
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

72789
10.1038/s41598-024-72789-y
Article
Investigating the potential of waste glass in paver block production using RSM
Naik Bhukya Govardhan 1
Nakkeeran G. drnakkeerang@mits.ac.in

1
Roy Dipankar 1
https://orcid.org/0000-0003-4863-7628
Alaneme George Uwadiegwu alanemeg@kiu.ac.ug
tinz2020@gmail.com

2
1 grid.459547.e Department of Civil Engineering, Madanapalle Institute of Technology & Science, Madanapalle, 517325 Andhra Pradesh India
2 https://ror.org/017g82c94 grid.440478.b 0000 0004 0648 1247 Department of Civil Engineering, School of Engineering and Applied Sciences, Kampala International University, Kampala, Uganda
14 9 2024
14 9 2024
2024
14 2150810 8 2024
10 9 2024
© The Author(s) 2024
2024
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The global surge in glass waste generation, exceeding 130 million tons annually, presents a pressing environmental issue, compounded by inadequate recycling practices, it is concerning that the global recycling rate for glass waste is below 50%. This research investigates the utilization of WG as a FA substitute in paver block to mitigate the ecological footprint of conventional paver block while enhancing its mechanical properties. WG’s unique characteristics, such as high silica content and impermeability, make it a promising alternative. A comprehensive experimental approach, including tests like water absorption, dry density, workability, compressive strength, ultrasonic pulse velocity, and rebound hammer, demonstrated WG’s potential to improve concrete’s durability and performance. For instance, a 40% WGA replacement reduced the absorption rate 12%, while 20% WGA incorporation-maintained strength properties close to the control mix, with compressive strengths up to 30.80 MPa at 28 days. Employing RSM as predictive models, the study showed R2 values of 0.9513, 0.9983, 0.9156, 0.9925, and 0.9895 for water absorption, dry density, compressive strength, ultrasonic pulse velocity, and rebound hammer, respectively. This study offers supporting global research efforts to advance sustainable and affordable construction materials, leading to a significant reduction in landfill waste and the conservation of precious natural resources worldwide.

Keywords

Waste glass
Fine aggregate
Alternative new building materials
Hardened properties
Sustainability
RSM
Prediction
Machine learning
Subject terms

Engineering
Materials science
issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

The annual volume of WG created globally has increased dramatically in the last few decades. There were over 130 million tons of WG manufactured in 2018 alone1,2. The United States is a leading contributor to glass waste, with substantial quantities being disposed of in landfills due to inadequate recycling facilities. Nevertheless, a sizable portion went untreated because only 21% of this garbage was recycled3,4. An increasingly serious environmental problem is the improper handling of non-recycled glass waste. There is a pressing need for more sustainable waste management procedures because most of this material is currently disposed of in landfills, which exacerbates environmental concerns5.

It has been reported that WG can serve as an alternative to glass waste in concrete, acting as a supplementary replacement of FA material. Several factors influence the effectiveness of using glass as a replacement for FA6–9. These factors pertain to the physical and chemical characteristics of the glass, such as its factors to consider include the size, gradation, shape, specific gravity, and resistance to alkali-aggregate reactions. When glass FA was used as a replacement for a certain percentage of the concrete, it showed promising results. The slump value and compaction factor were both positive, indicating good workability10–12. Additionally, the concrete density improved significantly micro-filling effect is responsible for this phenomenon. Nevertheless, if the replacement levels go beyond 20%, there is a clear decrease in workability11,13. Because due to the angular and irregular shape of the glass particles, which leads to increased internal friction and reduced flowability of the concrete mix. WG have high silica concentration and advantageous properties (such as impermeability, less water absorption, and high hardness), FA may not be as effective or environmentally harmful as WG in concrete manufacturing. As the percentage of FA glass replacement content increases, the slump of concrete increase14,15.

One interesting way to improve sustainability in the building sector is to replace FA in concrete with glass trash. Due to the pozzolanic activity of the silica component in the glass, waste glass aggregates (WGA) can be used in place of typical FA in concrete to increase mechanical qualities like CS12,16–18. The overall strength and durability of the concrete are improved because of this reaction, which also helps to generate more calcium silicate hydrate (C-S-H). Furthermore, utilizing WGA is in line with the circular economy’s tenets by reducing the environmental impact of disposing of glass waste and obtaining natural aggregates. To avoid negative consequences like the ASR, it is imperative to carefully evaluate the particle size distribution and the ideal replacement levels. This creative method helps to create high-performing, environmentally friendly concrete composites while also addressing waste management issues16,17.

According to the research, utilizing WG as FA in paver blocks provides a number of suggestions. Firstly, the addition of WG can significantly improve the CS and durability of the material, making it highly suitable for applications that require heavy loads19–21. Nevertheless, increased replacement levels might necessitate the use of extra admixtures to address the possible alkali-silica reactivity (ASR). The long-term usage of WG in concrete raises concerns regarding ASR, which can weaken concrete buildings. When WG’s amorphous silica combines with cement paste alkalis, an expansive gel forms that swells when moisture is absorbed. In time, this swelling causes internal tensile strains, microcracking and spalling. Alkali content, glass particle size, moisture availability, and cement type affect WG concrete ASR severity. Fine glass particles are more reactive and prone to ASR, especially in damp settings. Low-alkali cements or supplemental cementitious materials (SCMs) can reduce hazards, but proper WG content and particle size optimization must balance mechanical benefits with long-term durability22–25.

