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Article

Exploring the Potential of Lateritic Aggregates in Pervious Concrete: A Study on Mechanical Properties and Predictive Techniques

by
Pushparaj A. Naik
1 and
Shriram Marathe
1,2,*
1
Department of Civil Engineering, NMAM Institute of Technology (NMAMIT), Nitte (Deemed to be University), Udupi District, Karkala Taluk 574110, Karnataka, India
2
Department of Materials Engineering and Construction Processes, Faculty of Civil Engineering, Wroclaw University of Science and Technology, Politechnika Wroclawska 27, 50-370 Wroclaw, Poland
*
Author to whom correspondence should be addressed.
CivilEng 2025, 6(2), 30; https://doi.org/10.3390/civileng6020030
Submission received: 3 April 2025 / Revised: 13 May 2025 / Accepted: 5 June 2025 / Published: 10 June 2025
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Abstract

:
This study investigates the mechanical properties of pervious concrete incorporating river lateritic and quarry lateritic aggregates as sustainable alternatives to conventional aggregates. The research aims to evaluate the compressive strength, split tensile strength, and permeability of pervious concrete mixes with varying void ratios (20% and 24%) and aggregate sizes. The results indicate that pervious concrete containing quarry lateritic aggregates exhibits superior permeability due to its inherent porosity, while river lateritic aggregates provide relatively better compressive strength than quarry aggregates. However, both lateritic aggregates show lower mechanical strength than conventional pervious concrete. Additionally, Python-based predictive models employing multi-linear regression were developed to estimate compressive strength based on independent variables such as binder quantity, coarse aggregate content, water-to-cement ratio, and curing duration. The predictive models achieved R2 values of 0.69 for 7-day compressive strength and 0.82 for 28-day compressive strength, indicating strong predictive capabilities. This research highlights the potential of locally sourced materials in enhancing the sustainability of construction practices while offering valuable insights into the mechanical performance of pervious concrete and the utility of computational modeling for predicting concrete properties.

