ArticlePublisher preview available

A mathematical model to predict the porosity and compressive strength of pervious concrete based on the aggregate size, aggregate-to-cement ratio and compaction effort

Authors:
Sathushka Heshan Wijekoon at University of Jaffna
Sathushka Heshan Wijekoon
Thirugnanasivam Shajeefpiranath at University of Jaffna
Thirugnanasivam Shajeefpiranath
Daniel Niruban Subramaniam at University of Jaffna
Daniel Niruban Subramaniam
Navaratnarajah Sathiparan at University of Jaffna
Navaratnarajah Sathiparan
Read publisher preview
Download citation
To read the full-text of this research, you can request a copy directly from the authors.

Abstract and Figures

The purpose of this study is to explore how characteristics of pervious concrete, such as porosity and compressive strength, are affected by three factors: aggregate size, aggregate-to-cement ratio and compaction effort. The study used three aggregate sizes (5–12 mm, 12–18 mm and 18–25 mm), five different aggregate-to-cement ratios (3.0, 3.5, 4.0, 4.5 and 5.0) and seven different levels of compaction effort (0, 15, 30, 45, 60, 75 and 90 blows). A total of 15 mix designs were used to cast 630 pervious concrete cubes, which were then tested for porosity and compressive strength. The test data were analysed to establish mathematical relationships between the three factors and the two characteristics of pervious concrete. The models accurately predicted the porosity and compressive strength based on the aggregate size, aggregate-to-cement ratio and compaction effort. These models can be useful for practitioners and researchers in optimizing pervious concrete mix designs for various applications.
Figures - available from: Asian Journal of Civil Engineering
This content is subject to copyright. Terms and conditions apply.
A preview of this full-text is provided by Springer Nature.
Learn more
Content available from Asian Journal of Civil Engineering
This content is subject to copyright. Terms and conditions apply.
Vol.:(0123456789)
1 3
Asian Journal of Civil Engineering (2024) 25:67–79
https://doi.org/10.1007/s42107-023-00757-4
RESEARCH
A mathematical model topredict theporosity andcompressive
strength ofpervious concrete based ontheaggregate size,
aggregate‑to‑cement ratio andcompaction effort
SathushkaHeshanWijekoon1· ThirugnasivamShajeefpiranath1· DanielNirubanSubramaniam1·
NavaratnarajahSathiparan1
Received: 28 May 2023 / Accepted: 5 June 2023 / Published online: 12 June 2023
© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023
Abstract
The purpose of this study is to explore how characteristics of pervious concrete, such as porosity and compressive strength,
are affected by three factors: aggregate size, aggregate-to-cement ratio and compaction effort. The study used three aggre-
gate sizes (5–12mm, 12–18mm and 18–25mm), five different aggregate-to-cement ratios (3.0, 3.5, 4.0, 4.5 and 5.0) and
seven different levels of compaction effort (0, 15, 30, 45, 60, 75 and 90 blows). A total of 15 mix designs were used to cast
630 pervious concrete cubes, which were then tested for porosity and compressive strength. The test data were analysed to
establish mathematical relationships between the three factors and the two characteristics of pervious concrete. The models
accurately predicted the porosity and compressive strength based on the aggregate size, aggregate-to-cement ratio and com-
paction effort. These models can be useful for practitioners and researchers in optimizing pervious concrete mix designs for
various applications.
Keywords Pervious concrete· Compaction· Aggregate size· Porosity· Compressive strength
Introduction
Pervious concrete is a mixture of cement, coarse aggregate
and water with high permeability and environmental ben-
efits. It is widely used in the last 40years around the world,
predominantly for pervious paving (Aamer Rafique Bhutta
etal., 2013). Pervious concrete allows water to drain and
infiltrate through its pores, which reduces surface runoff
and flash floods and enhances the ecological performance
of the catchment. For example, it can be used in parking
lots to increase base flow, preserve water quality and effi-
ciently manage parking space. It also prevents surface glare
and reflection at night, which improves driver comfort and
safety and creates a quiet environment. In addition, the gaps
in pervious concrete may retain heat, which alters the earth's
surface temperature and humidity (Yang & Jiang, 2003).
