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\DOI{10.5802/crchim.380}
\datereceived{2024-02-05}
\daterevised{2024-05-20}
\datererevised{2024-07-17}
\datererevised{2024-10-12}
\dateaccepted{2025-01-17}
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\dateposted{2025-02-27}
\begin{document}

%\copyeditor{RB}

\begin{noXML}

\CDRsetmeta{articletype}{research-article}

\title{Production of eco-friendly alkali-activated concrete for
sustainable highway infrastructure by  effective
utilization of recycled construction and foundry wastes} 

\alttitle{Production de b\'{e}ton activ\'{e} par des alcalis et
respectueux de l'environnement par l'utilisation efficace de
d\'{e}chets de construction et de fonderie pour une infrastructure
routi\`{e}re durable}

\author{\firstname{Akhila} \lastname{Sheshadri}\CDRorcid{0000-0001-5061-6522}}
\address{Department of Civil Engineering, Nitte (Deemed to be
University), NMAM Institute of Technology (NMAMIT), Karnataka
574110, India}

\author{\firstname{Shriram} \lastname{Marathe}\CDRorcid{0000-0002-9713-507X}\IsCorresp}
\addressSameAs{1}{Department of Civil Engineering, Nitte (Deemed to be
University), NMAM Institute of Technology (NMAMIT), Karnataka
574110, India}
\address{Department of Materials Engineering and Construction
Processes, Wroclaw University of Science and Technology,
Wybrze\.{z}e Wyspia\'{n}skiego 27, 50-372 Wroclaw, Poland}
\email[S. Marathe]{ram.nmamit@gmail.com}

\author{\firstname{Sonia} \lastname{Lobo}\CDRorcid{0000-0002-8177-8855}}
\address{Department of Humanities, Nitte (Deemed to be University),
NMAM Institute of Technology (NMAMIT), Karnataka  574110, India}

\author{\firstname{\L{}ukasz} \lastname{Sadowski}\CDRorcid{0000-0001-9382-7709}}
\addressSameAs{2}{Department of Materials Engineering and Construction
Processes, Wroclaw University of Science and Technology,
Wybrze\.{z}e Wyspia\'{n}skiego 27, 50-372 Wroclaw, Poland}

\keywords{\kwd{Sustainability}
\kwd{Circular economy}
\kwd{Waste foundry sand}
\kwd{Recycled concrete aggregate}
\kwd{Alkali-activated concrete}
\kwd{Linear regression}
\kwd{Statistical analysis}
}

\altkeywords{\kwd{Durabilit\'{e}} \kwd{\'{E}conomie circulaire}
\kwd{D\'{e}chets de sable de fonderie} \kwd{Granulats de b\'{e}ton
recycl\'{e}} \kwd{B\'{e}ton activ\'{e} par l'alcali}
\kwd{R\'{e}gression lin\'{e}aire} \kwd{Analyse statistique}} 

\thanks{Nitte Education Trust (NUFR grant number
N/RG/NUFR1/NMAMIT/2022/09 dated 28.12.2022).}

\begin{abstract}
A sustainable substitute for conventional pavement concrete composite
is provided by alkali-activated concrete (AAC), which is made from industrial
byproducts and which has less of an impact on the environment than
conventional cement-based materials. The purpose of this research is to
combine waste foundry sand (WFS) with recycled concrete aggregates
(RCAs) to manufacture alkali-activated slag  concrete (AASC) and to
create a circular economy model that maximizes the use of recovered
materials, minimizes waste disposal issues, and uses less energy during
the extraction and manufacturing of raw resources. The desirable grade
of the AASC mix was carefully formulated to comply with Indian
Standards for high-quality pavement applications. The optimal mix
proportions were determined through a series of experiments based on
the hardened and fresh properties of concrete. The resulting AASC
composite consisted of 20\% WFS and 50\% RCA based on volume. The ideal
compressive strength of the mix of 64 MPa was attained after 28 days,
which exceeded the required strength for the desired paving concrete. The
combination that was used showed a rise of 9.67\% in split tensile
strength and an increase of 20.3\% in flexural strength between the
period of 7 and 28 days. Models of simple linear regression (SLR) and
multiple linear regression (MLR) were used to predict compressive
strength. SLR analysis revealed that RCA and WFS had considerable
negative impacts on compressive strength, with coefficients of
\tralicstex{-}{${-}$}0.026315 and \tralicstex{-}{${-}$}0.13476, respectively. With an R-squared value of
0.8044, the MLR model demonstrated strong predictive ability. The study
focuses on how AASC supports resource efficiency, promotes sustainable
building practices, and reduces reliance on natural aggregates. The
findings contribute significantly to our understanding of how AAC might
be widely applied in the building industry to promote a circular
economy and to enhance environmental sustainability.
{\vspace*{-4pt}}  
\end{abstract}