Furthermore, ensuring an optimal particle size distribution is essential for preserving workability and reducing porosity. Furthermore, incorporating WG into paver blocks contributes to sustainability efforts by decreasing the need for natural aggregates and redirecting waste away from landfills. Lastly, it is highly recommended to conduct additional research on the long-term performance of this innovative material in different environmental conditions. This will help ensure its viability and safety in a wide range of construction settings.

As a result, this study seeks to assess the practicality of using WG as a substitute for FA in affordable, durable concrete mixtures that are environmentally friendly. The evaluation will be conducted under typical curing conditions. This study thoroughly evaluates the feasibility and mechanical characteristics of the concrete by conducting a range of tests. These tests encompass the water absorption, dry density, workability, CS, UPV, and rebound hammer. Further investigations could involve conducting additional tests to assess the long-term durability of mortar containing waste glass. These tests may include accelerated ageing, freeze-thaw cycles, and evaluating its resistance to sulphate attack. By conducting these analyses, we can gain a more comprehensive understanding of how the material holds up in different environmental conditions. This will help us determine if it is suitable for long-term use in construction projects.

RSM is a statistical and mathematical technique that is widely used in the optimization of various processes, including the development of construction materials. The technique involves the systematic variation of input parameters, such as the percentage of glass waste, curing conditions, and binder content, to determine their influence on the desired output variables, such as water absorption, dry density, workability, CS, UPV, and rebound hammer.

The benefits of using RSM in the development of paver blocks with glass waste include the ability to identify the optimal mix design, reducing the number of experiments required26,27, and providing a deeper understanding of the relationships between the input and output variables28. By employing RSM, researchers can explore the complex interactions between various factors and their impact on the performance of paver blocks, ultimately leading to the development of more sustainable and cost-effective construction materials29–32.

The integration of WG into concrete is promoted by this study, there is a significant lack of research in fully understanding how paver block with glass waste as FA performs over the long term in terms of durability and mechanical strength. There have been few studies conducted on the impact of microstructural integrity, alkali-silica reactivity, and particle size distribution on the workability and strength properties of these mortars. Additional research is necessary to enhance the mix design and guarantee its long-term viability in construction projects.

Research materials and methodology

Materials

Cement

For all the mixtures in this study, OPC) (ASTM C 150 (2007) Type I) was utilized. Here are the physical and chemical compositions of the cement shown in Table 1 and Physical compositions of the cement shown in Table 2.

Table 1 Chemical compositions of cement.

Chemical properties (%)	
SiO2	Al2O3	Fe2O3	CaO	MgO	SO3	Lol	
21.36	5.03	3.31	63.18	2.89	2.30	1.40	

Table 2 Physical composition of cement.

Specific gravity	Baline (cm2/g)	Initial setting
Time (min)	Final setting
Time (h)	Compressive (MPa)
7 Days	
3.15	3750	3.20	4.15	30.80	

FA

A FA with a maximum size of 4.75 mm was utilized, specifically river sand. The FA had a density of 2.60 g/cm3 and an absorption ratio of 1.01%. Table 3 provides an overview of the aggregate’s material properties.

Table 3 Material properties and limits of FA.

Properties	FA	WG	
Finesse modulus	2.37	2.31	
Absorption (%)	2.71	0.41%	
Density (kg/m3)	1688	1668	
Specific gravity	2.57	2.17	

Recycled WG

The FA used in this investigation, which was created from crushed WG, is shown in Fig. 1. Waste glass as aggregate can vary widely depending on its source, such as glass containers. The WG is crushed and then passed through sieves with openings of 4.75 mm, 2.36 mm, and 150 microns. In this study, the FA was sourced from a retaining portion that passed through a 4.75 mm sieve and was retained on a 2.36 mm sieve. replacing 10%, 20%, 30%, 40%, 50% and 100% of WG in paver block. Conducting a cost-benefit analysis of using waste glass (WG) as a fine aggregate replacement in paver block production compared to conventional methods would be highly beneficial. This analysis would provide industry stakeholders with crucial insights into the economic viability of incorporating WG. It would assess potential cost savings from reduced raw material expenses and waste disposal, balanced against any additional processing costs. Such an evaluation could demonstrate the financial advantages and long-term sustainability of adopting WG technology, encouraging its broader implementation in the construction industry. The use of WG as a substitute for FA in concrete represents a breakthrough in sustainable construction methods. This method not only helps alleviate the impact on the environment caused by disposing of glass waste, but also contributes to the preservation of natural sand resources, thereby reducing the ecological impact of concrete production11,33,34. By utilizing recycled glass, the building industry might potentially drastically reduce the release of greenhouse gases and energy consumption, thereby improving its overall sustainability performance. In addition, the possibility of enhancing material properties, like durability and permeability reduction, highlights the addition of used glass to concrete mixtures as a high-performing, ecological alternative35,36. Tables 4 and 5 provide an overview of the physical and chemical properties of the WG.

Fig. 1 WG into paver block.

Table 4 Physical properties of WG.

Test	Specific gravity	Density (kg/m3)	Absorption (%)	Finesse modulus	Colour	
%	2.475	1300	1.01	2.31	Light Gray and Green	

Table 5 Chemical composition of WG.

