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

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

引用次数: 0

Abstract

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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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

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 ...

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

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.