\begin{altabstract}
Un b\'{e}ton activ\'{e} par des
alcalis a \'{e}t\'{e} fabriqu\'{e} \`{a} partir de sous-produits
industriels. Il peut constituer un substitut durable aux composites de
b\'{e}ton de chauss\'{e}e conventionnels et un moindre impact sur
l'environnement. L'objectif de cet travail de recherche est de combiner
des d\'{e}chets de sable de fonderie (WFS) avec des granulats de
b\'{e}ton recycl\'{e}s (RCA) pour fabriquer un b\'{e}ton alcalin
activ\'{e} \`{a} base de laitier (AASC) et de cr\'{e}er un mod\`{e}le
d'\'{e}conomie circulaire qui maximise l'utilisation de mat\'{e}riaux
r\'{e}cup\'{e}r\'{e}s, minimise les probl\`{e}mes d'\'{e}limination des
d\'{e}chets et utilise moins d'\'{e}nergie lors de l'extraction et de
la fabrication de ressources brutes. La qualit\'{e} d\'{e}sir\'{e}e du
m\'{e}lange AASC a \'{e}t\'{e} soigneusement formul\'{e}e pour se
conformer aux normes indiennes pour des applications de chauss\'{e}e de
haute qualit\'{e}. Les proportions optimales du m\'{e}lange ont
\'{e}t\'{e} d\'{e}termin\'{e}es par une s\'{e}rie d'exp\'{e}riences
bas\'{e}es sur les propri\'{e}t\'{e}s du b\'{e}ton durci et frais. Le
composite AASC obtenu se compose de 20 \% de WFS et de 50 \% de RCA en
volume. La r\'{e}sistance id\'{e}ale \`{a} la compression du
m\'{e}lange a \'{e}t\'{e} \'{e}valu\'{e}e \`{a} 64 MPa apr\`{e}s 28
jours. Elle d\'{e}passe la r\'{e}sistance requise pour le b\'{e}ton de
pavage souhait\'{e}. La combinaison utilis\'{e}e a montr\'{e} une
augmentation de 9,67 \% de la r\'{e}sistance \`{a} la rupture et une
augmentation de 20,3 \% de la r\'{e}sistance \`{a} la flexion entre la
p\'{e}riode de 7 et 28 jours. Des mod\`{e}les de r\'{e}gression
lin\'{e}aire simple (SLR) et de r\'{e}gression lin\'{e}aire multiple
(MLR) ont \'{e}t\'{e} utilis\'{e}s pour pr\'{e}dire la r\'{e}sistance
\`{a} la compression. L'analyse SLR a r\'{e}v\'{e}l\'{e} que le RCA et
le WFS avaient des impacts n\'{e}gatifs consid\'{e}rables sur la
r\'{e}sistance \`{a} la compression, avec des coefficients de \tralicstex{-}{${-}$}0,026315
et \tralicstex{-}{${-}$}0,13476, respectivement. Avec une valeur R\textsuperscript{2} {de
0,8044, le mod\`{e}le MLR a d\'{e}montr\'{e} une forte capacit\'{e} de
pr\'{e}diction. Les r\'{e}sultats obtenus contribuent de mani\`{e}re
significative \`{a} la compr\'{e}hension de la mani\`{e}re dont la CAA
pourrait \^{e}tre largement appliqu\'{e}e dans l'industrie du
b\^{a}timent afin de promouvoir l'\'{e}conomie circulaire et
d'am\'{e}liorer la durabilit\'{e} environnementale.}
\end{altabstract}

\shortrunauthors

\maketitle

\twocolumngrid

\end{noXML}


\section{Introduction}

The construction industry is facing a growing challenge in balancing
development and environmental responsibility. Researchers are actively
exploring alternative binders, with alkali-activated concrete (AAC)
emerging as a viable and hopeful resolution. AAC utilizes industrial
byproducts such as fly ash and slag, in turn reducing
$\mathrm{CO}_{2}$ emissions~\cite{1,2}. The expanding scope of modern
industrial operations produces significant amounts of waste materials,
presenting substantial environmental and economic difficulties. The
proliferation of industrial waste generated by modern society is
increasing at a rapid pace, and therefore advanced methods are required
to gather and repurpose these substances~\cite{3}. Due to the
inadequacy of landfills and the rising costs of disposal, resource
conservation is gaining popularity in developed countries. Roads, which
necessitate a substantial quantity of aggregates, serve as a pragmatic
avenue for recycling waste materials. Several waste materials may be
recycled and then used to build roads. Some of these resources include
broken concrete, steel slag, municipal solid waste, incinerator ash,
recovered asphalt pavement, nonferrous slag, and discarded plastic
fibers or granules~\cite{4}. In pavement construction applications,
these supplementary materials have been utilized as aggregates or
mineral fillers~\cite{5}. Marathe et~al.~\cite{6} reported that
air-cured AAC performed better when compared to 
ordinary Portland cement concrete (OPCC) mixes.
Mithun et~al.~\cite{7} showed that AAC mixes that are produced with a
fractional replacement of fine aggregates by copper slag provided
satisfactory performance, thereby paving the way for the replacement of
aggregates in AAC. The construction and demolition (C\&D) sector
generates three billion metric tons of waste each year (predominantly
consisting of nonhazardous and inactive materials)~\cite{8}. Waste
from C\&D operations makes waste management issues more severe by
converting valuable land into dump sites and in turn increase the
cost of landfills~\cite{9,10,11}. However, this seemingly innocuous
waste stream harbors substantial potential for resource conservation
and the implementation of a circular economy. The use of C\&D waste
(used as recycled concrete aggregates [RCAs]) as a substitute for natural
coarse aggregates (NCAs) is advantageous for mitigating the
environmental concerns brought on by \mbox{the construction} sector, and can
potentially save up to 60\% of NCA~\cite{12}. Using recycled aggregates
in concrete composite leads to a 28\% drop in carbon dioxide emissions
as a result of both reduced energy consumption during production and
lower transportation emissions when reprocessing facilities for
recycling are in close proximity~\cite{13}. Recent studies have delved
into incorporating RCA into AAC mixtures. Research by Yang et~al.~\cite{14}  examined how RCA affects the mechanical characteristics
of AAC. Aggarwal et~al.~\cite{15} explored methods for converting
concrete waste into a usable AAC binder. RCA offers a dual benefit:
reducing waste sent to landfills and conserving natural aggregates.
Recent studies have shown that the incorporation of RCA into AAC can
induce mechanical properties comparable to natural aggregate concrete
while at the same time enhancing sustainability. These studies
highlight the potential of AAC for reducing reliance on virgin
materials and  C\&D waste. 

Foundry sand, commonly composed of silica sand or lake sand, is
utilized in the fabrication of cores and molds employed in the
production of nonferrous and ferrous metal castings~\cite{3,16,17}.
When it is not in use anymore, it is classified as waste foundry sand
(WFS) and can be utilized to produce a desirable quality of concrete
and also as a construction material~\cite{18}. In concrete, WFS can be
used as a fractional replacement for ordinary sand to attain similar
levels of strength. WFS is characterized by its fine granularity and
high silica content, making it suitable as a fractional replacement for
natural sand in concrete mixtures~\cite{19}. Incorporating WFS into AAC
helps in creating high-performance concrete while also addressing the
issue of foundry waste disposal~\cite{20}. Although Sithole
et~al.~\cite{21} explored the use of WFS in traditional concrete
production, research on its use in AAC remains limited. Sheshadri
et~al.~\cite{18} reported that WFS can be used in AAC up to a maximum
limit of 20\% without any compromise in strength properties. This
was confirmed by microstructure analysis. 