1. Introduction

Urbanization has led to increased impervious surfaces, causing challenges such as stormwater runoff, reduced groundwater recharge, and urban heat islands [1]. Permeable or pervious concrete (PC), characterized by its high porosity, offers a sustainable solution by allowing water infiltration, thereby mitigating these issues [2]. Civil engineers are continually searching for alternative materials in concrete construction that can enhance structural performance and promote sustainable resource management [3]. Traditional PC primarily utilizes natural aggregates; however, the exploration of alternative materials like lateritic aggregates and the integration of machine learning (ML) techniques for property prediction are emerging research areas [4].
The sustainability of PC can be enhanced by incorporating alternative aggregates. A study investigated the use of recycled coarse aggregate from concrete demolition waste in PC mixes, revealing a 36% reduction in compressive strength and a 57% decrease in tensile strength compared to natural aggregates. However, integrating fine aggregates and natural fibers improved these mechanical properties, suggesting the viability of recycled materials in PC applications [5]. Similarly, the feasibility of using ceramsite as an aggregate in PC was examined, focusing on mechanical properties and freeze–thaw durability. The study found that increasing the water–cement ratio generally decreased strength and permeability [6]. Despite these challenges, ceramsite-based PC demonstrated potential for applications requiring lightweight and permeable materials.
Lateritic soils, rich in iron and aluminum, are prevalent in tropical regions [7] and have been studied for their suitability in concrete. Lateritic aggregates, derived from the weathering of rocks in tropical and subtropical regions, are rich in iron and aluminum oxides. Their abundance and local availability make them a sustainable alternative to conventional aggregates in concrete production. Studies have shown that incorporating lateritic materials into concrete can influence its mechanical properties. For instance, research indicates that laterite can effectively replace fine aggregates, achieving compressive strengths above 35 MPa at 28 days even with 100% of the lateritic aggregates [8]. However, the strength of laterite aggregate concrete is generally lower than that of gravel or crushed granite aggregate concrete. Despite this, the use of lateritic aggregates in PC has not been extensively explored, presenting an opportunity for sustainable construction practices in regions where laterite is readily available. Research on laterized concrete, which incorporates lateritic materials, has shown that aggregate size significantly impacts strength. Specifically, smaller aggregate sizes (12 mm) resulted in higher compressive strength compared to larger sizes (20 mm and 40 mm), highlighting the importance of aggregate selection in optimizing concrete performance [9]. On the other hand, the application of ML techniques in predicting PC properties has gained attention [10]. A study employed Artificial Neural Networks (ANNs) to predict the compressive strength and porosity of PC, achieving high accuracy with R2 values exceeding 0.97. This demonstrates the potential of ML models in accurately forecasting PC properties based on mix design parameters [11]. Another investigation developed a Convolutional Neural Network (CNN) model to predict the 28-day compressive strength of PC, incorporating variables such as aggregate content, water content, and admixtures. The model achieved a mean absolute percentage error of 9.13%, indicating its robustness and applicability across various PC formulations [12].
Despite advancements in utilizing alternative aggregates and ML in PC composite research, specific gaps remain persistent. While lateritic materials have been studied in conventional concrete, their application in PC is underexplored. Understanding the mechanical and permeability properties of PC with lateritic aggregates is essential for broader adoption. From the Literature, ML models have clearly shown promise in predicting PC properties; however, their application to PC incorporating lateritic aggregates has not been extensively studied. Developing predictive models for such mixes could enhance mix design efficiency and performance optimization. Accordingly, this research was planned to fill the gaps in the Literature. Hence, this study aims to address the identified research gaps by the following means:
  • Investigating the compressive and tensile strengths of PC mixes incorporating river and quarry lateritic aggregates, compared to traditional coarse aggregates, at varying void ratios (20% and 24%);
  • Measuring the permeability to determine the impact of lateritic aggregates on water infiltration capabilities of these PC mixes;
  • Utilizing simple Python-based ML techniques to predict the compressive strength of PC mixes with lateritic aggregates, facilitating efficient mix design and performance forecasting.
The void ratio targets of 20% and 24% were selected in alignment with IRC:44-2017 provisions and previous studies to represent typical ranges that balance mechanical integrity with permeability in PC applications. Overall, this study seeks to expand the knowledge of PC by incorporating lateritic aggregates and leveraging ML for property (i.e., compressive strength) prediction. The outcomes are expected to contribute to sustainable construction practices by utilizing locally available materials and advanced computational techniques, thereby addressing environmental concerns and enhancing infrastructure resilience.

2. Experimental Methodology and Materials

This section briefly delineates the comprehensive methodology, experimental procedures, and the vital properties of materials employed in the investigation of PC incorporating lateritic aggregates. The study mainly aims to evaluate the mechanical and permeability properties of PC mixes with varying aggregate types and proportions, designed to achieve specific void ratios. The research methodology, as illustrated in Figure 1, encompasses a literature review, material selection and testing, mix-design formulation, specimen preparation, mechanical and permeability testing, development of ML models, and subsequent data analysis followed by the conclusions and recommendations for future research directions within this realm.

