大的碳排放源之一,因此成为环境可持续性和缓解气候变化的一个方面。水泥生产的每个阶段都会释放一氧化碳和其他温室气体。全球约8%的一氧化碳排放来自水泥行业,使其成为主要贡献者。不同的辅助胶凝材料(SCMs),如粉煤灰(FA)、硅灰(SF)和矿渣,被用于部分替代石灰石等传统原材料,以减少环境影响。本研究调查了使用辅助胶凝材料,特别是粉煤灰和硅粉(AF),作为混凝土中水泥的部分替代品以减少环境影响。该研究首先确定了粉煤灰按水泥重量计的最佳替代百分比(20%、30%和40%)。随后,使用最佳的粉煤灰百分比,并按水泥重量的不同百分比(5%、10%和15%)添加硅粉。该研究评估了混凝土混合物的力学性能,包括工作性、抗压强度、劈裂抗拉强度和抗弯强度。还通过吸水率和快速氯离子渗透性试验评估了耐久性。分析了混凝土的微观结构特性,以了解它们对性能的影响。由于生产和使用混凝土对所有活动都有重大的环境影响,因此进行了深入的生命周期评估(LCA)。此外,还采用了人工神经网络来预测混凝土的抗压强度。该研究得出结论,在混凝土混合物中掺入粉煤灰和硅粉是生产更环保混凝土的一种有前景的方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce09/11330979/1fcc5c2d8e1d/41598_2024_67519_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce09/11330979/8100323dad11/41598_2024_67519_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce09/11330979/3758b61c0c0e/41598_2024_67519_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce09/11330979/ee49a55ff899/41598_2024_67519_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce09/11330979/1fcc5c2d8e1d/41598_2024_67519_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce09/11330979/8100323dad11/41598_2024_67519_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce09/11330979/3758b61c0c0e/41598_2024_67519_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce09/11330979/ee49a55ff899/41598_2024_67519_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce09/11330979/1fcc5c2d8e1d/41598_2024_67519_Fig9_HTML.jpg

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Compressive strength prediction of high-strength oil palm shell lightweight aggregate concrete using machine learning methods.基于机器学习方法的高强油棕壳轻集料混凝土抗压强度预测
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The Numerical Analysis of Replenishment of Hydrogel Void Space Concrete Using Hydrogels Containing Nano-Silica Particles through ELM-ANFIS.
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