Startseite Modeling the strength parameters of agro waste-derived geopolymer concrete using advanced machine intelligence techniques
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Modeling the strength parameters of agro waste-derived geopolymer concrete using advanced machine intelligence techniques

  • Ahmed A. Alawi Al-Naghi EMAIL logo , Muhammad Nasir Amin EMAIL logo , Suleman Ayub Khan EMAIL logo und Muhammad Tahir Qadir
Veröffentlicht/Copyright: 25. Juni 2024
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REVIEWS ON ADVANCED MATERIALS SCIENCE
Aus der Zeitschrift REVIEWS ON ADVANCED MATERIALS SCIENCE Band 63 Heft 1

Abstract

The mechanical strength of geopolymer concrete incorporating corncob ash and slag (SCA-GPC) was estimated by means of three distinct AI methods: a support vector machine (SVM), two ensemble methods called bagging regressor (BR), and random forest regressor (RFR). The developed models were validated using statistical tests, absolute error assessment, and the coefficient of determination (R 2). The importance of various modeling factors was determined by means of interaction diagrams. When estimating the flexural strength and compressive strength of SCA-GPC, R 2 values of over 0.85 were measured between the actual and predicted findings using both individual and ensemble AI models. Statistical testing and k-fold analysis for error evaluation revealed that the RFR model outperformed the SVM and BR models in terms of accuracy. As demonstrated by the interaction graphs, the mechanical characteristics of SCA-GPC were found to be extremely responsive to the mix proportions of ground granulated blast furnace slag, fine aggregate, and corncob ash. This was the case for all three components. This study demonstrated that highly precise estimations of mechanical properties for SCA-GPC can be made using ensemble AI techniques. Improvements in geopolymer concrete performance can be achieved by the implementation of such practices.

Keywords: artificial intelligence; geopolymer concrete; mechanical properties

Abbreviations

AI

artificial intelligence

BR

bagging regressor

CCA

corncob ash

CS

compressive strength

FA

fine aggregate

FAS

fly ash

FS

flexural strength

GPC

geopolymer concrete

GGBFS

ground granulated blast furnace slag

MAE

mean absolute error

MK

metakaolin

MAPE

mean absolute percentage error

ML

machine learning

PC

Portland cement

RFR

random forest regressor

RHA

rice husk ash

RM

red mud

RMSE

root mean squared error

SCMs

supplementary cementitious materials

SF

silica fume

SVM

support vector machine

SCA-GPC

concrete incorporating corncob ash and slag

1 Introduction

The long history of concrete as an essential building material has highlighted the environmental impact of concrete over years [1]. With the global demand for cement and concrete expected to triple by 2,050, carbon emissions are projected to increase, and biodiversity is likely to decline at a faster rate than previously anticipated [2]. Due to its large energy and carbon footprint, Portland cement (PC) has been the target of researchers seeking to create alternative binders [2]. In the manufacturing process of PC, crucial for concrete binding, approximately 1.80 metric tons of raw materials are utilized, resulting in the emission of 0.8 metric tons of CO2 [3]. Thus, cement output must be mitigated immediately to reduce environmental change [2]. One methodical and technical approach to ensuring materials’ long-term viability is to recycle them into fresh construction materials from agricultural and industrial waste [4]. There are societal, economic, and environmental benefits to producing supplementary cementitious materials from recycled agricultural and industrial waste [5,6]. Using recycled materials in place of PC has been proven to be an efficient, affordable, and long-term strategy for reducing one’s carbon footprint [7,8,9].

Sustainable concrete, also known as geopolymer concrete (GPC), replaces the PC with recycled agro-industrial resources, making a cementitious binder redundant [10,11]. The utilization of alkali hydroxide and alkali silicate appears to be a component of the activation process for raw materials based on the aluminosilicate structure [12]. There is a wide variety of reprocessed agronomic and manufacturing materials that have potential as precursors, including fly ash (FAS), red mud (RM), geopolymers (alumino-silicates), rice husk ash (RHA), ground granulated blast furnace slag (GGBFS), silica fume (SF), and metakaolin (MK) [13,14,15,16,17,18]. GGBFS in producing GPC presents minimal environmental repercussions alongside favorable cost-effectiveness, heightened rigidity, and exceptional resistance to chemical degradation. Moreover, it holds promise as a key ingredient in eco-friendly and economically viable concrete formulations [19,20,21,22,23]. The corncob ash (CCA) component, instead, is novel. More traditional pozzolanic components, including FAS and RHA, can be replaced or supplemented with CCA due to its elevated level of silica. The usage of on-the-spot heated GPC is associated with a number of problems; thus, researchers are considering creating this green concrete at room temperature instead. It is also critical to know that there are other criteria for judging performance outside of reaching strength norms. Evaluating a structure’s resistance to environmental and other pressures is essential for accurately estimating its lifespan. GPC is a prospective concrete solution that could be used in ecologically sensitive places because of its improved mechanical capabilities and improved resilience [31]. All of the aforementioned factors point to GPC’s unique chemical makeup as the source of the material’s exceptional mechanical capabilities and endurance [18,24,25]. Using nano-silica and reused plastic particles has allowed GPC to perform better in recent years [26,27,28]. Waste-based GPC has numerous advantages, as can be seen in Figure 1.

Figure 1 Advantages of waste-derived GPC in construction [29].
Figure 1

Advantages of waste-derived GPC in construction [29].

Experts in the fields of science, engineering, research, and computer programming are starting to notice that AI is having a major influence on how new products are developed and enhanced. Problems exist in the engineering industry, and there is a high demand for individuals who can find ways to integrate AI into their jobs. Nevertheless, there are still certain downsides and performance issues with AI-based systems, even though the future seems bright. Artificial intelligence programs have formidable obstacles when it comes to tasks that people typically take for granted, such as object identification and natural conversation understanding [30]. This poses a challenge for modern AI in creating appropriate alternatives for training computer perception. AI systems have utilized machine learning (ML) to tackle these issues [30,31]. ML algorithms allow computers to gain the necessary expertise for autonomous action by analyzing a sufficiently large dataset [32,33]. Getting back to the qualities that make the most explicit data is the first step before putting the plan into action. The term “feature extraction” is now used to describe this procedure. Then, ML is used to train sample data, attributes, and pattern separation instructions [30,34,35]. Modern civil engineering research relies on statistical methods and AI to address ever-increasingly complicated issues. Estimating concrete’s compressive strength (CS) is a typical use case for these techniques in civil engineering [15,36]. The ability to forecast self-compacting concrete’s slump and impact strength [37], varied column axial bearing [38], and shear behavior of beams in a structure [39], as well as the forecasting of chloride contamination [40] are some of the harder challenges solved utilizing these strategies. These estimates assist in decreasing the number of test configurations for future investigations, shortening their duration and expense. ML approaches such as artificial neural networks, gradient boosting (GB), expression trees (ETs), Gaussian process regression (GPRs), decision trees (DTs), support vector machines (SVMs), and extreme gradient boosting (XGB) may estimate concrete strength [41,42,43]. The mechanical properties of GPC were better predicted by the individual and ensemble models than by any of the other models.