The porosity and compressive strength of pervious con-
crete are the two key determinants of its suitability for vari-
ous applications. Porosity is the measure of voids or hol-
low spaces in the concrete matrix that formed by the gaps
between the cement paste-coated aggregates. It is quantified
as a percentage of the volume of voids of the total volume of
the mixture. The porosity of pervious concrete was observed
to vary between 15 and 35%, making it permeable (Chan-
drappa & Biligiri, 2016; Li etal., 2017; Xu etal., 2018).
By adjusting the mix proportion, it can be designed as an
appropriate construction material for pavements, railroads
and boundary walls. Its compressive strength ranges from 3
to 30MPa (Alemu etal., 2021). The characteristics of pervi-
ous concrete depend on the components, such as cement and
aggregates, and the related variables, such as aggregate-to-
cement (A/C) ratio, aggregate size, water-to-cement (W/C)
ratio and compaction energy (Anburuvel & Subramaniam,
2022).
The aggregate-to-cement ratio is a key parameter that
influences the properties of pervious concrete, specifically
in the binder coating thickness of aggregates. Many stud-
ies have reported that a higher A/C ratio leads to higher
porosity, but lower compressive strength. This is because
* Navaratnarajah Sathiparan
sakthi@eng.jfn.ac.lk
1 Department ofCivil Engineering, Faculty ofEngineering,
University ofJaffna, Ariviyal Nager, Kilinochchi, SriLanka
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
... Although several computational methods have been proposed by studies in contemporary literature to define the shape of an aggregate, they are mostly confined to the context in which the model was either developed or employed (Anburuvel and Subramaniam 2022, 2024, Wijekoon 2024). Literature has not established a universal representation of the shape of an aggregate, that could be used in a mathematical performance model, to incorporate the impact of shape of the aggregate on the performance of the material. ...
... Although several computational methods have been proposed by studies in contemporary literature to define the shape of an aggregate, they are mostly confined to the context in which the model was either developed or employed (Anburuvel and Subramaniam 2022, 2024, Wijekoon 2024). Literature has not established a universal representation of the shape of an aggregate, that could be used in a mathematical performance model, to incorporate the impact of shape of the aggregate on the performance of the material. ...
View
... This approach is proposed to reduce reliance on laboratory testing, which can generate construction waste, and empower engineers with accurate prediction tools Subramaniam et al., 2023). Traditional mathematical models may struggle to capture the intricacies of these relationships, whereas ML can offer effective solutions for both linear and non-linear scenarios (Gao et al., 2019;Sathiparan et al., 2023;Wijekoon et al., 2023). ...
Prediction model for compressive strength of rice husk ash blended sandcrete blocks using a machine learning models
Article
Full-text available
  • May 2024
Sandcrete blocks are popular for construction but their production relies on cement, which is a major contributor to greenhouse gases. Rice husk ash (RHA), a waste product, can partially replace cement in sandcrete blocks. This study uses machine learning (ML) to predict the compressive strength of these blocks, which is influenced by factors such as the ratio of fine aggregate to binder, RHA to binder ratio, water-to-binder ratio, and curing time. The data were collected from published literature on factors affecting compressive strength from various sources and analyzed 795 observations. The analysis showed that strength increases with longer curing but decreases with higher ratios of aggregate-to-binder, RHA-to-binder, and water-to-binder. The data were divided for training and testing ML models. Five algorithms were investigated, and the eXtreme Gradient Boosting (XGB) model emerged as the best for predicting compressive strength. The XGB model strongly correlated with predicted and measured strength, with an R² value of 0.94 for training data and 0.89 for testing data. It also displayed lower error metrics compared to other models. XGB's success is due to its ability to handle complex relationships and prevent overfitting. This study highlights the potential of ML for predicting the strength of RHA-blended sandcrete blocks.
View
... Unfortunately, none of these studies considers the compaction effect for forecasting the properties of pervious concrete. But, numerous studies demonstrate that compaction heavily influences the properties of pervious concrete, especially on porosity and compressive strength[46].Table 1. List of machine learning methods used to predict the properties of pervious concrete ...