2.1. Materials and Mix Design

Portland Pozzolana Cement (PPC) was utilized as the binding material in all PC mixes. The fundamental properties of the cement are presented in Table 1. Three types of coarse aggregates were incorporated: normal aggregates, quarry lateritic aggregates, and river lateritic aggregates. The two types of lateritic aggregates used in the investigation are shown in Figure 2. The selected physical properties of these aggregates are summarized in Table 2. Potable water, conforming to IS 456:2000 standards [13], was used for mixing and curing all PC specimens.
A total of 9 different PC mixes were designed, targeting void ratios of 24% and 20%, respectively, as per IRC 44 guidelines [14] with water-to-cement ratio. Each void ratio category comprised nine distinct mix designations, varying in aggregate type and gradation. These mix designs were formulated to assess the impact of aggregate type and size distribution on the mechanical and hydrological properties of PC, aiming to identify optimal compositions for structural and permeability performance. The water-to-cement (w/c) ratio was maintained at 0.35 across all mixes, aligning with standard practices for PC, where w/c ratios typically range from 0.28 to 0.40. The binder content (PPC) was set at 270 kg/m3 for the 24% void ratio mixes and 330 kg/m3 for the 20% void ratio mixes, within the typical range of 270 to 420 kg/m3 recommended for such mixes.
Table 1. Basic Properties of Cement (PPC).
Table 1. Basic Properties of Cement (PPC).
Property of PPCResultTest MethodReference
Specific Gravity3.15IS 4031 (Part 11)-1988[15]
Fineness<1%IS 4031 (Part 1)-1996[16]
Standard Consistency32%IS 4031 (Part 4)-1988[17]
Initial Setting Time95 minIS 4031 (Part 5)-1988[18]
Table 2. Selected Properties of Aggregates.
Table 2. Selected Properties of Aggregates.
PropertyNormal (Granite) AggregateRiver Lateritic AggregateQuarry Lateritic AggregateTest Method and Reference
Specific Gravity2.692.572.309IS 2386 (Part 3)-1963 [19]
Water Absorption0.50%5.82%6.42%IS 2386 (Part 3)-1963 [19]
Aggregate Impact Value16.80%56.70%62.40%IS 2386 (Part 4)-1963 [20]
Los Angeles Abrasion Value30%52.60%57.20%IS 2386 (Part 4)-1963 [20]
The mix designations and descriptions are as follows: PCN 1: PC with 100% normal aggregate passing 20 to 16 mm; PCN 2: PC with 75% normal aggregate passing 20 to 16 mm and 25% aggregate passing 10 to 8 mm; PCN 3: with 50% normal (granite) aggregate passing 20 to 16 mm and 50% aggregate passing 10 to 8 mm; PCRL 1: with 100% river lateritic aggregate passing 20 to 16 mm; PCRL 2: with 75% river lateritic aggregate passing 20 to 16 mm and 25% aggregate passing 10 to 8 mm; PCRL 3: with 50% river lateritic aggregate passing 20 to 16 mm and 50% aggregate passing 10 to 8 mm; PCQL 1: with 100% quarry lateritic aggregate passing 20 to 16 mm; PCQL 2: with 75% quarry lateritic aggregate passing 20 to 16 mm and 25% aggregate passing 10 to 8 mm; PCQL 3: with 50% quarry lateritic aggregate passing 20 to 16 mm and 50% aggregate passing 10 to 8 mm. The final mix proportions for both void ratio designs are detailed in Table 3. Concrete mixes were prepared using a pan mixer to ensure uniformity. The mixing sequence involved dry mixing of aggregates and PPC, followed by the gradual addition of water to achieve the desired consistency. Specimens were cast in standard molds (cubes of side 150 mm, cylindrical specimen of diameter 150 mm for compressive, splitting tensile strength, and permeability tests) and compacted using a table vibrator to eliminate air voids. Post-casting, specimens were covered with plastic sheets for 24 h to prevent moisture loss and subsequently demolded and cured in water-task at 27 ± 2 °C until testing [21,22]. The compressive strength was determined at 7, 14, and 28 days of curing, where the other mechanical properties (splitting strength and permeability) were reported after 28 days of curing. For each mix designation and test type, a minimum of 3 specimens were cast and tested, and the results reported in this study represent the statistical average of these replicates. To ensure consistency in sample preparation, the dry mixing of aggregates and cement was performed for approximately 2 min, followed by the gradual addition of water. After water incorporation, wet mixing continued for an additional 2 to 3 min until a homogenous blend was achieved across all mixes.