This study used experimental data and AI algorithms to forecast the mechanical properties of slag and corncob ash-centered geo-polymer concrete (SCA-GPC), a GPC composed of slag and CCA. One standalone ML method and two ensemble ML processes were employed in the study to accomplish its goals. One method to evaluate the models’ accuracy involved comparing the predicted and actual outcomes, using statistical tests, and performing K-fold analysis. Carrying out experiments is difficult because of the lengthy and complex procedures involved in collecting materials, casting samples, curing them to increase strength, and evaluating them. Modern modeling techniques like ML can significantly aid the construction industry by overcoming these challenges. Conventional testing methods struggle to assess the overall impact of all parameters on SCA-GPC strength. To identify the most important variables, this study employed interaction graphs. Data necessary for ML approaches can be gathered from existing research. The dataset now has a plethora of potential uses, such as in ML algorithms, impact studies, and material property estimations. Utilizing an experimental dataset, this article validates the efficacy of ensemble ML algorithms in predicting SCA-GPC strength. The study’s findings might pave the way for greener construction methods, which would enhance GPC’s value to the business.

2 Study methods

2.1 Collecting and evaluating data

The research employed ML models, including SVM, bagging regressor (BR), and random forest regressor (RFR), to predict the CS and FS of SCA-GPC. The experimental investigation yielded a dataset comprising 260 data points [44]. According to the eight input variables (NaOH pellets [SHP], molar concentration [MC), GGBFS, curing day [CD], fine aggregate [FA], water [W], CCA, and concrete grade [CG]), the CS and FS of SCA-GPC were predicted. The data were collected and organized using data preparation. Data preparation for mining data is the standard technique for knowledge discovery from data to minimize major obstacles. Data preparation involves eliminating noise and unnecessary details from the dataset. Descriptive statistics in Table 1 provide a comprehensive summary of key characteristics within the refined dataset, offering valuable insights into its central tendencies, variability, and distribution. These statistics serve as fundamental tools for understanding the dataset’s structure, facilitating informed decision-making and hypothesis testing in subsequent analyses. One common way to find parameter dependencies is to use Pearson’s correlation coefficient (r) [45]. Two Figure 2(a) for CS and 2(b) for FS, show the results of the association map plot for the attributes. The r-squared test is useful for demonstrating parameter dependency and multicollinearity [46]. Within the range of −1 to +1, a strong negative relationship is provided by −1, a strong positive link by +1, and no correlation at all by 0 for the r-value [47]. This correlation between the input variables and the output (CS and FS) is displayed in the bottom row of Pearson’s array.

Table 1

Variable descriptions using statistics (parameters similar to [48,49,50])

Parameters Mean Standard error Median Mode Standard deviation Sample variance Kurtosis Skewness Minimum Maximum
GGBFS (kg·m−3) 218.65 9.54 228.00 0.00 153.90 23685.60 −1.12 0.12 0.00 488.00
CCA (kg·m−3) 215.24 9.48 195.00 0.00 152.78 23341.70 −1.12 0.13 0.00 488.00
FA (kg·m−3) 818.12 3.53 841.00 841.00 56.90 3237.34 −1.41 −0.14 728.00 899.00
W (kg·m−3) 35.22 0.13 35.16 37.86 2.15 4.63 −1.53 0.04 32.64 37.86
SHP (kg·m−3) 23.38 0.13 23.44 20.74 2.15 4.63 −1.53 −0.04 20.74 25.96
CD (days) 45.25 1.93 42.00 7.00 31.20 973.43 −1.34 0.25 7.00 90.00
MC (M) 14.00 0.10 14.00 12.00 1.65 2.72 −1.53 0.00 12.00 16.00
CG (MPa) 34.96 0.31 30.00 30.00 5.01 25.10 −2.02 0.02 30.00 40.00
CS (MPa) 35.90 0.76 36.04 29.04 12.19 148.64 −0.74 0.17 10.67 64.09
FS (MPa) 5.36 0.06 5.47 5.00 1.01 1.03 −0.58 −0.24 2.81 7.45
Figure 2 Parameter correlation heat map: (a) CS and (b) FS.
Figure 2

Parameter correlation heat map: (a) CS and (b) FS.

2.2 ML modeling

Laboratory studies were used to assess the mechanical properties of SCA-GPC. While CS and FS need ten inputs, the prediction models were built using just eight of the variable inputs. The SCA-GPC’s CS and FS were predicted using advanced ML algorithms that included SVM, BR, and RFR. The study achieved its goals by using Python code in the Spyder environment of Anaconda Navigator (version 5.1.5). Typically, ML algorithms are utilized to compare outputs with inputs throughout the process. Researchers allocated 70% of the data for training ML models, reserving the remaining 30% for testing. Additionally, the R 2 value of the predicted outcome served as an indicator of the model’s reliability. A low R 2 score signifies a significant deviation between predicted and actual outcomes, highlighting substantial discrepancies in the model’s predictive accuracy. This metric serves as a crucial indicator of the model’s efficacy in capturing the variance within the dataset, with lower scores suggesting a less accurate representation of the observed data [51]. The correctness of the model was validated by a number of analyses, which included statistical examinations and evaluations of errors. A simple graphical representation of an event model is shown in Figure 3, which may be seen hereunder.

Figure 3 ML process outline.
Figure 3

ML process outline.

2.2.1 Support vector machine

For supervised ML tasks like data regression and classification evaluation, there is the support vector machine (or SVM). SVM classification systems employ diverse categorization strategies aimed at maximizing the separation between different classes to the greatest extent possible within practical constraints. This approach ensures robust classification performance by effectively delineating boundaries between distinct categories in the feature space. For the purpose of depicting the samples, this method makes use of points on a plane or line. The additional instances are arranged in a manner that corresponds to their orientation along the vector, as shown in Figure 4. Figure 5 delineates the systematic approach for implementing SVM models, meticulously designed to deliver a holistic assessment of material strength considering multiple influential factors. This framework empowers users to fine-tune SVM model parameters using sophisticated optimization techniques, thereby augmenting its predictive precision and utility in material strength analysis.

Figure 4 Model mapping SVM [52].
Figure 4

Model mapping SVM [52].

Figure 5 Process flow diagram for SVM [53].
Figure 5

Process flow diagram for SVM [53].

2.2.2 BR

The BE technique is illustrated in Figure 6, a simplified flow diagram. A comparable ensemble method is the most effective way to describe the steps required to augment the forecast model with additional training data sets. Asymmetric sample statistics are substituted for the original set of statistics. With each new batch of training samples, it is possible to find the same or similar results. Once the bagging procedure is completed, each component has an equal chance of being included in the updated dataset. The overall projection quality remains unaffected by the size of the training dataset. The divergence could be significantly reduced if the target output is more accurately approximated. For each run of the simulations, this ensemble takes the average prediction and uses it. The median prediction from several simulations is utilized in regression [54]. Twenty separate models are used separately to optimize the SVM-based bagging technique and find its best output.