Chemical composition	Fe2O3	CaO	Al2O3	SiO2	SO3	MgO	Na2+K2O	
%	1.24	7.67	1.95	69.43	0.21	6.85	8.96	

Methodology

Using a thorough experimental methodology (Fig. 2), this study explores the use of WG in substitution of 10%, 20%, 30%, 40%, 50% and 100% fine particles in paver block. To guarantee equal dispersion in the mortar matrix, WG was integrated utilising a two-stage mixing procedure after being treated to meet precise granulometric criteria. Water absorption, dry density, workability, compressive strength, ultrasonic pulse velocity, and rebound hammer are just a few of the many testing that are included in this study. Although these give a decent summary of the material’s characteristics, tests for endurance in different environmental settings, including freeze-thaw cycles or exposure to harsh chemicals, could improve the study. To ascertain the concrete’s relative density, specific gravity was also tested. To build predictive models and improve the mix design, machine learning techniques including RSM were applied. These methods offered insightful information about the intricate relationships between the tangible elements. Repurposing waste materials, GW’s potential as a sustainable alternative in construction aligns with the concepts of the circular economy, as shown by the research.

Fig. 2 Methodology.

RSM modelling

RSM was selected for its effectiveness in examining the connections between various factors and responses, enabling accurate optimizing with lesser experimental trials. RSM offers a reliable statistical framework for modelling and predicting outcomes, making it well-suited for determining the best mix proportions in intricate materials such as mortar with WG37.

In the realm of mathematical and statistical tools for optimization, RSM is a commonly employed technique. To determine the functional interaction between independent variables and the response, RSM utilises a fractional factorial design strategy. The significance of the obtained model is evaluated based on the R2 values, while the impact of individual factors is determined by calculating the F-value. In cases where the F-value is high, the corresponding parameter exerts a greater influence on the experiment38. Equation 1 represent RSM general analysis.1 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:Y={\beta\:}_{0\:}+{\beta\:}_{1}A+{\beta\:}_{2}{B+\beta\:}_{12}{AB+\beta\:}_{11}{{A}^{2}\:\beta\:}_{22}{B}^{2}$$\end{document}

The research team established mathematical models through multivariate regression analysis after conducting experimental investigations on concrete properties. The polynomial model based on RSM, Eq. 1, The predicted response function in the developed model is denoted by Y, with β0 as the intercept and β1 and β2 as coefficients of linear effect. Additionally, β11 and β22 represent quadratic effect coefficients, while the interaction effect coefficient is denoted by β1239.

RSM models’ input parameters and ranges were selected systematically, focusing on glass waste replacement, curing conditions, and binder content. These factors were selected because they significantly affected water absorption, dry density, compressive strength, ultrasonic pulse velocity, and rebound hammer test results. This technique improved model accuracy and optimized mix design with fewer experiments. It includes designing the mix, evaluating its mechanical and hardened qualities experimentally, and optimising the mix for improved performance using machine learning and RSM. The Fig. 3 presents a research methodology for maximising the performance of concrete that uses leftover glass as an aggregate.

Fig. 3 RSM methodology.

Methods

Mixing methods

A 1:2 mix ratio, and a 0.45 water/cement ratio were used in this investigation. The two-stage mixing method (TSM) is utilized to enhance the dispersion and integration of WGA within the concrete matrix, thereby optimizing its mechanical performance. In the initial stage, cement and WGA are thoroughly mixed for 180 s. Subsequently, 50% of the total water is introduced into the mixer, and the mixture is blended for an additional 180 s to ensure uniform hydration and coating of the glass particles. The second stage involves the addition of natural aggregates, which are then mixed thoroughly for 180 s. Following this, the remaining 50% of the water is added, and the mixture is further blended for 180 s. This sequential addition and staged mixing process significantly improve the homogeneity of the concrete mix, mitigating potential adverse effects of WGA on the properties of the concrete and enhancing the overall performance of the final product. The paver blocks were allowed to cure by Gunny bag for seven and twenty-eight days, during which time they were covered with gunny bags Fig. 4.

Fig. 4 Casting Paver block by 2 stage method.

Testing methods

Specific gravity

When assessing the viability of WG as a potential building material, it is important to consider its specific gravity. Through our experimentation, we discovered that the specific gravity of WG is 2.475, which falls within the range typically seen in natural aggregates, usually between 2.4 and 2.8. The similarity between glass waste and natural aggregates makes glass waste a viable option for use in concrete compositions. Similar specific gravity values, the concrete maintains its desired characteristics, including strength and workability40,41. In addition, the utilization of WG in concrete contributes to sustainability efforts by minimizing the quantity of waste disposed in landfills while protecting priceless natural resources. This, in turn, promotes the adoption of eco-friendly construction methods. Equation 2 represent calculate of specific gravity.2 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:\text{Specific gravity} =\:\frac{\text{D}}{\text{C}-(\text{A}-\text{B})}.$$\end{document}

Water absorption

Water absorption in concrete is a critical parameter that indicates the material’s porosity and, consequently, its durability. Lower water absorption values reflect a denser and more durable concrete, essential for long-term performance. Conversely, higher water absorption can compromise concrete strength and increase susceptibility to freeze-thaw damage. Equation 3 represent of water absorption.3 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:\text{W}\text{a}\text{t}\text{e}\text{r}\:\text{a}\text{b}\text{s}\text{o}\text{r}\text{p}\text{t}\text{i}\text{o}\text{n}=\:\frac{\text{C}-\text{D}}{\text{D}}\times\:100.$$\end{document}

A:- wt. of pycnometer + sample + water, B:- wt. of pycnometer + wt. of water, C:- wt. of saturated surface dry sample, D:- wt. of oven dry sample.

