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Integrating testing and modeling methods to examine the feasibility of blended waste materials for the compressive strength of rubberized mortar

  • Muhammad Nasir Amin EMAIL logo , Roz-Ud-Din Nassar , Kaffayatullah Khan EMAIL logo , Siyab Ul Arifeen EMAIL logo , Mubasher Khan and Muhammad Tahir Qadir
Published/Copyright: December 31, 2024
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REVIEWS ON ADVANCED MATERIALS SCIENCE
From the journal REVIEWS ON ADVANCED MATERIALS SCIENCE Volume 63 Issue 1

Abstract

This research integrated glass powder (GP), marble powder (MP), and silica fume (SF) into rubberized mortar to evaluate their effectiveness in enhancing compressive strength ( f c ′ ). Rubberized mortar cubes were produced by replacing fine aggregates with shredded rubber in varying proportions. The decrease in rubberized mortar’s f c ′ was controlled by substituting cement with GP, MP, and SF. Although many literature studies have evaluated the suitability of industrial waste, such as MP, SF, and GP, as construction material, no studies have yet included the combined effect of these wastes on the f c ′ of rubberized mortar. This study aims to provide complete insight into the combined effect of industrial waste on the f c ′ of rubberized mortar. By substituting cement, GP, MP, and SF were added to rubberized mortar in different proportions from 5 to 25%. Furthermore, artificial intelligence prediction models were developed using experimental data to assess the f c ′ of rubberized mortar. The study determined that the optimal substitution levels for GP, MP, and SF in rubberized mortar were 15, 10, and 15%, respectively. Similarly, partial dependence plot analysis suggests that SF, MP, and GP have a comparable effect on the f c ′ of rubberized mortar. The machine learning models demonstrated a significant resemblance to test results. Two individual techniques, support vector machine and random forest, generate R 2 values of 0.943 and 0.983, respectively.

Keywords: rubberized mortar; compressive strength; industrial waste

1 Introduction

The construction sector significantly contributes to global CO2 emissions, responsible for 33% of overall CO2 emissions across all construction sectors [1]. Concrete, a crucial component of construction materials, significantly contributes to CO2 emissions [2]. It is imperative to decrease the carbon footprint (CFP) resulting from concrete manufacturing to promote the growth of the sustainable building industry in developing countries. To achieve CO2 reduction and foster a circular economy, international bodies and governments are exploring strategies to curtail primary resource consumption and maximize recycling rates [3]. The United Nations (UN) proposed the theory of Industrial Symbiosis Systems (ISS), which involves utilizing the derivatives of one trade as the resource for another trade, hence facilitating resource recycling [4]. To create a circular building economy, it is crucial to establish cyclical material flows, extend building lifespans, and maximize the use of recycled waste materials in construction [5]. According to the concept of UN-ISS, the utilization of waste like marble powder (MP), rubber, silica fume (SF), plastic, glass powder (GP), etc., in building materials can effectively decrease the quantity of waste being sent to landfills and also decrease the consumption of natural resources in the construction sector.

Recently, a notable rise has been observed in the number of automobiles worldwide, leading to a large amount of wasted tires. Usually, waste rubber (WR) tires are commonly disposed of via techniques like burning and dumping [6], which can lead to the wastage of resources, environmental pollution, and safety issues. Implementing suitable treatment techniques to mitigate the risk caused by discarded tires to the environment is crucial. Employing discarded tire rubber as an alternative to natural aggregate in traditional construction materials offers significant advantages for promoting a circular economy. It is the best approach for addressing potential environmental issues. Many research studies have explored the application of rubber as an aggregate or binder in place of traditional aggregates or cement to some extent. Murali et al. [7] studied the effect of steel fibers on the properties of concrete modified with crumb rubber aggregates. Tang et al. [4] studied the CFP analysis of WR concrete through a life cycle assessment approach. Similarly, Shahjalal et al. [8] researched replacing 0–15% with the increment of 5% fine aggregates (FA) with WR. Various mechanical and physical properties, including microstructure analysis, were assessed. In their study, Su et al. [9] replaced 0–35% WR to investigate modified concrete’s tensile, toughness, compressive, and single crack tensile strength. The study of Cr and Kathirvel [10] replaced aggregates with crumb rubber, and to minimize the impact of CO2 emission, this study also substituted cement with GGBFS and SF. Meanwhile, Karimi et al. [11] examined the impact of the size and proportion of WR granules on various physical characteristics (density and slump), strength parameters (tensile and compressive), and precisely the characteristics of crack initiation and propagation (in terms of shearing, tearing modes, and opening). WR in concrete offers several benefits, such as enhancing flexibility, decreasing weight, enhancing the ability to absorb energy, boosting resistance to impact, and minimizing the tendency to shrink when dry. These advantages demonstrate the possibility of employing granular WR in concrete or mortar. However, previous studies have demonstrated that rubberized mortar can be used in nonstructural elements like precast blocks or non-load-bearing walls, as they have diminished strength than conventional mortar or concrete [5,12]. So, rubberized mortar is irreplaceable with conventional reinforced concrete.