2.2. Machine Learning-Based Compressive Strength Prediction

In addition to the experimental analysis, this study integrates a Python (version 3.8) - based ML approach to predict the compressive strength of PC. The ML techniques in civil engineering offer a data-driven alternative to conventional trial-and-error mix designs, reducing material wastage and laboratory efforts while improving predictive accuracy [10,23]. In total, 108 data points were generated from compressive strength measurements corresponding to 9 different mix types, two void ratios (20% and 24%), and two curing ages (7 and 28 days). In the present investigation, a multi-linear regression (MLR) model was developed using Python (Spyder IDE, version 4.x) to estimate the strength of PC based on mix design parameters. The input variables for the model included: binder content (kg/m3); coarse aggregate content (kg/m3); water content (kg/m3); water-to-cement (w/c) ratio; curing period. The dependent variable was the compressive strength (MPa) at different curing ages. It is important to note that the current modeling effort was exploratory in nature and aimed at providing a preliminary understanding of how basic mix parameters relate to the compressive strength of PC containing lateritic aggregates. Accordingly, MLR was selected as a first-step statistical modeling tool due to its transparency, simplicity, and interpretability. While more sophisticated ML techniques such as Artificial Neural Networks (ANN) or ensemble-based models can offer higher predictive performance, their complexity and need for larger, more diverse datasets made them beyond the intended scope of the present study. However, the current dataset and findings lay a strong foundation for future dedicated studies that can explore and validate more advanced modeling frameworks [24].
The dataset was constructed from experimental results, consisting of measured compressive strength values mapped against corresponding mix parameters. Data pre-processing involved: normalization of input values to ensure uniformity; splitting the dataset into training (80%) and testing (20%) sets for model validation; and handling missing or inconsistent data through data cleaning techniques. Further, the ML model was implemented using NumPy, Pandas, and Scikit-learn libraries of python. MLR was chosen due to its capability to model relationships between multiple independent variables and the target output. The training phase involved: (i) fitting the dataset to train the regression model; (ii) tuning model parameters to enhance accuracy; and (iii) cross-validation to ensure generalizability across different mixes. After training, the model was evaluated simply based on 2 parameters: (i) R2 score, a measure of how well the input variables explain variations in strength; and (ii) Mean Squared Error (MSE), to quantify prediction accuracy. To visually access the model performance, a scatter plot comparing measured vs. predicted strength values was generated (See the details at the Section 3). This developed ML model is expected to serve as a supplementary approach to traditional experimental procedures, helping engineers develop efficient and sustainable pervious concrete mixes with enhanced predictability [10,25].

3. Test Results and Discussion

3.1. Workability, Density, and Saturated Water Absorption (SWA)

The PC is generally designed to have minimal slump, reflecting its low workability, which is essential for maintaining its porous structure [26]. In this study, the Compaction Factor Value (CFV) ranged from 0.68 to 0.73 across different mixes, indicating consistent low workability suitable for pervious composite applications. Notably, the PC mixes with a 20% void ratio exhibited slightly better workability than those with a 24% void ratio, likely due to the higher binder content facilitating better cohesion among aggregates [27]. The unit weight of the PC mixes, measured at 28 days, varied between 1329 kg/m3 and 1541 kg/m3. These values align with typical PC densities, which are generally lower than conventional concrete due to the intentional inclusion of voids [2]. The lower unit weights observed in mixes with higher void ratios can be attributed to increased porosity, resulting in reduced mass per unit volume. The SWA tests revealed values ranging from 7.98% to 11.1%. Higher void content in the mixes corresponded to increased water absorption, as the interconnected pore structure allows for greater water ingress. This characteristic is beneficial for stormwater management applications, as it enhances the material’s ability to facilitate groundwater recharge [28].