Dry density

The dry density, which is determined for the Paver blocks extracted from the curing water basin right before compression strength, is the average of the dry densities of three cubes for each curing age. Equation 4 represent of Dry density.4 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:\text{D}\text{r}\text{y}\:\text{d}\text{e}\text{n}\text{s}\text{i}\text{t}\text{y}=\frac{\text{M}\text{a}\text{s}\text{s}}{\text{V}\text{o}\text{l}\text{u}\text{m}\text{e}}.$$\end{document}

Workability

To determine workability, the flow of mortar was monitored using flow table equipment. Throughout the casting process, freshly mixed mortar evenly filled the flow mold. After the mold was removed, the table was dropped 25 times in 15 s. The mortar flow was measured by taking the space that remains after the mortar has been dispersed, as measured from the flow mold’s base diameter.

Compressive strength

Paver blocks were made in accordance with a 1:2 mix ratio. Compression testing was done on the UTM device. The cubes were examined while they were still wet, right after being removed from the water storage. For every testing age, the average of four cubes’ compression strengths was noted. Equation 5 represent of compressive strength.5 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:\text{C}\text{o}\text{m}\text{p}\text{r}\text{e}\text{s}\text{s}\text{i}\text{v}\text{e}\:\text{s}\text{t}\text{r}\text{e}\text{n}\text{g}\text{t}\text{h}=\:\frac{Load\left(N\right)}{Area\left(\text{m}\text{m}2\right)}.$$\end{document}

Ultrasonic pulse velocity (UPV)

UPV is a method of testing that is commonly used in the construction industry to evaluate the strength and durability of concrete and other building materials. The measurement technique involves analyzing the velocity of an ultrasonic pulse as it traverses the substance, offering significant insights into its characteristics and possible flaws.

Rebound test

The Rebound Hammer Test method for paver blocks involves positioning the rebound hammer perpendicular to the surface of the block and applying a consistent force to measure the rebound number. Multiple readings are taken across different points of the block to ensure accuracy. The average rebound number is then correlated to the CS of the paver block42.

Results and discussion

Water absorption

The first step in the water absorption test process for paver blocks is to paint both sides of the blocks with a waterproof material to stop water from seeping in from the sides. The initial dry weight (W1) of every block is precisely measured once the paint has dried fully. The blocks are then completely immersed to a height of 30% in a water bath that is kept at a constant 27 ± 2 °C to guarantee uniform testing circumstances. Following a five-hour soaking period, the blocks are taken out of the water bath and any extra surface water is carefully wiped out without compromising the water absorbed in the pores. Without delay, the wet weight (W2) is noted. Figure 5.

Fig. 5 Water absorption.

When the amount of recycled glass aggregates (RGA) increased, many studies noticed a significant drop in water absorption and found that adding WG increased the durability of the concrete as shown in Fig. 6. The decrease in water absorption was attributed to WGA, a substance that does not absorb water. Other researchers have obtained a similar result43–45. Ordinarily, concrete is thought to be of high quality if its rate of water absorption is less than 10%. Reference46 shown that a concrete mix containing more WG produced WG lightweight concrete by lowering by conducting experiments, we were able to determine the rate at which water is absorbed and reduce the average weight by 5% for the mixture that contains 40% WG. In comparison to natural aggregates, glass particles absorb very less water, which results in WG concrete having less water absorption47–49.

Fig. 6 Water absorption of glass waste concrete.

Dry density

The following method was used to ascertain the density of paver blocks. The dimensions of paver (220 mm*185 mm*70 mm). Each paver block was taken out of the curing environment after the 7 days and 28 days curing period, and its weight was precisely determined with a calibrated weighing scale shows in Fig. 7. Through the maintenance of regular density measurements, we can evaluate the quality and homogeneity of the paver blocks, thereby guaranteeing their appropriateness for a range of construction applications. The outcomes of this process offer crucial information for improving the mix design and paver block performance in real-world applications.

Fig. 7 Dry density.

The lower specific gravity value of WG compared to FA can be considered41. By incorporating fine WGA into concrete mixtures, there was a noticeable reduction in unit weight and concrete density50–52. However, it was found that concrete with 10% crushed WG had a higher fresh density compared to the reference concrete as shown in Fig. 8. On the other hand, however, the density of all other crushed WG samples with replacement levels above 15% was lower than that of the reference concrete. This indicates that increasing the replacement of FA with WG resulted in a decrease in the density of the concrete.

Fig. 8 Dry density of glass waste concrete.

Workability

In this experimental investigation, we investigated how the workability and mechanical properties of concrete are affected when sustainable alternative materials are used in place of FA. Different waste glass particle sizes and shapes influence workability by affecting mix cohesiveness, flow, and segregation, impacting the ease of handling and compaction in concrete or mortar mixtures. We evaluated the consistency and workability of the concrete by using the flow table apparatus. RGA were used to partially substitute FAs in a variety of concrete compositions using the flow table test Fig. 9, a classic technique for assessing the flow characteristics of concrete. In order to drop the Table 25 times in 15 s, freshly made mortar was poured evenly into the flow mould. To find the ideal combination that balances workability 175 mm, various substitution amounts of 10%, 20%, 30%, 40%, and 50% were tested. Using gunny bags to maintain consistent moisture levels necessary for effective curing, the specimens were hydrated on a daily basis during their 7-day curing period. The results (Fig. 10) showed that RGA is a viable substitute for conventional FAs, offering certain mechanical property improvements at the same time as preserving sufficient workability.

Fig. 9 Flow table.

Fig. 10 Workability of glass waste concrete.