{Conventional road construction methods heavily depend on NCA, which
results in environmental deterioration and nonsustainable
depletion of resources. Therefore, the inclusion of recycled/waste
materials as road construction materials has become a promising
approach to reducing these negative effects~\cite{22}. RCA and WFS are
among the most investigated alternative materials, and provide
\mbox{noteworthy} financial and environmental \mbox{advantages~\cite{23,24}.}
Reusing RCA not only preserves primary resources but also reduces the
volume of waste that is dumped in landfills. Additionally, the use
of WFS promotes more environmentally friendly construction practices
and lowers the expenses associated with waste disposal. Despite the
promising potential of RCA and WFS in AAC, the literature reveals
substantial gaps. First, even though there are studies on RCA and WFS
individually, limited research explores their combined effect in AAC
formulations. Understanding the synergistic effects of these materials
could lead to more efficient and sustainable concrete mixtures. The
goal of this study is to close the existing knowledge gap by conducting
an exhaustive examination of the impact of integrating both RCA and WFS
into a type of AAC mixture. The research assesses the effects of
various levels of the replacement of recycled materials on  fresh
mechanical properties of slag-based AAC (viz., alkali-activated slag
concrete [AASC]). The study uses a systematic experimental method and
performs an analysis to study the significance of these materials in
AASC through statistical (linear regression) analysis. The adaption of
the regressor model predicts the compressive strength for the given mix
design, thereby reducing the trials required for the optimization of
the mix, in turn promoting resource efficiency. By addressing these
objectives, this study seeks to advance the field of sustainable
construction materials and provide practical insights for the
large-scale adoption of AAC in the construction industry.}

\section{Materials and methodology}

This research utilized the following ingredients: ground granulated
blast furnace slag (GGBS), the properties of which are presented in
{Table}~\ref{tab1}, and river sand fine aggregate
(RSFA). RSFA has a specific gravity of 2.62 and water absorption of
2.22\% according to IS~2386: Part 3~\cite{25}, it belongs to zone II as
per IS~383: 1970~\cite{26}, and it has a fineness modulus of 3.91 as per
IS~2386: Part 1~\cite{27}. NCA has a specific gravity of 2.71, and water
absorption of 0.28\% determined in accordance with IS~2386: Part
3~\cite{25}; its fineness modulus is 6.13 according to IS~2386: 
Part~1~\cite{27}. Similarly, WFS has a specific gravity of 2.4 and water
absorption of 3.81\%; it does not belong to any zone as it has a
unimodal particle size \mbox{distribution} in the range of
150--300~${{\upmu}}$m. The specific gravity of the RCA used in the
study is 2.48, its fineness modulus is 6.57, and water absorption is
4.87\%. The alkaline activator solution (AAS) consists of sodium
hydroxide (NaOH) flakes  with a purity level of 97--98\%. Additionally,
it includes liquid sodium silicate (LSS), the composition of which is
ascertained as per IS~14212: 1995~\cite{28}. The specific gravity of the
LSS is 1.57, and its activator modulus (Ms), which measures the
proportion of SiO$_{2}$ to Na$_{2}$O, is 2.23.
The AAS was prepared in order to obtain the desired Ms value by dissolving
NaOH flakes in LSS. In order to modify the ratio of water to the
binder, laboratory tap water was incorporated. Clean water, meeting the
conditions specified in IS~456: 2000~\cite{29}, was used in the
production of concrete. 

\begin{table*}[t!]
\caption{\label{tab1}Properties of GGBS\vspace*{-1pt}}
\tabcolsep=1.2pt
\begin{tabular}{cccccccccccccc}
\thead
{Characteristics} & {F} & {IR} & {Mg} & {SS} & {S} & {LOI} & {Mn} &
{Cl} & {G} & {M} & 
\parbox[t]{1cm}{\centering CaO + MgO + SiO$_{2}$} & 
\parbox[t]{1cm}{\centering CaO + MgO/{\ubreak}SiO$_{2}$} & 
\parbox[t]{1cm}{\centering CaO/{\ubreak}SiO$_{2}$}\vspace*{2pt} \\
\endthead
\parbox[t]{3.2cm}{\centering Requirements as per IS:~12089-1987} & 
\parbox[t]{.8cm}{\centering Min 275} & \parbox[t]{.8cm}{\centering Max 1.5} & 
\parbox[t]{.8cm}{\centering Max 14.0} & \parbox[t]{.8cm}{\centering Max 2.0} & 
\parbox[t]{.8cm}{\centering Max 2.5} & \parbox[t]{.8cm}{\centering Max 3.0} & 
\parbox[t]{.8cm}{\centering Max 2.0} & \parbox[t]{.8cm}{\centering Max 0.1} & 
\parbox[t]{.8cm}{\centering Min 67.0} & \parbox[t]{.8cm}{\centering Max 1.0} &
\parbox[t]{.8cm}{\centering Max 66.66} & ${>}$1.00 & ${<}$1.40 \vspace*{6pt}\\
{Test values} & 360 & 0.3 & 8.32 & 0.18 & 0.26 & 0.05 & 0.08 & 0.021 &
92.84 & 0.09 & 75.96 & 1.21 & 0.96 
\botline
\end{tabular}
\tabnote{F: fineness (m$^{2}$/kg); IR: insoluble residue;
(\%), Mg: magnesia content (\%); SS: sulfide sulfur (\%); 
S: sulfite content (\%);  LOI: loss of ignition (\%); 
Mn: manganese content (\%); Cl: chloride content (\%); 
G: glass content (\%); M: moisture content (\%).}
\vspace*{-2pt}
\end{table*}