Prediction of Characteristics of Pervious Concrete by Machine Learning Technique Using Mix Parameters and Non-destructive Test Measurements
Preprint
Full-text available
  • May 2024
Ensuring quality in pervious concrete poses challenges, limiting its use. This work investigates the potential of machine learning to forecast its properties, offering a novel and accessible approach. Five machine learning techniques were employed on 300 experimental data points, considering mix parameters (aggregate size, ratio, compaction) and non-destructive measurement (ultrasonic velocity, resistivity). Artificial Neural Networks (ANNs) excelled, achieving high accuracy (R ² > 0.97) for prediction of porosity and compressive strength. Sensitivity analysis revealed the dominant influence of compaction energy, aggregate-to-cement ratio, and ultrasonic velocity, while aggregate size and resistivity had minimal impact. This study suggests that machine learning models, particularly ANNs, can be reliable and efficient for predicting pervious concrete properties. This has the potential to improve quality control and encourage broader adoption in the construction sector, ultimately leading to more sustainable and permeable infrastructure.
View
... More advanced methods could reduce the need for laboratory tests and offer engineers simple tools to predict experimental outcomes (Ahmed et al. 2022). Machine learning methods can also provide alternatives and answers for both linear and nonlinear situations where mathematical models cannot capture the relationship between the variables involved in a problem (Gao et al. 2019, Anburuvel and Subramaniam 2022b, Wijekoon et al. 2023. Table 1 briefly overviews the machine learning approach used to predict permeable concrete properties. ...
View
... Due to its numerous advantageous characteristics that reduce Editorial responsibility: C. Li. the adverse effect of impermeable catchments, pervious concrete has recently gained prominence in research and application. Due to high porosity and absence of fines, the required amount of cement is relatively high in pervious concrete to achieve good mechanical characteristics, specifically strengths (Shen et al. 2020;Wijekoon et al. 2023). ...
Utilization of supplementary cementitious materials in pervious concrete: a review
Article
  • Jan 2024
  • INT J ENVIRON SCI TE
Pervious concrete is an environmentally sustainable solution for urban catchment paving. Due to no fines in pervious concrete, the usage of cement is comparatively higher, which demands the usage of supplementary cementitious materials partially substituting cement to reduce environmental footprint. This study critically analyses research on pervious concrete with supplementary cementitious materials substitutions including silica fume, fly ash, metakaolin, lime-stone-powder, glass-powder, rice-husk-ash, granulated-blast-furnace-slag, sorghum-husk-ash, and palm-oil-fuel-ash. The impact on the strength of pervious concrete was more pronounced owing to the impact of supplementary cementitious materials in hydration processes, depending on the chemical constituents of supplementary cementitious materials. Pervious concrete with silica fume and fly ash has the best normalized compressive strength varied from 1.01 to 2.94 times in the replacement range of 5–15% for silica fume and varied from 1.05 to 2.20 times in the replacement range of 20–30% for fly ash, respectively. The optimum splitting tensile and flexural strength was observed for 10% replacement for several supplementary cementitious materials. The maximum improvements in flexural and splitting strength of pervious concrete with silica fume are 90 and 63%, respectively. The durability of pervious concrete was significantly affected by the presence of supplementary cementitious materials in pervious concrete. Pervious concrete with metakaolin had the best acid resistance with a replacement level of 15%, Because of larger particle size, FA affected negatively on abrasion resistance. Studies with multiple (binary) mixes of supplementary cementitious materials have also been shown to have significantly improved pervious concrete performance. The inclusion of supplementary cementitious materials in pervious concrete increases the environmental sustainability of pervious concrete.
View
... Furthermore, engineers must possess the necessary tools to accurately forecast the results of tests . Machine learning methods can be employed to offer alternative strategies and solutions for both linear and non-linear situations when traditional mathematical models may exhibit imprecision in capturing the relationships among the variables related to a specific problem (Gao et al. 2019, Wijekoon et al. 2023. ...