3.2. Compressive Strength (CS)

The CS of the trial-PC mixes was evaluated at 7, 14, and 28 days of curing for both 24% (Figure 3) and 20% (Figure 4) void ratios. As expected, CS increased with curing time across all mixes. For the 24% void ratio mixes, 28-day compressive strengths ranged from 4.0 MPa to 9.80 MPa, while the 20% void ratio mixes exhibited strengths between 4.30 MPa and 11.50 MPa.
The higher binder content in the 20% void ratio mixes likely contributed to the increased strength observed. Among the different aggregate types, mixes with normal aggregates (i.e., PCN series) consistently showed higher CSs compared to those with river lateritic (i.e., PCRL series) and quarry lateritic aggregates (i.e., PCQL series). This trend can be attributed to the superior mechanical properties of conventional aggregates, which enhance the load-bearing capacity of the concrete [29]. A study by Ukpata et al. (2024) revealed that there was a reduction in strength with the incorporation of lateritic aggregates due to their lower density and pozzolanic activity. Specifically, compressive strengths decreased with 10% and 25% laterite replacements, showing values of 30.1 N/mm2 and 23.1 N/mm2, respectively, compared to higher strengths without laterite [9].

3.3. Split Tensile Strength (STS)

The STS tests conducted at 28 days revealed, as indicated by Figure 5, that mixes with normal-granite aggregates achieved the highest tensile strengths, with the PCN 3 mix (20% void ratio) reaching 1.40 MPa. In contrast, mixes with river lateritic and quarry lateritic aggregates exhibited lower tensile strengths, with the PCRL 3 and PCQL 3 mixes (20% void ratio) achieving 1.082 MPa and 0.7 MPa, respectively. The reduced tensile strength in lateritic aggregate mixes may be due to the inherent material properties of lateritic aggregates, which often have higher porosity and lower strength compared to conventional aggregates [9]. The variations observed in both compressive and split tensile strength test results across the three replicates for each mix were within the permissible limit of ±15%, as prescribed in Clause 15.4 of IS 456:2000 for concrete strength tests [13].

3.4. Permeability

Permeability tests at 28 days demonstrated, as indicated by the Figure 6 that all PC mixes possessed significant permeability, a key characteristic for effective storm-water management [30]. Mixes with quarry lateritic aggregates (PCQL series) exhibited the highest permeability values, with the PCQL 1 mix (24% void ratio) achieving 0.6696 cm/s. This enhanced permeability is likely due to the higher porosity of lateritic aggregates, which contributes to a more interconnected pore structure within the concrete matrix.
Comparatively, mixes with normal aggregates (PCN series) showed slightly lower permeability values, which can be attributed to the denser aggregate structure reducing the overall porosity. The increased permeability with laterites is due to the connectivity of small pores and the finer materials that retain more water, reducing hydration availability, which alters the pore structure, increasing the number of pores and enhancing permeability in such mixes [31]. Numerous studies have revealed that the PC composites with diminished permeability consequences in enhanced strength [32,33]. However, a balance between strength and permeability is crucial in PC applications, and the choice of aggregate plays a significant role in achieving the desired performance characteristics [34].

3.5. Python-Based Compressive Strength Prediction Models

In an effort to predict the CS of PC mixes, Python-based MLR models were developed using input variables such as cement content, coarse aggregate type and size, water content, w/c ratio, and curing duration. The models demonstrated a strong correlation between predicted and actual CSs, with R2 values of 0.69 (with RMSE = 1.9011) for 7-day strength predictions and 0.82 (with RMSE = 2.2617) for 28-day predictions, as depicted in Figure 7. These results suggest that ML techniques can effectively model the complex relationships between mix design parameters and PC strength, offering a valuable tool for optimizing permeable composite formulations.
In a nutshell, this research highlights the critical influence of aggregate type (natural and lateritic) and void ratio on the mechanical and hydraulic properties of PC. Mixes with normal aggregates consistently outperformed those with lateritic aggregates in terms of compressive and tensile strengths [9]. However, lateritic aggregate mixes, particularly those with quarry lateritic aggregates, exhibited superior permeability, making them suitable for applications where drainage is a priority over structural load-bearing (strength) capacity [31]. The successful application of Python-based regression models to predict CS underscores the potential of integrating ML techniques in PC mix design optimization. By accurately forecasting the outcomes of various mix configurations, these models can reduce the reliance on extensive empirical testing, thereby streamlining the development process for PC with tailored properties [10]. Overall, this study contributes to the body of knowledge on PCs by elucidating the trade-offs between strength and permeability associated with different (non-conventional) aggregate types and mix designs. The findings provide practical insights for engineers and practitioners aiming to design PC mixes that meet specific performance criteria for sustainable construction applications.