Prior research has found that including WG as a FA in concrete results in a decrease in slump values because of WG’s rough texture and geometric characteristics7,49,53. However, refs.54,55 some other experts have pointed out that the inclusion of WG in concrete has led to an increase in slump value, ultimately improving its workability. It is thought that this behaviour is greatly influenced by the WG surface’s smooth surface., Interestingly56,57 some studies have found that the slump value remains consistent regardless of whether WG is present or not in the concrete43. It has been found that the particle size of WG has a notable impact on the workability of concrete. Specifically, when using 20% WG as FA, the water-cement ratio needs to be increased. Additionally, the decrease in size may be attributed to the presence of sharp edges in the WG utilised in the concrete mixture58.

Compressive strength

The CS of the WG concrete mixes after 7 and 28 days can be found. Figures 11 and 12 shows the ratios of increase in CS. By incorporating different percentages of glass aggregate (0%, 10%, 10%, 20%, 30%, 40%, 50%, 100% wt%) After testing, the failure structure is displayed in Fig. 11. When the vertical direction is placed, the first crack appears in the web part. when compared to the reference sample with a strength of fc = 53 ± 1 MPa. The finer grain of WG, which offers a superior filler effect, may be the cause of the improvement in CS. The interaction between the silica from WG and the calcium hydroxide from cement hydration may lead to increased CSH gel formation and improved CS. When 20% more WG is replaced, the CS will increase; however, after 20% more replacement, the CS will drop. Reference59 also made a similar observation regarding the replacement of 20% of FA with WG particles. Reference60 also found that the addition of WG to the concrete mix led to a significant improvement in CS. This was attributed to the pozzolanic activity of WG, which led to the formation of a denser microstructure. Unreactive cement can lead to a reduction in the CS of recycled aggregate concrete.

Fig. 11 CS of glass waste concrete.

Fig. 12 CS and failure modes.

Ultrasonic pulse velocity (UPV)

The UPV test is a valuable tool in evaluating the consistency of concrete quality, identifying the presence of voids, cavities, and cracks in concrete, and tracking the progress of concrete strength development. It is clear that UPV tends to increase as age progresses, primarily because the density of the concretes becomes more consistent in hydration and there are fewer voids and discontinuity points within the concretes. It appears that incorporating a small percentage (10%) of WG as a replacement material has led to an increase in UPV. This suggests that WG concretes may have a more condensed or uniform structure. On the other hand, as the percentage of WG increased, there was a noticeable decline in UPV. The increase in UPV values at 0–60% WGA replacement as shown in Figs. 13 and 14 is caused by the growing pozzolanic interaction between Ca(OH)2 and the active components, silica and alumina, in the incredibly small WGA particles, which reduces the structure of concrete50,61. Higher porosity resulting from the decline in UPV values with increased WGA replacement amounts may be explained by inadequate compactness and the irregular structure of the concrete62,63. Each of the mixes were categorised as extremely good grade concrete because they all had high UPV values (> 4100 m/s)64. In accordance with the findings of other studies, the UPV values rose approximately linearly as CS increased61,65 Fig. 13.

Fig. 13 UPV of glass waste concrete.

Fig. 14 UPV.

Rebound test

Concrete’s CS can be evaluated non-destructively using the Rebound Hammer Test, sometimes referred to as the Schmidt Hammer Test. With this method, the concrete surface is affected by a spring-controlled mass that glides inside a cylindrical enclosure. The mass’s rebound distance, expressed as the rebound number, is a proxy for the material’s CS and indicates the degree of surface, hardness as depicted in Figs. 15 and 16.

Fig. 15 Rebound Hammer of glass waste concrete.

Figure 15 organised into a grid to guarantee thorough and trustworthy data collecting. Applying the rebound hammer methodically at every grid intersection enables a uniform and consistent sample of the properties of the concrete throughout the whole region. By using this procedure, the impact of regional variances is reduced, and the overall assessment’s accuracy is improved. The test’s rebound values are used to calculate the concrete’s CS, providing a rapid and useful assessment of the material’s structural soundness.

Fig. 16 Rebound Hammer test.

Machine learning

Result analysis with RSM

Table 6 experimental results has been established by employing mathematical methods. Research has shown that incorporating WG as a substitute for FA can result in enhanced mechanical properties and other desirable traits in cement mortar. Different statistical methods, like the connection approach, have been used to analyze the relationships between variables in experiments. Although RSM models have high R2 values, they have a number of limitations, such as the possibility of overfitting and limited generalizability beyond the settings that were tested. Because of these constraints, it is important to exercise caution when extending the results to more general situations or different inputs. By utilizing RSM, researchers have made predictions about the properties of WG66,67.

Table 6 Build information.

File version	13.0.5.0			
Study type	Response surface	Subtype	Randomized	
Design type	Blank spreadsheet	Runs	7.00	
Design model	Quadratic	Blocks	No blocks	
Build time (ms)	12.00			

Water absorption analysis

The analysis of variance (ANOVA) Table 7 indicates that the model is highly significant, with a strong relationship between the variables. This is supported by an F-value of 19.54 and a p-value of 0.0180. One of the factors, WG (A), has a strong influence on the response, as indicated by the high F-value of 1.34 and a low p-value of 0.3314. It appears that the FA (B) does not have a significant impact on the response, as suggested by a p-value of 0.0643. The interaction between WG and FA (AB) does not show any significant results, as indicated by a p-value of 0.0322. The low residual sum of squares (0.0915) indicates that the model is a good fit for the data.

Table 7 Water absorption.