Figure 6 Bagging ensemble model: a schematic explanation [55].
Figure 6

Bagging ensemble model: a schematic explanation [55].

2.2.3 Random forest

Random split selection, in conjunction with bagged decision trees, allows for the attainment of RFR [56]. The assembly and operation of the RFR model are depicted in a simplified diagram, as shown in Figure 7. There is a random selection process for both the training data and the input parameters required to create each branch split in the forest’s trees [57]. The natural diversity of the tree is enhanced by the presence of this variable. When it comes to the forest, only completely developed binary trees are there. In the realm of universal regression techniques, the RFR method has been demonstrated to be effective. When the amount of variables exceeds the maximum number of possible clarifications, it has been demonstrated that combining the results of a large number of decision trees that were chosen at random yields more accurate results. It is useful for both planned and unplanned learning activities because the significance of its indications shifts significantly throughout the course of time [56].

Figure 7 Random forest modeling and structure [58].
Figure 7

Random forest modeling and structure [58].

2.3 Model’s validation

For the purpose of ensuring that the ML models had an accurate representation of the data, a number of distinct mathematical techniques and k-fold procedures were developed and implemented. The k-fold technique is frequently applied for the purpose of determining whether or not a procedure is considered to be effective. This strategy involves arbitrarily dividing the data set into ten different categories [59]. As depicted in Figure 8, ML simulations are trained using nine distinct sets, with only one reserved for validation. ML methods exhibit good performance in scenarios with low error and high R 2. Additionally, to yield positive outcomes, the treatment needs to be conducted a total of ten times. The precision of the model, which was previously quite excellent, is greatly improved by this procedure. Various ML approaches were also correlated by employing statistical error evaluation metrics like mean absolute percentage error, mean absolute error (MAE), and root mean squared error. Eqs. (1) and (3), obtained from previous works, were employed to statistically test the precision of the ML methods’ estimates [60,61]

(1) RMSE = ∑ ( P i − T i ) 2 n ,

(2) MAE = 1 n ∑ i = 1 n | P i − Ti | ,

(3) MAPE = 100 % n ∑ i = 1 n | P i − Ti | T i .

Figure 8 K-method operational procedure [52].
Figure 8

K-method operational procedure [52].

In this context, n stands for the total number of observations, P i refers to the anticipated results, and T i indicates the actual measured values.

2.4 Input parameter interaction analysis

Python and Jupyter Notebook 6.4.12 simulated input feature interaction. Matplotlib was used to create interaction graphs. Jupyter Notebook enables users to write and share interactive code, graphs, equations, and text documents online [62]. Among the numerous uses for this platform are filtering of data and alterations, mathematical simulation, arithmetical modeling, and data conception, among others [63]. For visualizing two-dimensional data arrays, Matplotlib is one of the Python libraries that is used the most frequently [64]. Initiating the plot() method, preparing the data, and establishing the required dependencies are all prerequisites to starting. In order to display a plot, the show() method must be used. Matplotlib is a Python library that uses NumPy, an extension for Python used in numerical mathematics [65]. It includes a variety of graphs: line, bar, scatter, and histogram. The study utilized scatter plots to visually depict the relationship between the input variables, a method commonly employed in numerous comparable investigations [66,67].

3 Results and analysis

3.1 CS models

3.1.1 CS-SVM model

Figure 9 shows the results of estimating the CS of SCA-GPC using the SVM model. Figure 9(a) graphically illustrates the agreement between the anticipated and observed CS. The dotted lines indicate a 20% deviation from the solid black line, which represents a perfect match with the data. Predictions for CS from the SVM model and the measured values were very close. To effectively determine the CS of SCA-GPC, the SVM technique was utilized. The results displayed a notable level of accuracy, with 83% of predictions falling within the 20% criterion and an R 2 value of 0.8745. Figure 9(b) shows the range of differences (errors) between experimental and predicted values using the SVM method is illustrated. The erroneous values exhibited a standard deviation ranging from 0.09 to 9.50 MPa, with an average of approximately 3.42 MPa. Specifically, there were 17 values below 1 MPa, 20 falling between 1 and 3 MPa, and 41 exceeding 3 MPa. Despite the scattered data, the error distribution suggests that the SVM model can effectively predict the CS of SCA-GPC.

Figure 9 (a) Connection between experimental and predicted CS in the CS-SVM model and (b) scattering of errors and predicted CS.
Figure 9

(a) Connection between experimental and predicted CS in the CS-SVM model and (b) scattering of errors and predicted CS.

3.1.2 CS-BR model

Figure 10 presents the outcomes of estimating the CS of SCA-GPC using the BR model. Figure 10(a) shows a distinct correlation between observed and predicted CS values, where the solid black line represents an ideal fit, and the dotted lines indicate a deviation of up to 20%. The experimental CS values closely align with the predictions from the BR model. The BR technique demonstrates remarkable performance, achieving an R 2 value of 0.9365 and with 97% of predictions falling within the 20% deviation threshold, indicating significant accuracy enhancement. Figure 10(b) shows the range of errors between experimental and predicted values using the BR technique, with incorrect results exhibiting a standard deviation ranging from 0.07 to 7.42 MPa and an average of 2.19 MPa. The data are categorized into 19 instances below 1 MPa, 27 falling between 1 and 3 MPa, and 22 exceeding 3 MPa. Error distribution analysis indicates that the BR model provides more precise predictions for SCA-GPC CS compared to the SVM model, albeit with slightly narrower variability in measurements.

Figure 10 (a) Connection between experimental and predicted CS in the CS-BR model and (b) scattering of errors and predicted CS.
Figure 10

(a) Connection between experimental and predicted CS in the CS-BR model and (b) scattering of errors and predicted CS.

3.1.3 CS-RFR model

Figure 11 shows the utilization of the RFR model to estimate the CS of SCA-GPC. Figure 11(a) illustrates the agreement between observed and predicted CS values. The CS values predicted by the RFR model closely resemble those obtained experimentally. The RFR technique exhibits remarkable accuracy in estimating the CS of SCA-GPC, boasting an impressive R 2 value of 0.9688, with 99% of predictions falling below the 20% threshold, as depicted in Figure 11(b). The error range, depicting the differences between experimental and predicted values using the RFR approach, varies with a standard deviation ranging from 0.01 to 11.56 MPa and an average of approximately 1.32 MPa. Further analysis reveals that among the total values, 48 were below 1 MPa, 21 fell between 1 and 3 MPa, and only 9 exceeded 3 MPa. The error distribution underscores the superior accuracy of the RFR model in predicting the CS of SCA-GPC compared to both the SVM and BR models, with significantly reduced error spread.