The mix proportion for the standard OPCC employed in highways was
developed according to the prescribed methods for creating traditional
OPCC with a design strength for highways and a desired workability
range of 25--75~mm slump, which is equivalent to a compaction factor
value in the range of 0.78--0.85, in accordance with the
specifications outlined in IRC: 44-2017~\cite{30}. Concrete workability
is indicated by the compaction factor value; a higher value represents
greater workability, which in turn results in  better compaction
of concrete, improved density, and enhanced mechanical performance.
This procedure aligns with the standard parameters for concrete
formulation that are outlined in the Indian standard
IS~10262: 2019~\cite{31}. The original mix ratios were adjusted based on
studies in the literature~\cite{2, 6, 32} in order to obtain the required AASC
mix; the final water-to-binder (w/b) ratio was fixed at 0.36, the
AAS Ms value at 1.25, and the Na$_{2}$O concentration at 4.0\% of the gross
binder mass. Specific details for the first composition of the
AASC mix are outlined in {Table~\ref{tab2}}. This composition meets the
criteria regarding both strength and workability, which are required
for quality pavement (highway) composite mixes. 
\looseness=-1

\begin{table*}[t!]
\caption{\label{tab2}Preliminary mix proportion for the slag-based
alkali-activated concrete mix\vspace*{-1pt}}
\begin{tabular}{ccccccccc}
\thead
{Notation} & {GGBS} & {Ms} & {w/b} & {LSS} & {NaOH} & {NCA} & {RSFA} &
{Water} \\
\endthead
Unit & kg & Ratio & Ratio & kg & kg & kg & kg & kg \\
A-0-0 & 493 & 1.25 & 0.4 & 75.12 & 11.18 & 1071.4 & 577.84 & 157.56 
\botline
\end{tabular}
\vspace*{-2pt}
\end{table*}

A subsequent study was conducted to evaluate {the influence} of RCA and
WFS on the characteristics of AASC mixes. NCA was substituted with RCA
at regular intervals of 25\%, starting from 0\% up to 100\%. RSFA
was replaced with WFS at consistent intervals of 5\%, starting from 0\%
up to 30\% in the reference mix. Therefore, considering the percentage
replacement levels of RCA and WFS along the intervals of replacement,
10 mixes were obtained, {including} the \mbox{reference} mix. The final
optimized mix is considered with 50\% RCA and 20\% WFS only as
represented by the ID A-50-20 mix.  Tests were conducted to assess
workability, with the compaction factor value being used as a
measurement. At the end of the 28 days, experiments were conducted
based on density, water absorption, and compressive strength
measurements to identify the optimal content of RCA and WFS. The AASC
mixes were identified by the abbreviation A-0-0, where A stands for
alkali-activated slag-based concrete mixes. The subsequent numbers
indicate the proportion of replacement of RCA and WFS with NCA and
RSFA, respectively. The optimized AASC blends were then employed for
further investigations, including the assessment of 
split tensile strength (STS) and
flexural strength. These measured parameters were then collated to the
reference AASC mix. The test results of only the optimized mix are
presented in Figure~\ref{fig1}. 

\begin{figure}
\includegraphics{fig01}
{\vspace*{-2pt}}
\caption{\label{fig1}(a) Variation in compressive strength of 
optimized AASC mixes. (b) Variation in split tensile and flexural
strengths of  optimized AASC mixes.}
\end{figure}

The ingredients were batched and mixed properly to achieve a
homogeneous mixture, following the set mix proportion design. The
mixing procedure involved the addition of NCA and RCA into the mixer,
followed by RSFA, WFS, and GGBS. These materials are run as a dry mix
for a couple of minutes before adding the alkaline solution and then
in the end, a calculated quantity of water. The specimens employed in
this experiment are 10~cm $\times$ 10~cm ${\times}$ 10~cm cubes, cylinders with 10~cm 
diameter and 20~cm height, and 10~cm ${\times}$ 10~cm ${\times}$ 50~cm beams. The
specimens were demolded 24~h after the casting procedure.
After being subjected to three days of moist curing, the AASC specimens
were allowed to air-cure in a laboratory setting at a temperature
of 27 ${\pm}$ 30~{\textdegree}C and a relative humidity of 85 ${\pm}$ 10\%
until the testing date. After the curing process, the cubes and beams
were tested to assess their \mbox{compressive} strength and \mbox{four-point} flexural
strength, respectively, using the requirements stated in
IS~516: 2021~\cite{33}. Furthermore, the cylinders underwent testing to
assess their STS in accordance with the requirements
outlined in IS~5816-1999~\cite{34}. The test findings were documented
at 7 and 28 days of curing periods for the compressive strength,
STS, and flexural strength tests, with each data
point being the engineered mean of the test results of three individual
specimens. 

\section{Results and discussion}

Figure~\ref{fig1} presents illustrations of the test results. The
characteristics of hardened and fresh properties of AASC,
partially incorporated with RCA and WFS, are analyzed. The study
indicates that the optimum percentage replacement of RCA is 50\%
\mbox{(A-50-0),}  whereas for WFS it is 20\% (A-50-20).

The calculated values for the compaction factor value, density, water
absorption, and volume of permeable voids (VPV) for each mix are
as follows: 0.923 (slump: 65 ${\pm}$ 5~mm), 2580~kg/m$^{3}$,
3.12\%, and 8.03\% for the A-0-0 mix; 0.893 (slump: \mbox{52 ${\pm}$ 5~mm}), 
2496~kg/m$^{3}$, 5.13\%, and 9.21\% for the A-50-0 mix; 
and 0.866 (slump: 41 ${\pm}$ 5~mm), 2445~kg/m$^{3}$, 5.79\%, and
9.67\% for the A-50-20 mix, respectively. The results indicate that a
higher proportion of RCA replacement in the AASC mixes is linked to a
decrease in compaction factor value, lowered density, increased
water absorption, and VPV. Multiple factors might influence the test
results, such as the effects of the RCA's relatively high porous
structure~\cite{35}. However, the increased calcium ion concentration
in RCA affects the formation of the binder gel and the development of
additional interfacial zones~\cite{36}. The adherence of old porous
mortar accelerates the rate at which concrete hardens and reduces the
workability of AASC. RCA is closely correlated with the amount of
cement mortar that is attached~\cite{37}. Moreover, the {inclusion} of
WFS in concrete leads to greater porosity as a result of the
particles' irregular shape and rough texture, which creates
interconnected voids within the concrete matrix, in turn increasing the
volume of voids per volume of concrete and the water
absorption~\cite{38,39}. Moreover, when WFS is utilized as a
fractional replacement for RSFA in concrete, it introduces air voids
within the concrete particles, thereby lowering the specific density of
the material and consequently lowering the concrete's
density~\cite{18}.

Figure~\ref{fig1} displays the results of the strength test for the
optimized AASC systems at 7 and 28 days. The data indicate that an
increase in amounts of RCA and WFS causes a decrease in 
compressive strength of the AASC mixtures. The experimental results
provide significant insights into the mechanical performance of
concrete mixes at both early and mature stages. 