Recently, comprehensive research has been conducted on replacing cement with supplemental cementitious materials (SCMs) such as GP, SF, MP, and other similar materials. MP is readily available in several countries and can be employed as a supplementary for cement in mortar or concrete production [13]. Because of its enduring characteristics, marble is frequently exploited for various non-structural products, like architectural decoration, cladding, sculpture, and flooring. A considerable amount of garbage is generated, mainly composed of dust particles, throughout the carving and cutting procedures for different marble applications. These materials harm natural habitats and trigger environmental degradation. MP has been utilized as a substitute for up to 60% of cement or aggregate proportion. SF, a highly pozzolanic by-product derived from the ferrosilicon industry, can enhance the durability and strength of concrete [14]. The research undertaken by Zhao and Zhang [15] suggests that substituting minor amounts of cement with SF in the concrete mix can produce high-performance concrete with exceptional mechanical characteristics. Murali et al. [16] investigated the impact resistance of ultra-high-performance geopolymer concrete enhanced with SF and GP. The inclusion of SF as an SCM in cementitious mixtures initiates a chemical makeup with calcium hydroxide due to cement hydration. The end product of this chemical makeup leads to the creation of an extra gel consisting of calcium silicate hydrate (CSH) [17]. Incorporating SF into a cementitious mixture augments the strength properties and performance of the material when subjected to loading. Comprehensive research efforts have been made on SF because of its exceptional mechanical and physical features. The findings have shown a substantial enhancement in the mechanical properties of cementitious composites, especially under higher strain rates. A previous study [17] showed that using 10% SF as an SCM is the most effective. This mixture has a compressive strength ( f c ′ ) of around 32.5 N·mm−2 and a fracture tensile strength of 3.45 N·mm−2. Adding more SF after this point leads to a loss in strength.

Compared to other waste like plastic and wood, glass exhibits chemical resilience. Glass cannot be crashed by natural activities, even when buried for an unlimited duration [18]. Consequently, the appropriate reuse of glass debris is garnering substantial worldwide attention. Repurposing glass trash into glass-based goods is a prevalent approach to recycling glass waste. To create glass products, cleaning and melting glass trash is necessary. An innovative process for reusing glass trash involves the fabrication of building materials. Glass trash can be converted into fine powder and partially replaced cement and/or aggregate in cementitious composites [19]. The utilization of crumpled glass, which is of particular aggregate size, is increasingly significant as a waste material due to its profound impact on concrete manufacturing. Replacing natural aggregates with shattered glass fragments is an appealing and noteworthy method that offers both environmental benefits and practical applications in the sustainable construction industry. Initially, it is worth noting that glass does not necessitate the process of being melted, resulting in reduced energy usage. Furthermore, glass does not necessitate cleaning and sifting, therefore simplifying the management of glass trash. Moreover, the increasing use of cement-based composites in the construction sector will lead to a higher need for glass waste. Cement-based materials provide the capacity to contain and separate dangerous glass components efficiently. Previous research has demonstrated that incorporating glass remains into cement-based compounds is a more advantageous method [20,21]. Harrison et al. [22] employed various proportions of GP fragments as an alternative for FA and cement, targeting to augment concrete’s mechanical properties. The obtained data show that strength was preserved when using 20% GP. Nevertheless, when the additions surpassed 30%, an adverse effect on its strength was detected.