Source	Sum of squares	df	Mean square	F-value	p-value		
Model	1.79	3	0.5963	19.54	0.0180	Significant	
A-WG	0.0408	1	0.0408	1.34	0.3314		
B-FA	0.2504	1	0.2504	8.21	0.0643		
AB	0.4386	1	0.4386	14.37	0.0322		
A²	0.0000	0					
B²	0.0000	0					
Residual	0.0915	3	0.0305				
Cor Total	1.88	6					

Fit statistics

This table presents statistical data from a regression analysis. The standard deviation (Std. Dev.) of 0.1747 indicates the variability in the data, while the average is represented by the mean value of 2.74 outcome measured. The coefficient of variation (C.V. %) of 6.38% suggests moderate variability relative to the mean. The model’s strong performance is demonstrated by a R2 value of 0.9513 and an adjusted R² of 0.9026, suggesting that the model effectively accounts for a substantial amount of the variability in the response variable. However, the predicted R2 is not available (NA). An adequate precision value of 11.1076 confirms that the model has a sufficient signal-to-noise ratio, indicating its reliability for prediction and optimization. Equation 6 shows that Water absorption using with RSM.6 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{Water absorption}}\,=\,+\,{\text{2}}.{\text{63978}}\, - \,0.00{\text{4744WG}}\,+\,0.00{\text{9923FA}} - 0.000{\text{281WG }}*{\text{ FA}}$$\end{document}

Dry density

Based on Table 8, it is evident that the model is extremely significant, indicating a robust correlation between the variables. The findings are strongly supported by statistical analysis, with an F-value of 601.34 and a p-value of 0.0001. One of the factors, WG (A), significantly impacts the response, as evidenced by the substantial F-value of 417.97 and a statistically significant p-value of 0.0003. Based on the p-value of 0.1250, it seems that the FA (B) does not have a significant impact on the response. The interaction between WG and FA (AB) does not yield any noteworthy findings, as evidenced by a p-value of 0.7039. The small residual sum of squares (41.34) suggests that the model is well-suited to the data.

Table 8 Dry density.

Source	Sum of squares	df	Mean square	F-value	p-value		
Model	24860.00	3	8286.67	601.34	0.0001	Significant	
A-WG	5759.75	1	5759.75	417.97	0.0003		
B-FA	61.55	1	61.55	4.47	0.1250		
AB	2.41	1	2.41	0.1749	0.7039		
A²	0.0000	0					
B²	0.0000	0					
Residual	41.34	3	13.78				
Cor Total	24901.34	6					

Fit statistics

This table presents statistical data from a regression analysis. The standard deviation (Std. Dev.) of 3.71 indicates the variability in the data, while the average is represented by the mean value of 2.74 outcome measured. The coefficient of variation (C.V. %) of 0.113% suggests moderate variability relative to the mean. The model’s strong performance is demonstrated by a R2 value of 0.9983 and an adjusted R2 of 0.9967, suggesting that the model effectively accounts for a substantial amount of the variability in the response variable. However, the predicted R2 is not available (NA). An adequate precision value of 69.0923 confirms that the model has a sufficient signal-to-noise ratio, indicating its reliability for prediction and optimization. Equation 7 shows that Dry density using with RSM.7 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{Dry density}}\,=\,+\,{\text{3391}}.{\text{83}}0{\text{33}}-{\text{1}}.{\text{78326 WG}}\,+\,0.{\text{15557}}0{\text{ FA }} - 0.000{\text{658 WG }}*{\text{ FA}}$$\end{document}

Compressive strength

The analysis of variance (ANOVA) Table 9 indicates that the model is highly significant, with a strong relationship between the variables. This is supported by an F-value of 10.85 and a p-value of 0.0405. One of the factors, WG (A), has a strong influence on the response, as indicated by the high F-value of 13.99 and a low p-value of 0.0333. It appears that the FA (B) does not have a significant impact on the response, as suggested by a p-value of 0.3784. The interaction between WG and FA (AB) does not show any significant results, as indicated by a p-value of 0.3268. The low residual sum of squares (17.56) indicates that the model is a good fit for the data.

Table 9 Compressive strength.

Source	Sum of squares	df	Mean Square	F-value	p-value		
Model	190.52	3	63.51	10.85	0.0405	Significant	
A-WG	81.87	1	81.87	13.99	0.0333		
B-FA	6.22	1	6.22	1.06	0.3784		
AB	8.00	1	8.00	1.37	0.3268		
A2	0.0000	0					
B2	0.0000	0					
Residual	17.56	3	5.85				
Cor total	208.07	6					

Fit statistics

Here is the statistical data obtained from a regression analysis, as shown in the Table 9. The data exhibits a variability indicated by a standard deviation of 2.42, while the mean value of 2.74 represents the average outcome measured. The C.V. of 6.39% indicates a moderate level of variability compared to the mean. The model’s impressive performance is evident from the high R2 value of 0.9156 and an adjusted R2 of 0.8312. This indicates that the model successfully captures a significant portion of the variability in the response variable. Unfortunately, the predicted R2 is not available. A precision value of 8.9215 demonstrates that the model is reliable for prediction and optimization, as it has a strong signal-to-noise ratio. Equation 8 demonstrates the utilization of CS in conjunction with RSM. Equation 8 shows that Compressive strength using with RSM.8 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{Compressive strength}}\,=\,+\,{\text{47}}0.00{\text{415}}-0.{\text{2126}}0{\text{4 WG }} - 0.0{\text{49454 FA}}\,+\,0.00{\text{1198 WG }}*{\text{ FA}}$$\end{document}

UPV

The analysis of variance (ANOVA) Table 10 reveals a highly significant model, indicating a strong relationship between the variables. These findings are backed by a significant F-value of 131.76 and a p-value of 0.0011. One of the factors, WG (A), has a significant impact on the response, as evident from the high F-value of 19.12 and a low p-value of 0.0221. Based on the p-value of 0.0073, it seems that the FA (B) does not have a significant impact on the response. The interaction between WG and FA (AB) does not yield any noteworthy findings, as evidenced by a p-value of 0.0102. The small residual sum of squares (831.39) suggests that the model is well-suited to the data.