Figure 11 (a) Connection between experimental and predicted CS in the CS-RFR model and (b) scattering of errors and predicted CS.
Figure 11

(a) Connection between experimental and predicted CS in the CS-RFR model and (b) scattering of errors and predicted CS.

3.2 FS models

3.2.1 FS-SVM model

Figure 12 shows the result of using the SVM model to approximate the FS of SCA-GPC. Figure 12(a) shows the agreement between the expected and observed FS. In terms of FS, the predictions made by the SVM model were pretty comparable to the values that were measured. An effective estimation of the FS of SCA-GPC was achieved through the utilization of the SVM analysis. A high level of accuracy was exhibited by the model, which, similar to the CS-SVM model, had an R 2 value of 0.8853 and had a 100% of its predictions falling below the threshold of 20%. As shown in Figure 12(b), the SVM method’s projected values differ from the experimental values by a wide variety of margins. The erroneous results had a standard deviation ranging from 0.003 to 0.763 MPa, with an average of approximately 0.276 MPa. Additionally, the analysis unveiled that 45 of the values were below 0.3 MPa, 27 fell within the range of 0.3–0.5 MPa, and 6 were found to exceed 0.5 MPa. It is clear, after examining the distribution of the errors, that the FS of SCA-GPC may be predicted by applying an SVM model despite the fact that its measurements (errors) are widely dispersed.

Figure 12 (a) Connection between experimental and predicted FS in the FS-SVM model and (b) scattering of errors and predicted FS.
Figure 12

(a) Connection between experimental and predicted FS in the FS-SVM model and (b) scattering of errors and predicted FS.

3.2.2 FS-BR model

For the purpose of approximating the FS of SCA-GPC, the BR model was utilized, as shown in Figure 13. Figure 13(a) shows the graphic representation of the agreement between the observed and projected FS. The experimental results for FS were very close to the predictions made by the BR model. A remarkable level of accuracy was achieved when the BR approach was used to efficiently identify the FS of SCA-GPC. The method’s R 2 value was 0.9293, and all of its predictions fell inside the 20% criterion. Figure 13(b) illustrates the range of discrepancies (errors) between the BR-predicted and experimental values. The error values, averaging approximately 0.205 MPa, had a standard deviation ranging from 0.003 to 0.591 MPa. Moreover, 56 of the values were below 0.3 MPa, 18 fell between 0.3 and 0.5 MPa, and 4 exceeded 0.5 MPa. The BR model’s FS prediction of SCA-GPC was noticeably more accurate than the SVM model’s, with slightly lower spread measurements (errors), as can be seen from the distribution of the errors.

Figure 13 (a) Connection between experimental and predicted FS in the FS-BR model and (b) scattering of errors and predicted FS.
Figure 13

(a) Connection between experimental and predicted FS in the FS-BR model and (b) scattering of errors and predicted FS.

3.2.3 FS-RFR model

For the purpose of approximating the FS of SCA-GPC, the RFR model was utilized, as shown in Figure 14. It is possible to notice a graphical representation of the degree of agreement that exists between the anticipated and observed FS in Figure 14(a). The FS values predicted by the RFR model and those obtained experimentally were very similar. An R 2 value of 0.9753, coupled with all predictions falling within the 20% threshold, highlights significantly improved accuracy in determining the FS of SCA-GPC through the RFR approach. In Figure 14(b), the distribution of errors or discrepancies between the experimental and predicted values using the RFR approach is illustrated. On average, the incorrect readings were around 0.090 MPa, with a standard deviation ranging from 0.002 to 0.936 MPa. Additionally, it was observed that 77 of the values were below 0.3 MPa, there were no values falling between 0.3 and 0.5 MPa, and only 1 value exceeded 0.5 MPa. By looking at the distribution of the errors, it is evident that the prediction of FS of SCA-GPC utilizing the RFR model was expressively more accurate than both the SVM and BR models, with significantly lesser spread measurements (errors).

Figure 14 (a) Connection between experimental and predicted FS in the FS-RFR model and (b) scattering of errors and predicted FS.
Figure 14

(a) Connection between experimental and predicted FS in the FS-RFR model and (b) scattering of errors and predicted FS.

3.3 Validation of models

Table 2 displays the results of Eqs. (1)–(3) applied to the CS and CS-approximation models in terms of the computed errors (MAE), root-mean-square error (RMSE), and mean absolute percentage error (MAPE). The MAEs for CS predictions using SVM, BR, and RFR were 3.420, 2.190, and 1.320 MPa, respectively. SVM, BR, and RFR all improved performance by an average of 11.10%, 6.90%, and 3.90%, respectively, according to the MAPE metric. Moreover, the RMSE values were calculated as 4.194 MPa for SVM, 2.907 MPa for BR, and 2.211 MPa for RFR. Similar trends were observed in the prediction models for flexural strength (FS) regarding MAE, RMSE, and MAPE, as seen in the CS prediction models. These findings indicate that compared to SVM and BR models, the RFR method offers superior accuracy. Table 3 displays the outcomes of computing R 2, RMSE, and MAE to validate the K-fold approach, while Figure 15 illustrates the K-fold assessments of various ML techniques for predicting CS and FS. The SVM approach produced CS estimates with MAE values ranging from 2.43 to 5.92 MPa, with an average of 3.73 MPa. The CS-BR model showed an MAE of 3.66 MPa, with a confidence interval from 2.00 to 5.05 MPa. Additionally, RFR had a mean MAE of 1.98 MPa, ranging from 0.97 to 4.43 MPa (minimum to maximum). The mean RMSE was also 4.71 MPa for the CS-SVM method, 4.67 MPa for the CS-BR method, and 2.39 MPa for the CS-RFR method. The highest R 2 values for CS-based SVM, BR, and RFR are, nevertheless, 0.88, 0.94, and 0.98. The FS prediction K-fold analysis showed a significant decrease in MAE and RMSE values from SVM to BR to RFR, with a slight increase in R 2 across the same models. The top-performing RFR model exhibited the highest R 2 and the lowest error rate for predicting both CS and FS of SCA-GPC. Based on K-fold R 2 measurements and error analysis, the RFR model proved to be the most accurate. However, both SVM and BR models also demonstrated strong performance. Consequently, SVM, BR, and RFR models may provide a more precise means of assessing the CS and FS of SCA-GPC.