4. Conclusions and Future Research Scopes

This study investigated the mechanical and hydraulic properties of pervious concrete (PC) incorporating different aggregate types normal (granite), river lateritic, and quarry lateritic across mixes designed for 20% and 24% void ratios. Given their lower mechanical strength, lateritic aggregate PC mixes are best suited for low-traffic applications such as footpaths, walkways, and parking areas, rather than structural or heavy-load pavements. The key findings are as follows:
  • The PC mixes with a 20% void ratio exhibited slightly better workability than those with a 24% void ratio, likely due to higher binder content enhancing cohesion among aggregates. The unit density varied between 1329 kg/m3 and 1541 kg/m3, and the SWA ranged from 7.98% to 11.1%, with higher void content corresponding to increased water absorption, beneficial for storm-water management needs;
  • The PC mixes with normal aggregates (PCN series) consistently showed higher CSs compared to those with river lateritic (PCRL series) and quarry lateritic aggregates (PCQL series). This trend can be attributed to the superior mechanical properties of conventional aggregates, which enhance the load-bearing capacity of the PC composites;
  • Similar trends were reported under STS results; the reduced tensile strength in lateritic aggregate mixes may be due to the inherent material properties of lateritic aggregates, which often have higher porosity and lower strength compared to conventional aggregates;
  • Mixes with quarry lateritic aggregates (PCQL series) exhibited the highest permeability values, with the PCQL 1 mix (24% void ratio) achieving 0.6696 cm/s. This enhanced permeability is likely due to the higher porosity of lateritic aggregates, which contributes to a more interconnected pore structure within the concrete matrix;
  • The developed Python-based MLR models effectively predicted the CS of PC mixes, with R2 values of 0.69 for 7-day strength predictions and 0.82 for 28-day predictions. This demonstrates the potential of integrating ML techniques in PC mix design optimization, offering a valuable tool for predicting the outcomes of various mix configurations and reducing reliance on extensive pragmatic testing.
Building upon the findings of this study, future research can explore the following areas:
  • Investigate the long-term performance of PC mixes incorporating lateritic aggregates under various environmental conditions, to assess their suitability for different ambiance;
  • Explore the use of other sustainable or recycled aggregates in PC to enhance mechanical properties while maintaining or improving permeability;
  • Employ advanced ML algorithms, such as artificial neural networks or ensemble methods, to improve the accuracy of CS predictions and optimize mix designs;
  • Conduct field studies to evaluate the real-world performance of PC pavements with varying aggregate types and void ratios, focusing on aspects like load-bearing capacity, permeability retention, and maintenance requirements;
  • Perform comprehensive life cycle cost assessments to evaluate the environmental and economic impacts of using different aggregate types in PC, aiding in the development of more sustainable construction practices;
  • Future work could also include life cycle-based sustainability comparisons of lateritic aggregate PC mixes, as inspired by recent studies on environmentally optimized retrofitting solutions [35].
By addressing these areas, future research can contribute to the development of more durable, sustainable, and high-performance permeable concrete solutions, thereby enhancing their applicability in various pavement construction scenarios.

Author Contributions

Conceptualization, software, investigation, writing; original draft preparation, writing; review and editing: S.M.; investigation, resources, formal analysis, validation, data curation, methodology, supervision, visualization, project administration: P.A.N. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The authors confirm that the data supporting the findings of this study are available within the article.