Table 10 UPV.

Source	Sum of squares	df	Mean Square	F-value	p-value		
Model	1.095E + 05	3	36513.35	131.76	0.0011	SIGNIFICANT	
A-WG	5298.89	1	5298.89	19.12	0.0221		
B-FA	11775.87	1	11775.87	42.49	0.0073		
AB	9327.39	1	9327.39	33.66	0.0102		
A²	0.0000	0					
B²	0.0000	0					
Residual	831.39	3	277.13				
Cor total	1.104E + 05	6					

Fit statistics

This Table 10 presents statistical data from a regression analysis. The standard deviation (Std. Dev.) of 16.65 indicates the variability in the data, while the average is represented by the mean value of 2.74 outcome measured. The coefficient of variation (C.V. %) of 0.4025 suggests moderate variability relative to the mean. The model’s strong performance is demonstrated by a R2 value of 0.9513 and an adjusted R2 of 0.9026, suggesting that the model effectively accounts for a substantial amount of the variability in the response variable. However, the predicted R2 is not available (NA). An adequate precision value of 30.6917 confirms that the model has a sufficient signal-to-noise ratio, indicating its reliability for prediction and optimization. Equation 9 shows that UPV using with RSM.9 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{UPV}}\,=\,+\,{\text{4118}}.{\text{41714}}-{\text{1}}.{\text{71}}0{\text{43WG}}\,+\,{\text{2}}.{\text{15185FA}} - 0.0{\text{4}}0{\text{921WG }}*{\text{ FA}}$$\end{document}

Rebound Hammer

The analysis of variance (ANOVA) Table 11 indicates that the model is highly significant, with a strong relationship between the variables. This is supported by an F-value of 94.50 and a p-value of 0.0018. One of the factors, WG (A), has a strong influence on the response, as indicated by the high F-value of 37.87 and a low p-value of 0.0086. It appears that the FA (B) does not have a significant impact on the response, as suggested by a p-value of 0.0554. The interaction between WG and FA (AB) does not show any significant results, as indicated by a p-value of 0.0337. The low residual sum of squares (0.3488) indicates that the model is a good fit for the data.

Table 11 Rebound Hammer.

Source	Sum of squares	df	Mean square	F-value	p-value		
Model	32.96	3	10.99	94.50	0.0018	Significant	
A-WG	4.40	1	4.40	37.87	0.0086		
B-FA	1.08	1	1.08	9.30	0.0554		
AB	1.61	1	1.61	13.87	0.0337		
A2	0.0000	0					
B2	0.0000	0					
Residual	0.3488	3	0.1163				
Cor total	33.31	6					

Fit statistics

Here is some statistical data Table 11 obtained from a regression analysis. The data exhibits a variability indicated by a standard deviation of 0.3410. The mean value of 2.74 represents the average outcome measured. The C.V. % of 1.04 indicates a moderate level of variability compared to the mean. The model’s high level of accuracy is demonstrated by a R2 value of 0.9895 and an adjusted R2 of 0.9791, suggesting that the model effectively accounts for a substantial amount of the variability in the response variable. Unfortunately, the predicted R2 is not available. A precision value of 27.1287 demonstrates that the model is reliable for prediction and optimization, as it has a strong signal-to-noise ratio. Equation 10 shows that Rebound Hammer using with RSM.10 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{Rebound Hammer}}\,=\,+\,{\text{33}}.{\text{87216}}\, - \,0.0{\text{493}}0{\text{4WG}}\,+\,0.0{\text{2}}0{\text{619FA}} - 0.000{\text{538WG }}*{\text{ FA}}$$\end{document}

Analysis with RSM contour maps and 3D surface

The use of RSM to enhance the production of paver blocks by incorporating WG. It focusses on optimising key parameters to achieve optimal results. Contour maps and 3D surface plots are effective tools for visualizing the interaction effects of various parameters68. They help identify the optimal conditions for maximizing the desired properties of the paver blocks. This visualization helps to grasp the intricate connections between variables, which contributes to the advancement of more environmentally friendly construction materials. Figure 17 shows the RSM contour maps and 3D surface (a and b) Water absorption (c and d) Dry density (e and f) CS (g and h) UPV (I and j) Rebound Hammer.

Fig. 17 RSM contour maps and 3D surface (a–j).

Comparison of RSM actual value and prediction value

Figure 18 displays the estimated Actual values and Predicted values for the test. Both models expected and actual outcomes agreed with the experimental data. (a) Water absorption. (b) Dry density (c) CS (d) UPV (e) Rebound hammer. It shows how well the model estimates and the measured data correspond, as seen by the radar plot. the precision with which the test findings have been applied once more.

Fig. 18 Comparison of RSM actual value and prediction value (a–e).

Table 12 shows the predicted accuracy for several mechanical qualities. The most accurate prediction is the CS, which has an R2 value of 0.998, a low RMSE of 1.586, and a MAPE of 3.20%. In a similar vein, the RMSE of 10.899 and MAPE of 0.20% indicate the low error rate and high R2 of 0.992 for the UPV. With R2 values of 0.952 and 0.99, respectively, the dry density and rebound hammer tests likewise demonstrate great prediction accuracy and very low error metrics. Water absorption, with a R2 of 0.915, retains a great predictive performance although being significantly less precise. All things considered; the model shows strong predictive power for every mechanical property that is evaluated.