View
... In addition, engineers must possess the requisite tools to effectively predict the outcomes of experiments [34,35]. Machine learning (ML) techniques may be used to provide alternate approaches and resolutions for both linear and non-linear scenarios in cases where conventional mathematical models may lack precision in capturing the interdependencies among the variables associated with a particular issue [34,36,37]. ...
Use of soft computing approaches for the prediction of compressive strength in concrete blends with eggshell powder
Article
Full-text available
  • Dec 2023
The present study showcases a prediction model for estimating the compressive strength of concrete combined with ESP utilizing machine-learning techniques. The models were created using 399 datasets that were sourced from published literature. The datasets included a range of input factors, including cement content, ESP content, fine aggregate content, coarse aggregate content, water content, and curing duration. The models used the compressive strength of ESP mixed concrete as the output variable. This research used a collection of seven machine learning algorithms, namely linear regression, artificial neural network, boosted decision tree regression, K nearest neighbors, random forest regression, support vector regression, and XGboost as statistical evaluation tools in order to determine the best accurate and dependable model for forecasting the compressive strength of ESP mixed concrete. Among the machine-learning models assessed in this investigation, the XGboost model has shown exceptional efficacy in forecasting compressive strength. It attained an R² value of 0.99 and an RMSE of 0.99 MPa for the training dataset while reaching an R² value of 0.82 and an RMSE of 4.48 MPa for the testing dataset. The sensitivity analysis results of the XGboost model indicate that the compressive strength of the material is mostly affected by the curing period. The compressive strength of a material is also significantly impacted by the amounts of cement and water in the mix.
View
View
View
Prediction model for compressive strength of rice husk ash blended sandcrete blocks using a machine learning models
Preprint
Full-text available
  • May 2024
Sandcrete blocks are popular for construction but their production relies on cement, a major greenhouse gas contributor. Rice husk ash (RHA), a waste product, can partially replace cement in sandcrete blocks. This study uses machine learning (ML) to predict the compressive strength of these blocks, influenced by factors like the ratio of fine aggregate to binder, RHA to binder ratio, water-to-binder ratio, and curing time. The data was collected from published literature on factors affecting compressive strength from various sources and analyzed 795 observations. The analysis showed that strength increases with longer curing but decreases with higher ratios of aggregate-to-binder, RHA-to-binder, and water-to-binder. The data was divided for training and testing ML models. Five algorithms were investigated, and the eXtreme Gradient Boosting (XGB) model emerged as the best for predicting compressive strength. The XGB model strongly correlated with predicted and measured strength, with an R² value of 0.94 for training data and 0.89 for testing. It also displayed lower error metrics compared to other models. XGB's success is due to its ability to handle complex relationships and prevent overfitting. This study highlights the potential of ML for predicting the strength of RHA-blended sandcrete blocks.
View
Effect of Aggregate Size and Compaction on the Strength and Hydraulic Properties of Pervious Concrete
Article
Full-text available
  • Jan 2023
Pervious concrete is one of the emerging sustainable materials that has recently gained the attention of many researchers. The importance of pervious concrete mainly depends on its application and on a modern integrated approach in which it is employed to reduce the effects of flooding. The main goal of this experimental analysis is to study the significance of aggregate size and the degree of compaction on the mechanical and hydraulic properties of pervious concrete. Eleven concrete mixture proportions were investigated by controlling the constituents with different aggregate fractions. The important variables considered were the aggregate sizes, viz., 0/4 mm, 4/8 mm, and 8/16 mm, with four different degrees of compaction. The porosity of the concrete structure was obtained by the partial filling of the voids in the aggregates with cement paste. The ingredients of the pervious concrete were also varied to study their significance and to evaluate the predominant factor that controls the mechanical and hydraulic properties based on the test results. Tests were conducted to determine properties such as compacting factor, compressive strength, splitting tensile strength, abrasion resistance, porosity, and hydraulic conductivity. The study revealed that the degree of compaction was one of the critical factors governing the strength and hydraulic properties of the pervious concrete; the maximum strength and minimum hydraulic conductivity were achieved with a higher degree of compaction. The test results imply that the cement content is the predominant factor determining the fresh and tensile properties of the pervious concrete, rather than the size of the aggregates used. In addition, the results also illustrated that the highly compacted pervious concrete samples made with 4/8 mm aggregates exhibited improved abrasion resistance and strength properties, but slightly reduced hydraulic conductivity, despite the designed porosity.
