比较预测绿色工程材料抗压强度的新型混合集合学习器:以稻壳灰混凝土为例

IF 2.2 4区 工程技术 Q2 ENGINEERING, CIVIL European Journal of Environmental and Civil Engineering Pub Date : 2024-04-08 DOI:10.1080/19648189.2024.2335343
Ali Reza Ghanizadeh, Amir Tavana Amlashi, Samer Dessouky, Seyed Abolfazl Ebrahimi
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引用次数: 0

摘要

与典型的机器学习(ML)算法相比,集合学习(EL)能够提高预测的精确度,因此其应用日益广泛。基于集合学习的方法预计将更加 ...
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A comparison of novel hybrid ensemble learners to predict the compressive strength of green engineering materials: a case of concrete composed of rice husk ash
The use of ensemble learning (EL) has grown due to its ability to enhance precision in predictions compared to typical machine learning (ML) algorithms. EL-based approaches are expected to be more ...
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来源期刊
European Journal of Environmental and Civil Engineering
European Journal of Environmental and Civil Engineering ENGINEERING, CIVIL-ENGINEERING, GEOLOGICAL
CiteScore
4.80
自引率
4.80%
发文量
153
审稿时长
6 months
期刊介绍: The European Research Area has now become a reality. The prime objective of the EJECE is to fully document advances in International scientific and technical research in the fields of sustainable construction and soil engineering. In particular regard to the latter, the environmental preservation of natural media (soils and rocks) and the mitigation of soil-related risks are now not only major societal challenges, but they are also the source of scientific and technical developments that could be extremely beneficial.
期刊最新文献
Influence of aggregate size on pervious concrete properties with and without construction and demolition waste Failure mechanism of a tunnel in a soil–rock mixture based on a new combined finite–discrete element method Development of sustainable HPC using rubber powder and waste wire: carbon footprint analysis, mechanical and microstructural properties Calcium hydroxide in geopolymers – a critical overview Investigation on the aircraft impact resistance of Steel-concrete construction
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