Table 2

Assessment of errors through statistical methods

ML technique CS FS
MAPE (%) RMSE (MPa) MAE (MPa) MAPE (%) RMSE (MPa) MAE (MPa)
SVM 11.10 4.194 3.420 5.30 0.321 0.277
BR 6.90 2.907 2.190 4.00 0.256 0.205
RFR 3.90 2.211 1.320 1.80 0.148 0.091
Table 3

Accuracy metrics (RMSE, R 2, and MAE) obtained from k-fold analysis

Property ML model Parameters K-fold
1 2 3 4 5 6 7 8 9 10
CS SVM MAE 2.43 3.99 3.93 3.12 3.65 5.92 3.78 2.72 4.69 3.11
RMSE 3.09 5.34 5.81 4.36 4.66 6.09 4.73 3.26 5.38 4.34
R 2 0.88 0.60 0.69 0.53 0.81 0.74 0.86 0.87 0.72 0.84
BR MAE 2.00 3.90 4.76 3.37 4.25 5.05 3.24 2.87 4.61 2.55
RMSE 3.05 5.16 6.16 4.22 5.21 5.83 4.55 3.16 5.35 4.00
R 2 0.94 0.61 0.62 0.55 0.78 0.74 0.89 0.92 0.69 0.92
RFR MAE 2.87 1.22 1.19 0.97 1.84 4.43 1.58 2.80 1.68 1.25
RMSE 3.33 1.84 1.31 1.55 2.38 4.49 2.04 3.27 2.10 1.64
R 2 0.93 0.95 0.98 0.98 0.94 0.85 0.97 0.91 0.96 0.98
FS SVM MAE 0.21 0.25 0.24 0.18 0.28 0.35 0.19 0.22 0.22 0.18
RMSE 0.24 0.32 0.25 0.31 0.32 0.42 0.32 0.27 0.27 0.21
R 2 0.85 0.84 0.89 0.89 0.87 0.75 0.85 0.83 0.89 0.86
BR MAE 0.18 0.14 0.20 0.21 0.32 0.31 0.21 0.22 0.22 0.23
RMSE 0.14 0.31 0.30 0.24 0.35 0.38 0.26 0.16 0.28 0.27
R 2 0.93 0.86 0.85 0.87 0.85 0.79 0.93 0.93 0.88 0.93
RFR MAE 0.19 0.19 0.13 0.17 0.13 0.26 0.15 0.24 0.18 0.09
RMSE 0.24 0.23 0.17 0.21 0.14 0.31 0.20 0.34 0.19 0.09
R 2 0.96 0.92 0.95 0.86 0.96 0.85 0.97 0.82 0.95 0.97
Figure 15 The K-fold analysis findings: (a) CS and (b) FS.
Figure 15

The K-fold analysis findings: (a) CS and (b) FS.

3.4 Interaction of input parameters

This section analyses the relationship between the input variables and the final product, CS. Figure 16 illustrate the scatter plots comparing the CS of SCA-GPC with different inputs. The scatter plots are accompanied by bar graphs depicting the frequencies of the input and output components. The GGBFS effect and interaction are illustrated in Figure 16(a), which clearly demonstrates that the mechanical properties of concrete were directly impacted by both inputs. This indicates that the SCA-GPC’s strength was linearly proportional to the GGBFS content. The increased silica content in the GGBFS employed in different research may explain the higher quantities of GGBFS [68]. Figure 16(b) shows that the relationship between SCA-mechanical GPC’s and CCA’s characteristics was indirect. As the CCA concentration increased, the mechanical characteristics of SCA-GPC gradually degraded. Up to 800 kg·m−3, the strength of FA-input rose as the FA content grew; after that, the strength decreased significantly. Subsequently, when the FA concentration surpassed 850 kg·m−3, the SCA-GPC once again gained strength. Figure 16(c) shows the relationship between the FA content and the mechanical characteristics of SCA-GPC.

Figure 16 Interaction of input parameters with CS: (a) GGBFS, (b) CCA, (c) FA, (d) CA, (e) W, (f) SHP, (g) SHP, (h) CD, (i) MC, and (j) CG.
Figure 16 Interaction of input parameters with CS: (a) GGBFS, (b) CCA, (c) FA, (d) CA, (e) W, (f) SHP, (g) SHP, (h) CD, (i) MC, and (j) CG.
Figure 16

Interaction of input parameters with CS: (a) GGBFS, (b) CCA, (c) FA, (d) CA, (e) W, (f) SHP, (g) SHP, (h) CD, (i) MC, and (j) CG.

Figure 16(d)–(j) illustrate that factors such as CA, W, SHP, SSG, CD, MC, and CG have a minimal impact on concrete strength due to the low variability in the content of these input factors. The outcomes of the interaction analysis were notably influenced by both the raw material utilized and the size of the data sample under examination. Adjusting the input parameters and sampling frequency yields different results. It is important to note that the inputs and database size used to run the algorithms determined the aforementioned results. Using different databases and input factors can result in different outcomes. Further research is needed to enhance understanding of the relationship between the material’s components.

4 Discussions

In this study, the ML models are used to ensure that the predictions are specifically suited to GPC. This situation arises due to the limitation of these models to accept values from a constrained set of eight input variables. Given that all models utilize the same unit measurements and testing technique, it is feasible to depend on the CS and FS predictions generated by any of the models. If there are more than eight parameters in the composite analysis, it is possible that the projected models will not function properly. If the data used to train these models differ significantly dissimilar to what they are intended to achieve, it is possible that they will not perform as predicted. It is dependent on the degree of consistency or variation in the units of the input parameters as to how accurately the models anticipate the results. For the models to correctly function, it is essential to keep the unit sizes consistent. ML models offer diverse applications within the construction industry, encompassing tasks like material strength forecasting, quality assurance, risk assessment, predictive maintenance, and improving energy efficiency. Nonetheless, these models encounter challenges such as reliance on human input, utilization of potentially inaccurate data, and occasional errors in predictions. To overcome these hurdles and optimize ML-driven outcomes, future research avenues could include integrating Internet of Things (IoT) devices, developing hybrid models, adopting explainable AI methodologies, incorporating sustainability considerations, and tailoring data generation and dissemination processes for specific industrial sectors. These advancements in technology have the potential to yield significant advantages for the construction field, facilitating higher levels of efficiency, comprehension, accountability, and well-informed decision-making alongside enhanced safety and project efficacy. The findings of this study could also promote more environmentally responsible building practices in the construction industry, potentially increasing the adoption of GPC.

5 Conclusions

Using three different ML models, including SVM, BR, and RFR, the purpose of this work was to make a prediction about the mechanical properties of GPC (SCA-GPC) that was made up of slag and CCA. For the purpose of training and verifying the models that were produced, 260 different sets of data pertaining to mechanical characteristics were utilized. These sets included CS and FS. The following are some of the most significant findings that emerged from the research:

  • The study’s conclusion indicates that RFR models exhibited the highest accuracy in predicting the CS and FS of SCA-GPC among the models assessed. The R 2 values for the three ML models (SVM, BR, and RFR) that were created for SCA-GPC’s CS and FS prediction were all greater than 0.85.

  • Models were assessed for efficacy using statistical measures (MAE, RMSE, and MAPE). A more accurate ML model was represented by a lower error value. The lower error rates supported statements that RFR models accurately predicted SCA-GPC’s CS and FS.

  • K-fold analysis (MAE, RMSE, and R 2) also validated the RFR model’s exceptional precision as paralleled to the commendable precision of SVM and BR models.

  • The input/output interaction analysis revealed that the most important input parameters that had a stronger correlation with the CS and FS of SCA-GPC were FA, CCA, and GGBFS.

