A comprehensive review on pervious concrete

Adresi, Mostafa; Yamani, Alireza; Karimaei Tabarestani, Mojtaba; Rooholamini, Hamed

doi:10.1016/j.conbuildmat.2023.133308

Cited by 30 publications

(7 citation statements)

References 168 publications

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“…Table 2 provides the sources and relevant component information for these 111 sets of data. Given that the compressive strength of pervious concrete is typically lower than that of conventional concrete and varies within the range of 2-28 MPa [22], although advances in research have led to improvements in the compressive strength of pervious concrete, it still remains lower than that of conventional concrete. Based on this, we searched for pervious concrete samples within the compressive strength range of 2 to 40 MPa for model training and prediction.…”

Section: Data Sourcementioning

confidence: 99%

“…While previous studies have derived variation patterns of compressive strength in pervious concrete [22] with specific material configurations based on experimental materials, proposing empirical formulas, Table 1 illustrates some of these formulas and their predictive effectiveness. However, the empirical formulas may not precisely predict the compressive strength of pervious concrete with different materials and mix proportions as the scope of application widens and new materials are developed.…”

Section: Introductionmentioning

confidence: 99%

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The Prediction of Pervious Concrete Compressive Strength Based on a Convolutional Neural Network

Yu,

Zhu,

Xiang

2024

Buildings

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To overcome limitations inherent in existing mechanical performance prediction models for pervious concrete, including material constraints, limited applicability, and inadequate accuracy, this study employs a deep learning approach to construct a Convolutional Neural Network (CNN) model with three convolutional modules. The primary objective of the model is to precisely predict the 28-day compressive strength of pervious concrete. Eight input variables, encompassing coarse and fine aggregate content, water content, admixture content, cement content, fly ash content, and silica fume content, were selected for the model. The dataset utilized for both model training and testing consists of 111 sample sets. To ensure the model’s coverage within the practical range of pervious concrete strength and to enhance its robustness in real-world applications, an additional 12 sets of experimental data were incorporated for training and testing. The research findings indicate that, in comparison to the conventional machine learning method of Backpropagation (BP) neural networks, the developed CNN prediction model in this paper demonstrates a higher coefficient of determination, reaching 0.938, on the test dataset. The mean absolute percentage error is 9.13%, signifying that the proposed prediction model exhibits notable accuracy and universality in predicting the 28-day compressive strength of pervious concrete, regardless of the materials used in its preparation.

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Section: Data Sourcementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Prediction of Pervious Concrete Compressive Strength Based on a Convolutional Neural Network

Yu,

Zhu,

Xiang

2024

Buildings

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“…A variety of studies have been conducted to propose novel techniques for improving pervious concrete materials [1][2][3][4]. Research by Zheng et al [5] focused on the creation of alkali-activated metakaolin pervious concrete, incorporating copper slag as an innovative component.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Ferronickel Slag-Ordinary Portland Cement Pervious Concrete

Tang,

Peng,

Mei

et al. 2024

Materials

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It is unknown whether Ferronickel slag (FNS)-ordinary Portland cement (OPC)-based pervious concrete (FOPC) is feasible. To this end, a feasibility study was conducted on FOPC. Firstly, a detailed microscopic examination of the FNS powder was conducted, encompassing analyses of its particle size distribution, SEM, EDS, and chemical composition. These analyses aimed to establish the suitability of a composite of FNS and OPC as a composite cementitious material. Subsequent experimentation focused on evaluating the compressive strength of the composite paste material with varying mixed proportions, revealing a slight reduction in strength as the FNS substitution rate increased. Furthermore, the study designed eighteen different mix proportions of FOPC to investigate the key physical properties, including porosity, density, compressive strength, and the coefficient of permeability. Findings indicated that increases in the cementitious material proportion correlate with enhanced concrete strength, where the ratio of cementitious to aggregate increased by 6.7% and 16.5%, and the strength of FOPC increased by 10–13% and 30–50%, respectively. Conversely, a rise in the FNS substitution rate led to a reduction in compressive strength across different mix ratios. Additionally, the ratio of paste material to aggregate was found to significantly influence the permeability coefficient. These comprehensive performance evaluations suggest that incorporating FNS into OPC for pervious concrete applications is a feasible approach, offering valuable insights for the promotion of waste reuse and the advancement of energy conservation and emissions reduction efforts.

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“…Many sidewalks in the city cover around 20-40% of the area, preventing rainwater from infiltrating the soil underneath [1], [2]. This leads to flooding in urban areas, causing damage to the infrastructure and becoming a serious problem for citizens who lose access to clean water.…”

Section: Introductionmentioning

confidence: 99%

“…This leads to flooding in urban areas, causing damage to the infrastructure and becoming a serious problem for citizens who lose access to clean water. The standing water eventually turns into wastewater, which increases the risk of contamination [2]. Thus, it became important to develop suitable material to cover these problems.…”

Section: Introductionmentioning

confidence: 99%

Improving the mechanical behavior of pervious concrete using polypropylene and waste rope fibers

Alkraway,

Shereen,

Salah Nasr

et al. 2024

QJES

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Urbanization has led to the damage of infrastructure due to floods and water accumulation on roads and sidewalks. To address this problem, pervious concrete was designed to drain water smoothly. However, pervious concrete has certain drawbacks, such as brittleness and poor tensile strength. To overcome these shortcomings, it is reinforced with fiber. Polypropylene fibers are commonly used for this purpose. On the other hand, managing waste plastic is a major problem as it has a significant environmental impact and requires large areas for landfills. Waste rope fibers (WRF) are among these wastes. There have been very limited investigations on the use of WRF in pervious concrete. Therefore, this study aims to investigate the effect of polypropylene (PP) fibers and waste rope fibers (WRF) on the mechanical and structural properties of pervious concrete. PP and WRF fibers were added in proportions of 0.25%, 0.5%, and 0.75% by volume of concrete. A range of tests (compressive strength, tensile strength, density, permeability, load-deflection behavior, and ductility) were conducted to evaluate the resulting concrete. The results indicated that although the permeability was decreased by adding fibers, the fibers significantly improved the mechanical and structural properties of pervious concrete. The highest values for compressive strength, splitting tensile strength, and ultimate load were 83.4%, 72.4%, and 89.62% for PP fibers-based mixtures, while they were 49.9%, 41.9%, and 102.83% for mixtures made with WRF at an addition rate of 0.5% for both types of fibers. The results also demonstrated that the existence of fibers improved the ductility of the concrete, which means that WRF can be used successfully in producing eco-friendly pervious concrete with better performance than the control specimen.

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A comprehensive review on pervious concrete

Cited by 30 publications

References 168 publications

The Prediction of Pervious Concrete Compressive Strength Based on a Convolutional Neural Network

The Prediction of Pervious Concrete Compressive Strength Based on a Convolutional Neural Network

Performance Analysis of Ferronickel Slag-Ordinary Portland Cement Pervious Concrete

Improving the mechanical behavior of pervious concrete using polypropylene and waste rope fibers

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