Study on multi-objective optimization algorithm of permeable concrete design parameters on its permeability

The properties of permeable concrete are affected by design factors including the water-cement ratio, porosity, aggregate gradation, and fiber content, and these parameters are mutually restricted, which makes it a design problem to achieve sufficient mechanical properties while ensuring water permeability. In this study, by examining how various molding techniques and additional materials affect the properties, a multi-objective optimization model was established to balance water permeability and mechanical properties, and genetic algorithm (GA) was selected to solve this problem, because it has the ability of global search and the advantage of dealing with complex variables. In the experimental analysis part, ordinary portland cement, continuously graded macadam, natural river sand and other materials are used, and the initial mixture ratio range is set according to the design parameter range of multi-objective optimization model, and concrete specimens are prepared, the compressive strength and permeability coefficient are evaluated. The results show that Pareto frontier shows the trade-off between water permeability and mechanical properties of different design parameter combinations. With the increase of water permeability coefficient, the difference between compressive strength and ideal value also increases. A representative combination of design parameters is selected from Pareto frontier solution set for detailed analysis, and the concrete specimen under this combination has good water permeability and compressive strength. In addition, the permeability coefficient after optimization is generally higher than that before optimization, which shows that the optimization algorithm is effective in improving the permeability of permeable concrete. By constructing a multi-objective optimization model and selecting GA to optimize the design parameters of pervious concrete, this study successfully improves the pervious performance of concrete while maintaining good mechanical properties, which provides new ideas and methods for the design of pervious concrete and helps to promote the wide application of pervious concrete in urban construction.

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Publication Details

Journal
IET conference proceedings.
Published
2025-10-01
DOI
https://doi.org/10.1049/icp.2025.2964
Primary Topic
BIM and Construction Integration
Type
article
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article

Study on multi-objective optimization algorithm of permeable concrete design parameters on its permeability

Wen‐Sheng Zhao
IET conference proceedings.
BIM and Construction Integration
article

Study on multi-objective optimization algorithm of permeable concrete design parameters on its permeability

Wen‐Sheng Zhao
article en

Abstract

The properties of permeable concrete are affected by design factors including the water-cement ratio, porosity, aggregate gradation, and fiber content, and these parameters are mutually restricted, which makes it a design problem to achieve sufficient mechanical properties while ensuring water permeability. In this study, by examining how various molding techniques and additional materials affect the properties, a multi-objective optimization model was established to balance water permeability and mechanical properties, and genetic algorithm (GA) was selected to solve this problem, because it has the ability of global search and the advantage of dealing with complex variables. In the experimental analysis part, ordinary portland cement, continuously graded macadam, natural river sand and other materials are used, and the initial mixture ratio range is set according to the design parameter range of multi-objective optimization model, and concrete specimens are prepared, the compressive strength and permeability coefficient are evaluated. The results show that Pareto frontier shows the trade-off between water permeability and mechanical properties of different design parameter combinations. With the increase of water permeability coefficient, the difference between compressive strength and ideal value also increases. A representative combination of design parameters is selected from Pareto frontier solution set for detailed analysis, and the concrete specimen under this combination has good water permeability and compressive strength. In addition, the permeability coefficient after optimization is generally higher than that before optimization, which shows that the optimization algorithm is effective in improving the permeability of permeable concrete. By constructing a multi-objective optimization model and selecting GA to optimize the design parameters of pervious concrete, this study successfully improves the pervious performance of concrete while maintaining good mechanical properties, which provides new ideas and methods for the design of pervious concrete and helps to promote the wide application of pervious concrete in urban construction.

IET conference proceedings.Vol. 2025(25)
Sino Biological (China) (CN), Xi'an Jiaotong University (CN)
Openalex Percentile: Top 67%
BIM and Construction Integration
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