The compressive strength after 28 days of the \mbox{A-50-0} mix was 67~MPa,
which surpasses the target strength for  pavement concrete
requirements as per the IS~456: 2000~\cite{29} standard. {Therefore, this
particular concrete mixture can be considered the best option because
it maximizes the amount of waste products used while at the same time
maintaining the necessary strength. This is in line with the work of}
Kisan and Sangathan in 2005 {and the SP7 guidelines for replacing
aggregates in pavement applications.} The AASC mix A-50-20 exhibited a
compressive strength of 64~MPa at 28~days. This mix is tailored to
replace FA with 20\% of WFS. It surpasses the target strength necessary
for pavement grade concrete according to IS~456: 2000~\cite{29}. The
optimized mixes, AS-50-0 and A-50-20, were subjected to additional
mechanical strength testing, including STS and
flexural strength tests.

The A-50-0 mix demonstrated satisfactory mechanical properties over
time. The compressive strength showed a 13.07\% increase from 7 days to
28~days while STS exhibited a 2.72\% increase over
the same period. Additionally, the flexural strength displayed a 7.04\%
increase from 7 days to 28 days. However, at 28~days, there was a
13.94\% decrease in compressive strength, a 6.3\% decrease in
STS, and a 6.6\% decrease in flexural strength when
compared to the reference A-0-0 mix. The AS-50-20 mix \mbox{demonstrated}
acceptable mechanical properties over time. The compressive strength
showed a 4.57\% increase from 7 days to 28 days while 
STS exhibited a 9.67\% increase over the same
period. Additionally, the flexural strength displayed a 20.3\% increase
from 7 days to 28 days. However, at 28 days, there was a 4.66\%
decrement in compressive strength, a 4.44\% decrease in STS,
and a 0.56\% decrease in flexural strength when compared to
the optimized A-50-0 mix. The mean values obtained for the modulus of
elasticity (MOE) were analyzed. A decrement in MOE is observed upon
examination of the outcomes when RCA and WFS are incorporated. The
reference mix A-0-0 attained 49.7 GPa. The percentage decrease in MOE
for the A-50-20 mix when compared to the {A-50-0} mix was 4.66\% while
for the A-50-0 mix when  compared to the reference mix, it was 11.72\%.
The variation in these mechanical properties can be attributed to the
poorer qualities of RCA in comparison to NCA~\cite{41}, such as lower
impact and crushing strengths, and the occurrence of a new interfacial
transition zone (ITZ) in addition to the existing one. The diminished
compressive strength might be associated with the old adhered mortar on
the RCA, which renders the concrete more porous~\cite{42}. A potential
drawback of RCA is its heightened  propensity for irregularity in its
behavior when used in  composites~\cite{43}. The rough texture and
uneven shape of WFS particles, when incorporated into concrete, can
lead to increased porosity and interconnected voids within the
concrete~\cite{44}. {The RCA concrete typically
contains three distinct types of ITZ zones: one situated between the
newly applied cement mortar and aggregates; another located between the
old and freshly affixed cement mortar; and a third zone 
within the RCA between the aggregate and old adhered mortar~\cite{42}.
As a consequence of  rapid propagation of  innate microcracks in
the ITZ, which is subject to a large concentration of stress, the final
category is typically the weakest.} These microcracks, which are
concentrated under high stress, tend to spread quickly.
{The feeble nature of the contact between
aggregate and mortar contributes to the inferior strength of recycled
aggregate concrete when compared to natural aggregate\break
concrete~\cite{45,46}.}

{Moreover, WFS has irregular particle shapes and surface
characteristics that are dissimilar from those of typical fine
aggregates because of their unimodal particle size
distribution~\cite{18}. These \mbox{irregularities} contribute to a less
uniform stress distribution within the matrix as well as
discontinuities and stress concentration points that result in 
reduced bond strength between the matrix and the aggregate. This
results in areas within the concrete matrix that are weakened and more
likely to break under flexural loading, which in turn reduces bond
strength and compromises the material's resilience to bending forces.
Therefore, with an increment in the proportion of WFS, the mechanical
performance of AASC is diminished.}

\section{Statistical analysis}

The  compressive strength of concrete is a pivotal feature in
ascertaining the structural integrity and performance of any concrete
structure. This analysis delivers a comprehensive understanding of the
individual influences of several factors on the compressive strength of
concrete. The utilization of simple linear regression (SLR) provides an
understanding of the specific impact of each independent variable, such
as GGBS, NaOH, LSS, water, NCA, RCA, RSFA, and WFS. The objective is to
unravel the intricate relationships of these variables and to offer
crucial insights for optimizing AASC mixes. The current methodology
centers on the application of SLR for the meticulous examination of
each independent variable's influence on the compressive strength of
AASC. For each variable of interest, a dedicated SLR analysis is
conducted, treating compressive strength as the dependent variable and
the chosen factor as the sole predictor. This targeted approach enables 
a focused understanding of the linear relationship between a specific
factor and compressive strength. The SLR model is formulated as
follows: 
{\begin{equation}\label{eq1}
\mathrm{CS}=\upbeta_{0}+\upbeta_{1}X_{1}+{\upvarepsilon}          
\end{equation}}\unskip                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     
where

CS is the predicted compressive strength;

$\upbeta_{0}$ represents the intercept;

$\upbeta_{1}$ is the coefficient of  independent variables;

$X_{1}$ is the value of  independent variables;

${\upvarepsilon}$ is the error term capturing unexplained \mbox{variation.}

A large number of datasets were obtained from the experiments performed
on the AASC mixes. Initially, the trials were considered at very small
intervals, resulting in a total of 15 trial mixes. For each trial mix, 
10 test specimens were cast. There were a total of 150 specimens, based
on which a large set of data points were obtained.  However, since the
variations in strength at such small intervals were not significant, 
they were not incorporated into the experimental results of the research
paper but were incorporated into statistical modeling to improve
the accuracy of the  $R$-squared value. Data was collected from
experimental analysis, each varying the levels of the independent
variables under consideration, that is, NCA, RCA, RSFA, and WFS were
systematically varied to encompass a broad spectrum of scenarios. This
dataset serves as the foundation for the SLR analyses, enabling the
unique impact of each factor on the compressive strength of AASC to be
discerned. 