The methodology detailed in this article allows scientists and engineers to effectively assess, enhance, and validate GPC mixture proportioning. Nevertheless, additional research is needed to assemble a broader dataset encompassing a diverse range of strength grades to facilitate the development of prediction models.

Acknowledgments

The authors would like to express their deepest gratitude to the Scientific Research Deanship and to the College of Engineering at the University of Ha’il-Saudi Arabia for providing the necessary support for conducting this research through project number RG-23 062.

  1. Funding information: This research has been funded by the Scientific Research Deanship at the University of Ha’il – Saudi Arabia, through project number RG-23 062.

  2. Author contribution: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Conflict of interest: The authors state no conflict of interest.

  4. Data availability statement: The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.

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Received: 2024-03-11
Revised: 2024-04-29
Accepted: 2024-06-04
Published Online: 2024-06-25

© 2024 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  40. Numerical investigations on constitutive model parameters of HRB400 and HTRB600 steel bars based on tensile and fatigue tests
  41. Research progress on Fe3+-activated near-infrared phosphor
  42. Discrete element simulation study on effects of grain preferred orientation on micro-cracking and macro-mechanical behavior of crystalline rocks
  43. Ultrasonic resonance evaluation method for deep interfacial debonding defects of multilayer adhesive bonded materials
  44. Effect of impurity components in titanium gypsum on the setting time and mechanical properties of gypsum-slag cementitious materials
  45. Bending energy absorption performance of composite fender piles with different winding angles
  46. Theoretical study of the effect of orientations and fibre volume on the thermal insulation capability of reinforced polymer composites
  47. Synthesis and characterization of a novel ternary magnetic composite for the enhanced adsorption capacity to remove organic dyes
  48. Couple effects of multi-impact damage and CAI capability on NCF composites
  49. Mechanical testing and engineering applicability analysis of SAP concrete used in buffer layer design for tunnels in active fault zones
  50. Investigating the rheological characteristics of alkali-activated concrete using contemporary artificial intelligence approaches
  51. Integrating micro- and nanowaste glass with waste foundry sand in ultra-high-performance concrete to enhance material performance and sustainability
  52. Effect of water immersion on shear strength of epoxy adhesive filled with graphene nanoplatelets
  53. Impact of carbon content on the phase structure and mechanical properties of TiBCN coatings via direct current magnetron sputtering
  54. Investigating the anti-aging properties of asphalt modified with polyphosphoric acid and tire pyrolysis oil
  55. Biomedical and therapeutic potential of marine-derived Pseudomonas sp. strain AHG22 exopolysaccharide: A novel bioactive microbial metabolite
  56. Effect of basalt fiber length on the behavior of natural hydraulic lime-based mortars
  57. Optimizing the performance of TPCB/SCA composite-modified asphalt using improved response surface methodology
  58. Compressive strength of waste-derived cementitious composites using machine learning
  59. Melting phenomenon of thermally stratified MHD Powell–Eyring nanofluid with variable porosity past a stretching Riga plate
  60. Development and characterization of a coaxial strain-sensing cable integrated steel strand for wide-range stress monitoring
  61. Compressive and tensile strength estimation of sustainable geopolymer concrete using contemporary boosting ensemble techniques
  62. Customized 3D printed porous titanium scaffolds with nanotubes loading antibacterial drugs for bone tissue engineering
  63. Facile design of PTFE-kaolin-based ternary nanocomposite as a hydrophobic and high corrosion-barrier coating
  64. Effects of C and heat treatment on microstructure, mechanical, and tribo-corrosion properties of VAlTiMoSi high-entropy alloy coating
  65. Study on the damage mechanism and evolution model of preloaded sandstone subjected to freezing–thawing action based on the NMR technology
  66. Promoting low carbon construction using alkali-activated materials: A modeling study for strength prediction and feature interaction
  67. Entropy generation analysis of MHD convection flow of hybrid nanofluid in a wavy enclosure with heat generation and thermal radiation
  68. Friction stir welding of dissimilar Al–Mg alloys for aerospace applications: Prospects and future potential
  69. Fe nanoparticle-functionalized ordered mesoporous carbon with tailored mesostructures and their applications in magnetic removal of Ag(i)
  70. Study on physical and mechanical properties of complex-phase conductive fiber cementitious materials
  71. Evaluating the strength loss and the effectiveness of glass and eggshell powder for cement mortar under acidic conditions
  72. Effect of fly ash on properties and hydration of calcium sulphoaluminate cement-based materials with high water content
  73. Analyzing the efficacy of waste marble and glass powder for the compressive strength of self-compacting concrete using machine learning strategies
  74. Experimental study on municipal solid waste incineration ash micro-powder as concrete admixture
  75. Parameter optimization for ultrasonic-assisted grinding of γ-TiAl intermetallics: A gray relational analysis approach with surface integrity evaluation
  76. Producing sustainable binding materials using marble waste blended with fly ash and rice husk ash for building materials
  77. Effect of steam curing system on compressive strength of recycled aggregate concrete
  78. A sawtooth constitutive model describing strain hardening and multiple cracking of ECC under uniaxial tension
  79. Predicting mechanical properties of sustainable green concrete using novel machine learning: Stacking and gene expression programming
  80. Toward sustainability: Integrating experimental study and data-driven modeling for eco-friendly paver blocks containing plastic waste
  81. A numerical analysis of the rotational flow of a hybrid nanofluid past a unidirectional extending surface with velocity and thermal slip conditions
  82. A magnetohydrodynamic flow of a water-based hybrid nanofluid past a convectively heated rotating disk surface: A passive control of nanoparticles
  83. Prediction of flexural strength of concrete with eggshell and glass powders: Advanced cutting-edge approach for sustainable materials
  84. Efficacy of sustainable cementitious materials on concrete porosity for enhancing the durability of building materials
  85. Phase and microstructural characterization of swat soapstone (Mg3Si4O10(OH)2)
  86. Effect of waste crab shell powder on matrix asphalt
  87. Improving effect and mechanism on service performance of asphalt binder modified by PW polymer
  88. Influence of pH on the synthesis of carbon spheres and the application of carbon sphere-based solid catalysts in esterification
  89. Experimenting the compressive performance of low-carbon alkali-activated materials using advanced modeling techniques
  90. Thermogravimetric (TG/DTG) characterization of cold-pressed oil blends and Saccharomyces cerevisiae-based microcapsules obtained with them
  91. Investigation of temperature effect on thermo-mechanical property of carbon fiber/PEEK composites
  92. Computational approaches for structural analysis of wood specimens
  93. Integrated structure–function design of 3D-printed porous polydimethylsiloxane for superhydrophobic engineering
  94. Exploring the impact of seashell powder and nano-silica on ultra-high-performance self-curing concrete: Insights into mechanical strength, durability, and high-temperature resilience
  95. Axial compression damage constitutive model and damage characteristics of fly ash/silica fume modified magnesium phosphate cement after being treated at different temperatures
  96. Integrating testing and modeling methods to examine the feasibility of blended waste materials for the compressive strength of rubberized mortar
  97. Special Issue on 3D and 4D Printing of Advanced Functional Materials - Part II
  98. Energy absorption of gradient triply periodic minimal surface structure manufactured by stereolithography
  99. Marine polymers in tissue bioprinting: Current achievements and challenges
  100. Quick insight into the dynamic dimensions of 4D printing in polymeric composite mechanics
  101. Recent advances in 4D printing of hydrogels
  102. Mechanically sustainable and primary recycled thermo-responsive ABS–PLA polymer composites for 4D printing applications: Fabrication and studies
  103. Special Issue on Materials and Technologies for Low-carbon Biomass Processing and Upgrading
  104. Low-carbon embodied alkali-activated materials for sustainable construction: A comparative study of single and ensemble learners
  105. Study on bending performance of prefabricated glulam-cross laminated timber composite floor
  106. Special Issue on Recent Advancement in Low-carbon Cement-based Materials - Part I
  107. Supplementary cementitious materials-based concrete porosity estimation using modeling approaches: A comparative study of GEP and MEP
  108. Modeling the strength parameters of agro waste-derived geopolymer concrete using advanced machine intelligence techniques
  109. Promoting the sustainable construction: A scientometric review on the utilization of waste glass in concrete
  110. Incorporating geranium plant waste into ultra-high performance concrete prepared with crumb rubber as fine aggregate in the presence of polypropylene fibers
  111. Investigation of nano-basic oxygen furnace slag and nano-banded iron formation on properties of high-performance geopolymer concrete
  112. Effect of incorporating ultrafine palm oil fuel ash on the resistance to corrosion of steel bars embedded in high-strength green concrete
  113. Influence of nanomaterials on properties and durability of ultra-high-performance geopolymer concrete
  114. Influence of palm oil ash and palm oil clinker on the properties of lightweight concrete
https://doi.org/10.1515/rams-2024-0035
Schlagwörter für diesen Artikel
artificial intelligence; geopolymer concrete; mechanical properties
Creative Commons
BY 4.0