Acknowledgments

All significant testing was conducted at the research lab of the Civil Engineering Department at NMAM Institute of Technology in Nitte, Karnataka, India. Special appreciation goes to the following undergraduate students from the B.E. (Civil) program: Shreyas Mayya D.(4NM20CV441)—team leader, Vignesh H. (4NM20CV454), Yuvaraj S. Salian (4NM20CV459), Prashanth K. Naik (4NM19CV038), and Vibha V. Prabhu (4NM20CV452), for their assistance in conducting few of the preliminary laboratory experimentations.

Conflicts of Interest

All the authors confirm no known financial interests or personal relationships that might be perceived as influencing the research presented in this paper.

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Figure 1. Flow-chart Showing the Experimental Methodology.
Figure 1. Flow-chart Showing the Experimental Methodology.
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Figure 2. (a) River lateritic aggregate; (b) quarry lateritic aggregate.
Figure 2. (a) River lateritic aggregate; (b) quarry lateritic aggregate.
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Figure 3. Compressive Strength—24% Voids Design.
Figure 3. Compressive Strength—24% Voids Design.
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Figure 4. Compressive Strength—20% Voids Design.
Figure 4. Compressive Strength—20% Voids Design.
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Figure 5. Splitting Tensile Strength Results of PC mixes at 28 Days of Curing.
Figure 5. Splitting Tensile Strength Results of PC mixes at 28 Days of Curing.
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Figure 6. Co-efficient of Permeability of PC mixes at 28 Days of Curing.
Figure 6. Co-efficient of Permeability of PC mixes at 28 Days of Curing.
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Figure 7. Experimental and Predicted CS of PC Mixes from ML-Modeling. (i) Seven-day compressive strength; (ii) twenty-eight-day compressive strength.
Figure 7. Experimental and Predicted CS of PC Mixes from ML-Modeling. (i) Seven-day compressive strength; (ii) twenty-eight-day compressive strength.
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Table 3. Mix Proportion Design Details (in kg/m3).
Table 3. Mix Proportion Design Details (in kg/m3).
Mix ID24% Void Ratio20% Void Ratio
PPCWaterCA1CA2PPCWaterCA1CA2
PCN 1270.6894.7415660330.83115.7915660
PCN 2270.6894.741174.5391.5330.83115.791174.5391.5
PCN 3270.6894.74783783330.83115.79783783
PCRL 1270.6894.741490.60330.83115.791490.60
PCRL 2270.6894.741117.95372.65330.83115.791117.95372.65
PCRL 3270.6894.74745.3745.3330.83115.79745.3745.3
PCQL 1270.6894.741339.160330.83115.791339.160
PCQL 2270.6894.741004.37334.79330.83115.791004.37334.79
PCQL 3270.6894.74669.58669.58330.83115.79669.58669.58
Note: CA1→ Coarse aggregates 20–16 mm; CA2 → Coarse aggregates 10–8 mm.
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Naik, P.A.; Marathe, S. Exploring the Potential of Lateritic Aggregates in Pervious Concrete: A Study on Mechanical Properties and Predictive Techniques. CivilEng 2025, 6, 30. https://doi.org/10.3390/civileng6020030

AMA Style

Naik PA, Marathe S. Exploring the Potential of Lateritic Aggregates in Pervious Concrete: A Study on Mechanical Properties and Predictive Techniques. CivilEng. 2025; 6(2):30. https://doi.org/10.3390/civileng6020030

Chicago/Turabian Style

Naik, Pushparaj A., and Shriram Marathe. 2025. "Exploring the Potential of Lateritic Aggregates in Pervious Concrete: A Study on Mechanical Properties and Predictive Techniques" CivilEng 6, no. 2: 30. https://doi.org/10.3390/civileng6020030

APA Style

Naik, P. A., & Marathe, S. (2025). Exploring the Potential of Lateritic Aggregates in Pervious Concrete: A Study on Mechanical Properties and Predictive Techniques. CivilEng, 6(2), 30. https://doi.org/10.3390/civileng6020030

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