The outcomes from the SLR analysis reveal insightful patterns between
individual factors and the compressive strength of AASC. In the
\mbox{analysis} for NCA, the estimated intercept is 51.280958, suggesting a
baseline compressive strength when NCA is zero. The coefficient for NCA
is 0.024950, indicating that for each unit increment in NCA, the
compressive strength is anticipated to rise by 0.024950~units. Both
coefficients are highly statistically significant, affirming the
strength and significance of the linear relationship. For RCA, the
estimated intercept of 79.802711 represents the expected compressive
strength when the RCA is zero. The coefficient for RCA is
${-}$0.026315, signifying a decrease in compressive strength by
${-}$0.026315 units for each unit increase in the RCA. Similar to NCA,
RCA is highly significant, underlining the robustness of the model.
Interpreting the coefficients for FA and WFS, the estimated intercepts
of 32.84472 and 70.37998, respectively, represent the baseline
compressive strength. The coefficients for FA of 0.09684 and WFS of
${-}$0.13476 indicate the expected change in compressive strength for
each unit increase in the respective aggregates. All these coefficients
are statistically significant and provide meaningful understanding
of the associations between compressive strength and the independent
variables.
\looseness=-1

Additionally, substantial $R$-squared values for both NCA and RCA, of
0.8633 and 0.8632, respectively, emphasize the models' ability to
explain a considerable proportion of the variability in compressive
strength. This signifies a robust fit of the models to the data and
provides confidence in their predictive capabilities. The
interpretation of \mbox{coefficients} for RSFA and WFS similarly highlights
their impact on compressive strength, with the $R$-squared values of
0.4984 and 0.4985 indicating a moderate but still significant
explanatory power. In summary, the combination of statistically
significant coefficients and high $R$-squared values underscores the
reliability and explanatory strength of  SLR models in capturing the
relationships between individual factors and compressive strength in
AASC mixes. Furthermore, it was observed that variables, namely GGBS, NaOH,
LSS, and water, did not exhibit any impact on the dependent variable
compressive strength due to their constant values across all\unskip\break
observations.


The observed patterns and statistical significance revealed in the SLR
analysis provide a foundation for formulating predictive equations that
capture relations among individual factors and the compressive strength
of concrete. When expressing these relationships in the context of the
obtained coefficients, the equations take the following form:
{{\begin{eqnarray}
&\displaystyle\hspace*{-5pc}
\text{For natural coarse aggregates (NCAs): compressive}
\hspace*{-5pc}\nonumber\\&\displaystyle 
\text{strength} = 51.280958+0.024950\times \text{NCA}+\upvarepsilon \label{eq2} \Seqnsplit
&\displaystyle\hspace*{-5pc} 
\text{For recycled concrete aggregates (RCAs):}
\hspace*{-5pc} \nonumber\\&\displaystyle 
\text{compressive strength}=79.802711- 0.026315
\nonumber\\&\displaystyle \quad \quad 
\times~\mathrm{RCA}+\upvarepsilon \label{eq3}\Seqnsplit
&\displaystyle\hspace*{-5pc} 
\text{For river sand fine aggregates (RSFAs): compressive}
\hspace*{-5pc} \nonumber\\&\displaystyle 
\text{strength}=32.84472+0.09684 \times \mathrm{FA}+\upvarepsilon \label{eq4}\Seqnsplit
&\displaystyle\hspace*{-5pc} 
\text{For waste foundry sand (WFS): compressive}
\hspace*{-5pc}\nonumber\\&\displaystyle 
\text{strength} =70.37998-0.13476 \times \mathrm{WFS}+\upvarepsilon 
\label{eq5}
\end{eqnarray}}\unskip}
These equations serve as predictive models, offering insights into how
changes in each independent variable contribute to variations in the 
compressive strength of concrete. In each
equation, ${\upbeta}_0$
and ${\upbeta}_{1}$  quantify the magnitude and direction of the impact, providing
a practical tool for informed decision-making with regard to concrete
mix design and construction practices.%{\vspace*{-3pt}}

\begin{figure*}[t!]
\includegraphics{fig02}
\caption{\label{fig2}Residuals versus fitted plot.}
{\vspace*{-7pt}}
\end{figure*}


\subsection{Assessing model assumptions: residual plots}
%{\vspace*{-3pt}}

To determine the precision of the analysis, a number of residual plots
were employed to inspect the model assumptions. Residuals versus
leverage, residuals versus fitted, Q--Q residuals, and scale--location
are all presented in these graphs.


\subsubsection{Residuals versus  fitted plot}


Figure~\ref{fig2} displays the residuals versus fitted plot. This graph is
used to verify the linearity and homoscedasticity assumptions of the
regression model~\cite{47}. The residuals are shown on the vertical
axis while the fitted values of the dependent variable, compressive
strength, are represented on the horizontal axis. The plot shows the
residuals dispersed randomly along the horizontal axis (zero line).
This randomness implies that there is no apparent pattern or
systematic structure in the residuals, in turn indicating that the
relationship between the independent variables (e.g., NCA, RCA, RSFA,
WFS) and the dependent variable (compressive strength) is appropriately
linear. This satisfies the linearity assumption and confirms that a
linear regression model is suitable for this dataset. The residuals do
not exhibit any clear forms or trends, and instead are uniformly
distributed over the range of fitted values. This uniformity in the spread
of residuals indicates that the variance of the residuals is constant
across different levels of predicted compressive strength. This
observation satisfies the homoscedasticity assumption, suggesting that
the model's predictions are reliable and that the estimated standard
errors of the coefficients are valid. 

\begin{figure*}[t!]
\includegraphics{fig03}
\caption{\label{fig3}Q--Q residual plot.}
{\vspace*{2pt}}
\end{figure*}


\subsubsection{Q--Q residuals}

The quantiles of the residuals and the quantiles of a hypothetical
normal distribution are compared using the Q--Q plot. This helps to
evaluate the normality assumption of the residual~\cite{48}. The
residuals are assumed to follow a normal distribution if the points are
nearly aligned with the 45{\textdegree} reference line. In Figure~\ref{fig3},
the 45{\textdegree} reference line is closely followed by the Q--Q plot
points, indicating that residuals are roughly normally distributed.
{The close adherence to the 45{\textdegree} line indicates that the residuals
do not deviate significantly from normality, confirming the robustness
of the regression model and the reliability of the statistical
inferences drawn from it. Normal residuals ensure that the $t$-tests and
$F$-tests used to determine the significance of the regression
coefficients are valid. Non-normal residuals can lead to incorrect
$p$-values, affecting the interpretation of which predictors are
significant.}




\begin{figure*}[t!]
\includegraphics{fig04}
\caption{\label{fig4}Scale--location plot.}
{\vspace*{2pt}}
\end{figure*}


