建筑废料回收:废料衍生水泥替代品的机械性能和预测模型

Waste Management Bulletin Pub Date : 2025-04-01 Epub Date: 2025-01-21 DOI:10.1016/j.wmb.2025.01.004
Moutaman M. Abbas
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摘要

工业化程度的提高导致天然建筑材料的短缺,从而提高了建筑公司对可持续发展方法的认识。本研究阐述了如何将陶瓷废粉、废玻璃粉、废花岗石废粉、废大理石废粉、废砖废粉等废料作为环保水泥替代品加入混凝土混合物中。目的是研究这些补充胶凝材料的机械特性,不断回收工业废物,促进环境可持续发展。除了实验结果之外,还开发了一个神经网络模型来预测含有这些材料的混凝土的抗压强度,并对从文献中收集的数据进行了训练。该模型成功地证明了其在不同替换水平下复制抗压强度结果趋势的能力,验证了研究结果并提高了研究的可靠性。在不同的养护期,对水泥在混凝土中的替代水平进行了测试,从5%到50%,对抗压强度和抗拉强度进行了测试。试验结果表明,10 - 15%的替代水平在大多数废物的最佳范围内。抗压和抗拉强度的提高在养护28天左右达到最大。剂量的增加导致机械性能的损失,表明更高替代百分比的可行性有限。本综述在机器学习预测的支持下,强调了这些材料在改善建筑行业可持续实践方面的潜力,以制造低环境影响和资源效率的补充水泥材料(scm)。
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Recycling waste materials in construction: Mechanical properties and predictive modeling of Waste-Derived cement substitutes
Increased industrialization has resulted in a shortage of natural building materials, thus increasing awareness of sustainable approaches by construction companies. This research explains how waste materials—Ceramic Waste Powder, Waste Glass Powder, Waste Granite Dust, Waste Marble Powder, and Waste Brick Powder—can be employed as environmentally friendly cement alternatives in concrete mixtures. The objective is to study the mechanical characteristics of these supplementary cementitious materials with continuous industrial waste recycling for environmentally sustainable development. In addition to experimental findings, a neural network model was developed to predict the compressive strength of concrete containing these materials, trained on data collected from the literature. The model successfully demonstrated its ability to replicate trends in compressive strength results across varying replacement levels, validating the findings and enhancing the study’s reliability. Tests were carried out for replacement levels of cement by the materials in concrete, from 5 % to 50 %, on compressive and tensile strengths at various curing periods. The test results show that a 10–15 % replacement level is within the optimum range for most of the waste materials. It is also observed that compressive and tensile strength improvement tends to be maximum around 28 days of curing. Increases in dosage lead to a loss in mechanical properties, indicating limited viability for higher replacement percentages. The present review, supported by machine learning predictions, highlights the potential of these materials to improve sustainable practices in the building industries, toward manufacturing Supplementary Cementitious Materials (SCMs) with low environmental impact coupled with resource efficiency.
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