View
A METAHEURISTIC-BASED ARTIFICIAL NEURAL NETWORK FOR PLASTIC LIMIT ANALYSIS OF FRAMES
Article
Full-text available
  • Dec 2023
Despite the advantages of the plastic limit analysis of structures, this robust method suffers from some drawbacks such as intense computational cost. Through two recent decades, metaheuristic algorithms have improved the performance of plastic limit analysis, especially in structural problems. Additionally, graph theoretical algorithms have decreased the computational time of the process impressively. However, the iterative procedure and its relative computational memory and time have remained a challenge, up to now. In this paper, a metaheuristic-based artificial neural network (ANN), which is categorized as a supervised machine learning technique, has been employed to determine the collapse load factors of two-dimensional frames in an absolutely fast manner. The numerical examples indicate that the proposed method's performance and accuracy are satisfactory.
View
Hydraulic Performance of Pervious Concrete Based on Small Size Aggregates
Article
Full-text available
  • Jun 2022
e paper aims to study the impact of clogging on pervious concrete mixes and explore a simple method to calculate permeability and clogging using the falling head method in a fabricated unit. e materials used are cementitious materials and aggregates, along with superplasticizers. e cementitious materials used are OPC Grade 53 cement and micro Ground Granulated Blast Furnace Slag (μGGBS). Two separate narrow aggregate gradations are used: 2.36-4.75 mm and 4.75-6.30 mm. e water-binder ratio is taken as 0.25, and the aggregate-binder ratio is taken as 3.33. e compressive strength, permeability, and clogging potential of pervious concrete are calculated. e average permeability for 2.36-4.75 mm and 4.75-6.3 mm is 4.78 mm/s and 8.16 m/s, respectively. e clogging materials used are clay and sand with a concentration of 5 g/l. e introduction of clay slurry reduces the permeability by 69.8% and 74.9%, respectively, and with sand, it decreases by 74.7% and 71.7%, respectively, in its rst cycle. e permeability response for such small aggregates is di erent from the standard coarse aggregates. e paper compares the study's compressive strength, porosity, and permeability with the existing literature. It concludes that the maximum clogging occurs when the clogging material is introduced to the specimen for the rst time. e degradation of permeability depends on the clogging particle's particulate size and the concrete matrix's pore size. e smaller aggregates in pervious concrete are not recommended in areas of high siltation.
View
Practical considerations of porosity, strength, and acoustic absorption of structural pervious concrete
Article
Full-text available
  • Dec 2021
This study was conducted to address practical problems in producing structural pervious concrete, such as issues with mix proportioning, paste sagging, porosity, strength, and acoustic absorption. Concrete mixtures with varying aggregate types, target void ratios, and fiber contents were cast in cylindrical and panel-type specimens to determine the compressive strength, porosity, and acoustic absorption coefficient. Porosities of the concrete measured by two methods, i.e., using the volumetric method and computed tomography scanning, were compared to confirm the pore network distribution. A surface impedance method was used to measure the acoustic absorption of the panel samples. Paste with a flow of 170 ± 10 mm containing synthetic fibers prevented sagging successfully even after mechanical vibration. The concrete produced with this paste and having target void ratios of 10%–15% possessed adequate connected open pores for acoustic absorption coefficient over 0.5 and structural strength exceeding 20 MPa.