\subsubsection{Scale--location plot}

The scale--location diagram in Figure~\ref{fig4} is utilized to confirm
the regression model's homoscedasticity assumption. The plot shows 
the fitted values on the horizontal axis and the square root of the
standardized residuals on the vertical axis. Plot points are dispersed
at random around a horizontal line. The random scatter shows that the
residuals' distribution across various fitted value levels lacks any
\mbox{discernible} structure. The spread of the residuals does not exhibit any
obvious pattern or systematic structure, which implies that the
variance of the residuals is constant. This shows that the model's
predictions are consistently correct over the whole range of 
observed data, thereby supporting the homoscedasticity assumption.
There is no funnel shape or \mbox{fanning} out of the plot. The residuals'
continuous variance is supported by this fact. Reliable hypothesis
tests and confidence intervals are produced when the residuals are
uniformly distributed across all fitted value levels, ensuring that the
regression coefficients are effectively computed and the standard
errors of the coefficients are valid. This result strengthens the
\mbox{robustness} of the model and its findings by validating the regression
coefficients, standard errors, and related inferential statistics.


\begin{figure*}
\includegraphics{fig05}
\caption{\label{fig5}Residuals versus leverage plot.}
{\vspace*{-8pt}}
\end{figure*}



\subsubsection{Residuals versus leverage plot}

The residuals versus leverage plot in Figure~\ref{fig5} was examined to determine
which data points have high leverage and also to evaluate how they
affect the regression model. The plot displays several high-leverage
data points. These points have a large impact on the fitted values
because their predictor variable values deviate greatly from the mean.
The standardized residuals, which represent each residual's standard
deviation from the mean, are displayed on the plot's vertical axis.
Potential outliers are indicated by points with significant
standardized residuals. This study's residuals versus leverage graph
offers important insights into how different data items affect the
regression model. The graph aids in detecting significant points that
may disproportionately impact the model's coefficients and overall fit
by highlighting high-leverage points and evaluating their standardized
residuals. The overall validity and reliability of the regression
analysis are supported by the study, which makes sure that a small
number of data points do not have an excessive impact on the model's
coefficients.

\subsection{Multiple linear regression}

Continuing the analysis from examining the individual effects of SLR,
advancements were made in the investigation with the use of multiple
linear regression (MLR) to enhance the understanding of how these
factors collectively shape compressive strength. The results revealed
that the regression model performs well as evidenced by the high
\mbox{$R$-squared} value of 0.8044, which shows that nearly 80.44\% of variance
in the dependent variable is explained by the model. This is in
agreement with previous studies~\cite{49,50}. The $F$-statistics also
confirmed the overall importance of the model. The results of the MLR
analysis have furnished a robust regression equation---represented by
(Equation (\ref{eq6}))---for predicting the compressive strength of the developed
AASC composites.
{\begin{eqnarray}\label{eq6}
  \mathrm{CS} = 717.161 - 15.862\times \mathrm{RSFA} - 22.153 \times \mathrm{WFS}
\end{eqnarray}}\unskip
The regression model yielded an intercept of 717.2~MPa, 
which although mathematically valid does not correspond to any
practical concrete mix design. This value is a result of the regression
equation's formulation and reflects the predicted compressive strength
when RSFA and WFS are set to zero, a scenario that does not occur in
real-world applications. Future models might explore centering or
rescaling the data to provide more physically interpretable intercept
values.%{\vspace*{-10pt}}


\begin{figure}
\includegraphics{fig06}
\caption{\label{fig6}Scatter plot of predicted values versus actual values.}
{\vspace*{-10pt}}
\end{figure}


\subsection{Scatter plot of predicted values versus actual
values}
%{\vspace*{-5pt}}

The predicted values versus actual values scatter plot serves as a visual
tool for gauging the alignment between the regression model's
predictions and the actual data~\cite{51}. The plot highlights
assessing the proximity of data points to a diagonal reference line.  
This reference line signifies an ideal scenario where the model's
predictions and actual values perfectly coincide. When data points
cluster closely around this line, it signifies the model's precision in
making predictions~\cite{52}.

In Figure~\ref{fig6}, the predicted and actual compressive strength
values derived from the MLR model are compared. The objective is to
evaluate the prediction accuracy of the MLR model in determining the
compressive strength of the AASC mixtures. The scatter plot illustrates
the correlation between the expected and actual compressive strength
values. The graph demonstrates the degree of alignment between the
model's predictions and the experimental data. Data points that tightly
cluster around the diagonal line in the figure signify precise
predictions by the model. Points that considerably diverge from the
line indicate disparities between the expected and actual
values. The research indicates that most data points are
situated close to the diagonal line, suggesting that the model has a
high degree of prediction accuracy. The absence of observable trends or
systematic departures indicates that the model is resilient and
accurate for forecasting compressive strength in the tested AASC
blends. The concordance between the predicted and actual values
is essential as it validates the model's ability to generalize beyond
the training dataset and reliably forecast the compressive strength of
future AASC mixtures. The stochastic dispersion of errors around the
diagonal line substantiates the assertion that the model exhibits
\mbox{consistent} performance across various mix patterns. Validation of the
model using the scatter plot indicates that the MLR model is
appropriate for forecasting compressive strength results and may
effectively optimize mix designs without the need for significant
physical testing. This method conserves materials and enhances resource
efficiency and sustainability in concrete manufacturing.

