Response surface regression and machine learning models to predict the porosity and compressive strength of pervious concrete based on mix design parameters
IF 3.4
3区 工程技术
Q2 CONSTRUCTION & BUILDING TECHNOLOGY
引用次数: 0
Abstract
This study investigates the influence of aggregate size, aggregate-to-cement ratio, and compaction effort on pervious concrete's porosity and compressive strength. It proposes using response surfac...基于混合设计参数预测透水混凝土孔隙率和抗压强度的响应面回归和机器学习模型
本研究探讨了骨料粒度、骨料与水泥比以及压实力度对透水混凝土孔隙率和抗压强度的影响。它建议使用响应表面...
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来源期刊
Road Materials and Pavement Design
工程技术-材料科学：综合
CiteScore
8.10
自引率
8.10%
发文量
105
审稿时长
3 months
期刊介绍：
The international journal Road Materials and Pavement Design welcomes contributions on mechanical, thermal, chemical and/or physical properties and characteristics of bitumens, additives, bituminous mixes, asphalt concrete, cement concrete, unbound granular materials, soils, geo-composites, new and innovative materials, as well as mix design, soil stabilization, and environmental aspects of handling and re-use of road materials.
The Journal also intends to offer a platform for the publication of research of immediate interest regarding design and modeling of pavement behavior and performance, structural evaluation, stress, strain and thermal characterization and/or calculation, vehicle/road interaction, climatic effects and numerical and analytical modeling. The different layers of the road, including the soil, are considered. Emerging topics, such as new sensing methods, machine learning, smart materials and smart city pavement infrastructure are also encouraged.
Contributions in the areas of airfield pavements and rail track infrastructures as well as new emerging modes of surface transportation are also welcome.
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