View
View
View
Role of Different Types of RNA Molecules in the Severity Prediction of SARS-CoV-2 Patients
Article
  • Feb 2023
  • Pathol Res Pract
SARS-CoV-2 pandemic is the current threat of the world with enormous number of deceases. As most of the countries have constraints on resources, particularly for intensive care and oxygen, severity prediction with high accuracy is crucial. This prediction will help the medical society in the selection of patients with the need for these constrained resources. Literature shows that using clinical data in this study is the common trend and molecular data is rarely utilized in this prediction. As molecular data carry more disease related information, in this study, three different types of RNA molecules ( lncRNA, miRNA and mRNA) of SARS-COV-2 patients are used to predict the severity stage and treatment stage of those patients. Using seven different machine learning algorithms along with several feature selection techniques shows that in both phenotypes, feature importance selected features provides the best accuracy along with random forest classifier. Further to this, it shows that in the severity stage prediction miRNA and lncRNA give the best performance, and lncRNA data gives the best in treatment stage prediction. As most of the studies related to molecular data uses mRNA data, this is an interesting finding.
View
Mix design methods for pervious concrete based on the mesostructure: Progress, existing problems and recommendation for future improvement
Article
  • Jun 2022
High porosity endues pervious concrete with outstanding performances as water permeability, pollutant removal, and noise reduction, which make pervious concrete a sustainable material for wide application fields, but a uniform mix design method for pervious concrete is not established yet. This review is conducted to integrate the useful findings in the publication and search the efficient method for the mix design of pervious concrete. The quantitative relationships between the mechanical and hydraulic properties and the mesoscopic parameters of pervious concrete provides the foundation for the property estimation during the mix design of pervious concrete, and the existing model could quantitatively characterize the mesostructure of pervious concrete consisting of cement paste and aggregate. The paste thickness is an important parameter to the final mesostructure of pervious concrete. The film-forming ability test provides an effective approach to select the suitable W/C and dosage of chemical admixture. Based on the previous finding in the publications, the mixture of pervious concrete could be quantitatively controlled from fresh to hardened state, and a systematic mix design method for pervious concrete is suggested. The suggested method would conduce to better control the quality of pervious concrete and greatly reduce the number of trail batching.
View
Comparative study of fly ash and rice husk ash as cement replacement in pervious concrete: mechanical characteristics and sustainability analysis
Article
  • May 2022
Pervious concrete is a special type of concrete consisting of cement, coarse aggregate and water. Cement is a widely used raw material for construction including pervious concrete and had led to the release of huge amounts of CO2 to the environment. Therefore, there is lot of research interest in finding supplementary cementitious materials. The present study examined and compared the feasibility of using industrial waste fly ash (FA) and agricultural waste rice husk ash (RHA) for sustainable pervious concrete production. An experimental program was performed with substitution of FA and RHA contents of 5%, 10%, 15% and 20% as cement replacement and water to binder ratio of 0.3, 0.35, 0.4 and 0.45. The characteristics of pervious concrete and sustainability analysis were compared for both FA and RHA replacement with control concrete mortar. The results showed that both FA and RHA have a negative effect on permeability. The compressive strength was optimum for FA content lies between 10% and 15% replacement level and RHA content of 5% replacement level. Furthermore, the utilisation of FA and RHA decreased production cost, the total embodied energy and carbon emission, resulting in cost-effective and eco-friendly pervious concrete.
View
Influence of aggregate gradation and compaction on compressive strength and porosity characteristics of pervious concrete
Article
  • Mar 2022
This study examined the influence of aggregate gradation on compressive strength and porosity in Pervious Concrete (PC) subjected to various compaction efforts. Two aggregate gradations 12–18 and 18–25 mm were recombined in different proportions in the range of 10–50% to obtain five different gradations. PC specimens were cast with these five aggregate gradations by applying standard Proctor compaction, varying efforts from 0 to 75 blows. Test results indicated that wet density and compressive strength increased with compaction effort at higher rate for specimens casted with Aggregate-to-Cement (A/C) ratio 2.5 than 5.0, but porosity reduced at almost the same rate for both A/C ratios. Compressive strength reduced when aggregate gradation with larger size particles increased, however porosity increased. Altering aggregate gradation or compaction effort yielded no significant change in PC properties for A/C ratio of 5.0 than it did for 2.5. The developed mathematical models predicted compressive strength and porosity of PC mixes in terms of aggregate gradation and compaction effort. The highest mean deviation and relative error of model prediction were 1.377 MPa and 10.4% for compressive strength, and 1.414% and 5.8% for porosity, respectively.
View