{This study employed both SLR and MLR to examine the variables that
have an impact on the compressive strength of AASC. Significant
coefficients and the direction of their impacts were revealed using
SLR, which shed light on the specific effects of each variable. The
scope was constrained to interactions \mbox{involving} a single variable. In
contrast, several predictors were included in MLR, which also provided
higher predictive power, adjustments for confounding variables, and
interaction effects. It} may lead to  overfitting,
which {is more complicated and which} may also
{require careful interpretation.} The analysis {also depends
on multiple assumptions. However, with a high $R$-squared value of
0.8044, the MLR model was able to explain a significant amount of the
variance in compressive strength. The study concluded that
although SLR presented insightful information about the distinct impacts
of each variable, MLR provided a more thorough and reliable analysis. This
is due to the fact that it considered the combined impact of several
predictors on the compressive strength of AASC. This method aids in the
creation of concrete mixtures that are optimal for environmentally
friendly pavement materials. The developed concrete mixes utilize a
major amount of waste materials such as RCA and WFS, which are used as
replacement for natural aggregates. Aggregates generally occupy 70 to
80\% of the volume of concrete. \mbox{Replacing} natural aggregates with waste
materials helps in conserving natural aggregates. Furthermore, the MLR
model has better predictive accuracy in determining the compressive
strength of AASC mixes.} The analysis {helps in optimizing
the mix design without the need for trial mixes, which in turn further
\mbox{reduces} the use of materials. Therefore, the considered methodology
contributes to the} creation of  {optimal AASC}
paving {mixes that are ecologically viable.}

\section{Conclusion and future scope}

This study emphasizes the significant advancements that can be achieved
via the incorporation of RCA and WFS into AASC mixes. The outcomes of the
research work indicate the potential use of these combinations in
highway pavement infrastructure. Moreover, the use of RCA and WFS
aligns with sustainable construction practices and the circular
economy, offering the dual benefit of preserving the environment
by enabling the creation of eco-friendly high-performing construction
materials. The following points show the research's main findings and
key conclusions:
\begin{itemize}
\item The inclusion of RCA and WFS in AASC resulted in reduced
workability and increased water absorption when compared to the
reference mix. The A-50-0 mix showed a compaction factor value of
0.893, VPV of  9.21\%, and water absorption of 5.13\%. The A-50-20 mix
showed a compaction factor value of 0.866, VPV of  9.67\%, and water
absorption of 5.79\%.
\item {The optimal replacement percentages for RCA and WFS in AASC
mixes were determined to be 50\% and 20\%, respectively. These mixes
showed optimal compressive strengths of 67~MPa for the A-50-0 mix and
64~MPa for the A-50-20 mix after 28 days, with both exceeding the
target strength for pavement grade concrete.}
\item The A-50-0 mix and A-50-20 mix exhibited a flexural strength of
7.4~MPa and 7.3~MPa, respectively, which is greater than the minimum
requirement of flexural strength for pavement applications, which is
4.5~MPa as per IRC.
\item The A-50-0 mix exhibited a 13.07\% increase in compressive
strength, a 2.72\% increase in STS, and a
7.04\% increase in flexural strength from 7 to 28 days. The
A-50-20 mix showed a 4.57\% increase in compressive strength, a 9.67\%
increase in STS, and a 20.3\% increase in flexural strength over the same
period.
\item The natural aggregates in AASC mixes are reduced by
nearly 70\%, with them being replaced by C\&D wastes (up to
50\%) and WFS (up to 20\%). This contributes to
sustainability and a circular economy. Moreover, using industrial
byproducts (viz., GGBS) as a replacement for conventional OPC binders
could additionally reduce CO$_{2}$ emissions and improve  ecological
balance. 
\item The SLR analysis revealed significant negative impacts of RCA and
WFS on compressive strength, with coefficients of ${-}$0.026315 and
${-}$0.13476, respectively. 
\item The MLR model performed well and exhibited a high $R$-squared value
of 0.8044, indicating the robust predictive capability for compressive
strength outcomes. This model will help optimize the mix design,
thereby reducing the number of trials to obtain the optimized content,
and also improve resource efficiency. 
\end{itemize}
Overall, this investigation provides valuable information on the
influence of RCA and WFS on slag-based AAC mixes. Based on the
findings, the formulation of an AASC mix will effectively provide
waste-derived concrete with equal or better properties when compared to
OPCC mixes. This will enable buildings to adopt greener production
practices and conserve resources. It is suggested that further research
should be conducted to examine the characteristics of sustainable
concretes that were generated as part of this work, namely their
durability and time-dependent behavior (such as contraction and
creep). 


\section*{List of abbreviations and symbols}

\mbox{}
\begin{inftab}
\begin{tabular}{@{}ll@{}}
\tbody
${\upbeta}_{0}$ &  Intercept\\
${\upbeta}_{1}$ & Coefficient of independent variables\\
$X_{1}$ & Value of independent variables\\
${\upvarepsilon}$ & error term capturing unexplained variation\\
{AAC} & Alkali-activated concrete\\
{AAS} & Alkaline activator solution \\
{AASC} &  Alkali-activated slag concrete \\
\inftabbreak
{CS} & Compressive strength\\
{GGBS} & Ground granulated blast furnace slag\\
{LSS} & Liquid sodium silicate\\
{MLR} & Multiple linear regression\\
{NCA} &  Natural coarse aggregate \\
{OPCC} &  Ordinary Portland cement concrete\\
{RCA}  & Recycled concrete aggregate\\
{RSFA} &  River sand fine aggregate\\
{SLR} & Simple linear regression\\
{VPV} & Volume of permeable voids \\
{WFS} & Waste foundry sand
\end{tabular}
\end{inftab}

\section*{Declaration of interests} 

The authors do not work for, advise, own shares in, or receive funds
from any organization that could benefit from this article, and have
declared no affiliations other than their research organizations.

\section*{Acknowledgments} 

The Civil Engineering Department's laboratory at the NMAM Institute of
Technology, Nitte, was the site of all significant work. The Nitte
Education Trust provided financial assistance through the  NMAMIT Research
Fund for Civil Engineering {2021-22} and  the NURF Research Grant
(Sanction number N/RG/NUFR1/NMAMIT/2022/09 dated 28.12.2022).
The authors wish to express their heartfelt thanks to the Dean (R\&D),
HoD (Civil), and Principal of NMAMIT-Nitte, Directorate of Nitte (DU),
Nitte Education Trust (R), and NITTE (Deemed to be University) for
providing timely financial and other essential assistance for this
research. The authors would also like to thank the undergraduate
students of B.E. (Civil) for their extremely valuable help in 
conducting the laboratory experimental investigations.

\CDRGrant[NMAMIT]{N/RG/NUFR1/NMAMIT/2022/09}

\back{}  

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\refinput{crchim20240088-reference.tex}

\end{document}