Artikel in diesem Heft

  1. Review Articles
  2. Effect of superplasticizer in geopolymer and alkali-activated cement mortar/concrete: A review
  3. Experimenting the influence of corncob ash on the mechanical strength of slag-based geopolymer concrete
  4. Powder metallurgy processing of high entropy alloys: Bibliometric analysis and systematic review
  5. Exploring the potential of agricultural waste as an additive in ultra-high-performance concrete for sustainable construction: A comprehensive review
  6. A review on partial substitution of nanosilica in concrete
  7. Foam concrete for lightweight construction applications: A comprehensive review of the research development and material characteristics
  8. Modification of PEEK for implants: Strategies to improve mechanical, antibacterial, and osteogenic properties
  9. Interfacing the IoT in composite manufacturing: An overview
  10. Advances in processing and ablation properties of carbon fiber reinforced ultra-high temperature ceramic composites
  11. Advancing auxetic materials: Emerging development and innovative applications
  12. Revolutionizing energy harvesting: A comprehensive review of thermoelectric devices
  13. Exploring polyetheretherketone in dental implants and abutments: A focus on biomechanics and finite element methods
  14. Smart technologies and textiles and their potential use and application in the care and support of elderly individuals: A systematic review
  15. Reinforcement mechanisms and current research status of silicon carbide whisker-reinforced composites: A comprehensive review
  16. Innovative eco-friendly bio-composites: A comprehensive review of the fabrication, characterization, and applications
  17. Review on geopolymer concrete incorporating Alccofine-1203
  18. Advancements in surface treatments for aluminum alloys in sports equipment
  19. Ionic liquid-modified carbon-based fillers and their polymer composites – A Raman spectroscopy analysis
  20. Emerging boron nitride nanosheets: A review on synthesis, corrosion resistance coatings, and their impacts on the environment and health
  21. Mechanism, models, and influence of heterogeneous factors of the microarc oxidation process: A comprehensive review
  22. Synthesizing sustainable construction paradigms: A comprehensive review and bibliometric analysis of granite waste powder utilization and moisture correction in concrete
  23. 10.1515/rams-2025-0086
  24. Research Articles
  25. Coverage and reliability improvement of copper metallization layer in through hole at BGA area during load board manufacture
  26. Study on dynamic response of cushion layer-reinforced concrete slab under rockfall impact based on smoothed particle hydrodynamics and finite-element method coupling
  27. Study on the mechanical properties and microstructure of recycled brick aggregate concrete with waste fiber
  28. Multiscale characterization of the UV aging resistance and mechanism of light stabilizer-modified asphalt
  29. Characterization of sandwich materials – Nomex-Aramid carbon fiber performances under mechanical loadings: Nonlinear FE and convergence studies
  30. Effect of grain boundary segregation and oxygen vacancy annihilation on aging resistance of cobalt oxide-doped 3Y-TZP ceramics for biomedical applications
  31. Mechanical damage mechanism investigation on CFRP strengthened recycled red brick concrete
  32. Finite element analysis of deterioration of axial compression behavior of corroded steel-reinforced concrete middle-length columns
  33. Grinding force model for ultrasonic assisted grinding of γ-TiAl intermetallic compounds and experimental validation
  34. Enhancement of hardness and wear strength of pure Cu and Cu–TiO2 composites via a friction stir process while maintaining electrical resistivity
  35. Effect of sand–precursor ratio on mechanical properties and durability of geopolymer mortar with manufactured sand
  36. Research on the strength prediction for pervious concrete based on design porosity and water-to-cement ratio
  37. Development of a new damping ratio prediction model for recycled aggregate concrete: Incorporating modified admixtures and carbonation effects
  38. Exploring the viability of AI-aided genetic algorithms in estimating the crack repair rate of self-healing concrete
  39. Modification of methacrylate bone cement with eugenol – A new material with antibacterial properties
  40. Numerical investigations on constitutive model parameters of HRB400 and HTRB600 steel bars based on tensile and fatigue tests
  41. Research progress on Fe3+-activated near-infrared phosphor
  42. Discrete element simulation study on effects of grain preferred orientation on micro-cracking and macro-mechanical behavior of crystalline rocks
  43. Ultrasonic resonance evaluation method for deep interfacial debonding defects of multilayer adhesive bonded materials
  44. Effect of impurity components in titanium gypsum on the setting time and mechanical properties of gypsum-slag cementitious materials
  45. Bending energy absorption performance of composite fender piles with different winding angles
  46. Theoretical study of the effect of orientations and fibre volume on the thermal insulation capability of reinforced polymer composites
  47. Synthesis and characterization of a novel ternary magnetic composite for the enhanced adsorption capacity to remove organic dyes
  48. Couple effects of multi-impact damage and CAI capability on NCF composites
  49. Mechanical testing and engineering applicability analysis of SAP concrete used in buffer layer design for tunnels in active fault zones
  50. Investigating the rheological characteristics of alkali-activated concrete using contemporary artificial intelligence approaches
  51. Integrating micro- and nanowaste glass with waste foundry sand in ultra-high-performance concrete to enhance material performance and sustainability
  52. Effect of water immersion on shear strength of epoxy adhesive filled with graphene nanoplatelets
  53. Impact of carbon content on the phase structure and mechanical properties of TiBCN coatings via direct current magnetron sputtering
  54. Investigating the anti-aging properties of asphalt modified with polyphosphoric acid and tire pyrolysis oil
  55. Biomedical and therapeutic potential of marine-derived Pseudomonas sp. strain AHG22 exopolysaccharide: A novel bioactive microbial metabolite
  56. Effect of basalt fiber length on the behavior of natural hydraulic lime-based mortars
  57. Optimizing the performance of TPCB/SCA composite-modified asphalt using improved response surface methodology
  58. Compressive strength of waste-derived cementitious composites using machine learning
  59. Melting phenomenon of thermally stratified MHD Powell–Eyring nanofluid with variable porosity past a stretching Riga plate