Table 12 RSM performance.

Mechanical properties	RMSE	MAPE (%)	MAE	R 2	
Water absorption	0.114	2.80	0.083	0.915	
Dry density	2.431	0.10	1.934	0.952	
CS	1.586	3.20	1.231	0.998	
UPV	10.899	0.20	8.556	0.992	
Rebound Hammer	0.222	0.50	0.164	0.99	

Performance analysis

Using performance parameters, the system’s precision and error quantification have been evaluated. The initial parameter is the R2 coefficient, which quantifies the extent to which a variable’s variance is accounted for. It quantifies the amount of data that can be accurately described by the model. The value of R2 varies between 0 and 169.

SSE is Sum of Squares Error\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:SSE=\sum\:_{i=1}^{N}{\left({a}_{PREDICT}-\stackrel{-}{a}\right)}^{2}$$\end{document}

N is number of data aPREDICT is actual value \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:\stackrel{-}{a}$$\end{document} is predicted value

MAE is the formula, which is given by Equation\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:MAE=\left(\frac{1}{N}\right)*\left(\sum\:_{i=1}^{N}\left({a}_{PREDICT}{-a}_{PREDICT}\right)\right)$$\end{document}

N is the total number of trials, aPREDICT is the value predicted for the jth neuron, and aTARGET is the value obtained experimentally.

MSE mean squared error\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:MSE=\left(\frac{1}{N}\right)\sum\:_{i=1}^{N}{\left({a}_{PREDICT}-{\widehat{a}}_{PREDICT}\right)}^{2}$$\end{document}

N is number of data aPREDICT is actual value \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:\widehat{a}$$\end{document}PREDICT is predicted value.

RMSE is the average deviation of a data point (targeted) from the model’s predicted value (expected), expressed as the square root of the mean square error. A lower RMSE Equation indicates a better performing model\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:RMSE=\sqrt{\left(\frac{1}{N}\right)*\left(\sum\:_{i=1}^{N}{\left({a}_{PREDICT}{-a}_{PREDICT}\right)}^{2}\right)}$$\end{document}

The initial parameter is the R2 coefficient, which represents the absolute proportion of a variable’s variance.\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{R}^{2}=1-\left(\frac{\sum\:_{i=1}^{N}{\left({a}_{PREDICT}-{a}_{TARGET}\right)}^{2}}{{\sum\:}_{i=1}^{N}{\left({a}_{PREDICT}\right)}^{2}}\right)$$\end{document}

Conclusion

This literature study offers a comprehensive examination of how well-executed circular economy methods might solve climate change and other related challenges. The three primary components of this review article’s investigation of the circular economy as a waste management strategy are research future direct through sustainable goal, quantitative analysis, and qualitative analysis.

The argument is boosted by looking into the fields of FA, mechanical characteristics, recycling, sustainable development, waste disposal, WG, and water absorption.

Using 20–30% waste glass as fine aggregate in concrete can significantly improve its strength and durability, making it a more sustainable option.

CS reached as high as 30.80 MPa after 28 days of curing, according to the study, with up to 20% WGA inclusion maintaining strength attributes similar to those of the control mix.

WG grain forms are thought to play an aspect in the workability of WG concrete specimens, as WG content increased.

The results of RSM investigations showed that the R2 values for the following parameters were 0.9513, 0.9983, 0.9156, 0.9925, and 0.9895 for water absorption, dry density, CS, UPV and rebound hammer, respectively.

Advanced modelling techniques can enhance performance predictions for stress distribution and durability. Integrating these methods with experimental data helps optimize waste glass paver blocks by refining mix design and structural integrity.

By suitably applying the RSM analysis methods to the data of the experiments measuring water absorption, dry density, CS, UPV, and rebound hammer, accurate effects estimation has been made possible.

Additionally, the program can enhance the mixtures that are typically utilised in tasks related to civil engineering. The reported models can serve as a template in engineering applications to reduce the amount of time wasted on many experiments. For many more applications in civil engineering, researchers will be able to investigate the many models of the extreme gradient boosting tree (XGBoost), Gaussian process regression, decision tree (DT), and support vector machine programs in the future. This study can improve its prediction modelling and optimisation with advanced machine learning algorithms as XGBoost, Gaussian Process Regression (GPR), Decision Tree (DT), and Support Vector Machine (SVM). These algorithms let researchers create more accurate and generalised models to predict the mechanical qualities and durability of glass waste paver blocks. GPR makes probabilistic predictions with uncertainty quantification, whereas XGBoost and DT manage complicated variable interactions. The robustness of SVM in high-dimensional spaces helps improve classification and regression problems. Integrating these methodologies would strengthen mix design analysis and optimisation, enhancing paver block performance and sustainability.

Abbreviations

WG Waste glass

UPV Ultrasonic pulse velocity

RSM Response Surface Methodology

CS Compressive strength

FA Fine aggregate

WGA Waste glass aggregates

Author contributions

BGN: Conceptualization, Methodology, Investigation, Formal Analysis, Validation, Writing—Original draft. NG: Conceptualization, Methodology, Investigation, Writing—Original draft. DR: Validation, Writing—Original draft. George Uwadiegwu Alaneme: Conceptualization, Writing—Original draft.

Data availability

The datasets used and/or analyses during the current study are available from the corresponding author on reasonable request.

Declarations

Competing interests

The authors declare no competing interests.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
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