  60. Development and characterization of a coaxial strain-sensing cable integrated steel strand for wide-range stress monitoring
  61. Compressive and tensile strength estimation of sustainable geopolymer concrete using contemporary boosting ensemble techniques
  62. Customized 3D printed porous titanium scaffolds with nanotubes loading antibacterial drugs for bone tissue engineering
  63. Facile design of PTFE-kaolin-based ternary nanocomposite as a hydrophobic and high corrosion-barrier coating
  64. Effects of C and heat treatment on microstructure, mechanical, and tribo-corrosion properties of VAlTiMoSi high-entropy alloy coating
  65. Study on the damage mechanism and evolution model of preloaded sandstone subjected to freezing–thawing action based on the NMR technology
  66. Promoting low carbon construction using alkali-activated materials: A modeling study for strength prediction and feature interaction
  67. Entropy generation analysis of MHD convection flow of hybrid nanofluid in a wavy enclosure with heat generation and thermal radiation
  68. Friction stir welding of dissimilar Al–Mg alloys for aerospace applications: Prospects and future potential
  69. Fe nanoparticle-functionalized ordered mesoporous carbon with tailored mesostructures and their applications in magnetic removal of Ag(i)
  70. Study on physical and mechanical properties of complex-phase conductive fiber cementitious materials
  71. Evaluating the strength loss and the effectiveness of glass and eggshell powder for cement mortar under acidic conditions
  72. Effect of fly ash on properties and hydration of calcium sulphoaluminate cement-based materials with high water content
  73. Analyzing the efficacy of waste marble and glass powder for the compressive strength of self-compacting concrete using machine learning strategies
  74. Experimental study on municipal solid waste incineration ash micro-powder as concrete admixture
  75. Parameter optimization for ultrasonic-assisted grinding of γ-TiAl intermetallics: A gray relational analysis approach with surface integrity evaluation
  76. Producing sustainable binding materials using marble waste blended with fly ash and rice husk ash for building materials
  77. Effect of steam curing system on compressive strength of recycled aggregate concrete
  78. A sawtooth constitutive model describing strain hardening and multiple cracking of ECC under uniaxial tension
  79. Predicting mechanical properties of sustainable green concrete using novel machine learning: Stacking and gene expression programming
  80. Toward sustainability: Integrating experimental study and data-driven modeling for eco-friendly paver blocks containing plastic waste
  81. A numerical analysis of the rotational flow of a hybrid nanofluid past a unidirectional extending surface with velocity and thermal slip conditions
  82. A magnetohydrodynamic flow of a water-based hybrid nanofluid past a convectively heated rotating disk surface: A passive control of nanoparticles
  83. Prediction of flexural strength of concrete with eggshell and glass powders: Advanced cutting-edge approach for sustainable materials
  84. Efficacy of sustainable cementitious materials on concrete porosity for enhancing the durability of building materials
  85. Phase and microstructural characterization of swat soapstone (Mg3Si4O10(OH)2)
  86. Effect of waste crab shell powder on matrix asphalt
  87. Improving effect and mechanism on service performance of asphalt binder modified by PW polymer
  88. Influence of pH on the synthesis of carbon spheres and the application of carbon sphere-based solid catalysts in esterification
  89. Experimenting the compressive performance of low-carbon alkali-activated materials using advanced modeling techniques
  90. Thermogravimetric (TG/DTG) characterization of cold-pressed oil blends and Saccharomyces cerevisiae-based microcapsules obtained with them
  91. Investigation of temperature effect on thermo-mechanical property of carbon fiber/PEEK composites
  92. Computational approaches for structural analysis of wood specimens
  93. Integrated structure–function design of 3D-printed porous polydimethylsiloxane for superhydrophobic engineering
  94. Exploring the impact of seashell powder and nano-silica on ultra-high-performance self-curing concrete: Insights into mechanical strength, durability, and high-temperature resilience
  95. Axial compression damage constitutive model and damage characteristics of fly ash/silica fume modified magnesium phosphate cement after being treated at different temperatures
  96. Integrating testing and modeling methods to examine the feasibility of blended waste materials for the compressive strength of rubberized mortar
  97. Special Issue on 3D and 4D Printing of Advanced Functional Materials - Part II
  98. Energy absorption of gradient triply periodic minimal surface structure manufactured by stereolithography
  99. Marine polymers in tissue bioprinting: Current achievements and challenges
  100. Quick insight into the dynamic dimensions of 4D printing in polymeric composite mechanics
  101. Recent advances in 4D printing of hydrogels
  102. Mechanically sustainable and primary recycled thermo-responsive ABS–PLA polymer composites for 4D printing applications: Fabrication and studies
  103. Special Issue on Materials and Technologies for Low-carbon Biomass Processing and Upgrading
  104. Low-carbon embodied alkali-activated materials for sustainable construction: A comparative study of single and ensemble learners
  105. Study on bending performance of prefabricated glulam-cross laminated timber composite floor
  106. Special Issue on Recent Advancement in Low-carbon Cement-based Materials - Part I
  107. Supplementary cementitious materials-based concrete porosity estimation using modeling approaches: A comparative study of GEP and MEP
  108. Modeling the strength parameters of agro waste-derived geopolymer concrete using advanced machine intelligence techniques
  109. Promoting the sustainable construction: A scientometric review on the utilization of waste glass in concrete
  110. Incorporating geranium plant waste into ultra-high performance concrete prepared with crumb rubber as fine aggregate in the presence of polypropylene fibers
  111. Investigation of nano-basic oxygen furnace slag and nano-banded iron formation on properties of high-performance geopolymer concrete
  112. Effect of incorporating ultrafine palm oil fuel ash on the resistance to corrosion of steel bars embedded in high-strength green concrete
  113. Influence of nanomaterials on properties and durability of ultra-high-performance geopolymer concrete
  114. Influence of palm oil ash and palm oil clinker on the properties of lightweight concrete
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