Various literature studies have been conducted on GP, MP, and WR applications in regular concrete and/or mortar. Similarly, considerable studies have been undertaken to study the impact of SF on cementitious composites, compiled in a recent review article [23]. However, no prior research has examined the collective impact of these factors on cement mortar, specifically when incorporating WR as a partial replacement for FA. This work explores the collective effect of GP, MP, and SF on the f c ′ of rubberized mortar. For this purpose, FA is replaced with WR in various proportions ranging from 0 to 25%. The addition of WR to the mortar cubes will considerably reduce its strength. Therefore, this study evaluated the influence of using GP, MP, and SF individually and together on the f c ′ of mortar samples altered with WR. Various mortar samples were produced by altering the proportions of GP, MP, and SF, replacing cement with percentages ranging from 5 to 25%. The study evaluated the collective influence of GP + MP, SF + GP, MP + SF, and GP + SF + MP by augmenting cement with mass fractions ranging from 5 to 15%. Undoubtedly, conducting laboratory experiments demands a substantial investment of resources and effort, as it entails the acquisition of raw materials, creating samples followed by curing, and, finally, laboratory testing. Evolutionary techniques, like machine learning (ML), can assist the construction sector in addressing the above-described challenges. Artificial intelligence (AI) methodologies, such as ML, originate in developing new modeling techniques in this field. The use of ML approaches to forecast the performance of engineered materials is gaining more prominence [24,25,26]. An emerging practice is utilizing data-driven methods to establish a non-linear correlation between the elements of mortar or concrete and their mechanical properties [27,28]. Following the laboratory testing, the test results were systematically arranged to develop prediction algorithms using ML techniques. The purpose was to examine the efficiency of ML algorithms in forecasting the f c ′ of rubberized mortar. Two ML algorithms, namely random forest (RF) and support vector machine (SVM), were nominated to evaluate their predicting capabilities. The chosen ML methodologies employed unprocessed data like FA, SCMs, and cement quantities as input and generated f c ′ as an outcome. ML technologies can minimize the need for additional experiments in adjusting the composition of rubberized mortar in the engineering industry. After the successful implication of ML models, this study utilized partial dependence plot (PDP) analysis to study further the effect of input precursors on the target variable. The results of this work have the potential to promote the utilization of sustainable building materials by recognizing the feasibility of using rubberized mortar that is improved using industrial waste.

2 Methodology

2.1 Raw materials and mix design

The raw ingredients used in this research consist of accessible FA, type 1 Portland cement, and water suitable for drinking. The scrambled WR was acquired from a rubber recycling site in close proximity and subsequently passed through sieve #4 to guarantee that it adhered to the required size standards for its application as a substitute for FA. The shredded WR underwent a 24-h water soaking pretreatment before being added to the mix in a saturated surface-dry state. Pakistan’s local industrial chemical firm has been reached to procure superplasticizer (SP) and SF. The study employed CARBOPLAST G-2002 SP, intended explicitly for cement-based materials. The SPs were utilized to attain the optimum workability of the blend, thereby decreasing the ratio of water to cement (W/C). The MP was obtained from a marble factory located nearby. The GP and MP were filtered through a #200 sieve before being used as a replacement for cement. The ball mill device was used to convert glass trash into GP. Table 1 presents the precise elements, their respective quantities in kg·m−3, and their appropriate ratios. The reference sample, also called the control mix (CM), consisted of a W/C of 0.28, a binder-to-FA ratio of 1:1.5, and a 1.25% SP dosage based on the mass of the cement. WR was incorporated into mortar samples as a replacement for FA, with mass proportions of 5–25%. In light of the substantial proof that incorporating WR into cement-based composites decreases the material’s strength [29], other SCMs like GP, MP, and SF were employed in different proportions (5–25% by weight) as replacements for cement. This was conducted to determine their influence on the f c ′ of rubberized mortar.

Table 1

Quantity of materials adopted to create rubberized mortar mixtures

Rubber content (%) FA (kg·m−3) Cement (kg·m−3) Water (kg·m−3) Rubber (kg·m−3) MP/SF/GP (%) MP/SF/GP (kg·m−3) SP (kg·m−3)
0 1,240 825 231 0 0 0 10.3
5 1,178 825 231 62 5 41.25 10.3
10 1,116 825 231 124 10 82.5 10.3
15 1,054 825 231 186 15 123.75 10.3
20 992 825 231 248 20 165 10.3
25 930 825 231 310 25 206.25 10.3

2.2 Experiments

A sequence of mortar mixes was created by gradually substituting FA with WR in 5% increments, ultimately reaching a maximum replacement of 25%. The replacement is expected to cause a substantial decrease in the f c ′ of the mortar cubes. To mitigate this expected decline in strength and investigate the potential impacts of several SCMs, GP, MP, and SF were added to the mixtures. In incremental steps, these SCMs were substituted by a fraction of the cement content, ranging from 5 to 25%. The experimental design involved varying the proportions of individual and combined SCMs to assess their impact on the compressive strength of rubberized mortar. The aggregates in the composition were weighed in their dry state and then combined for 2–3 min via a mortar mixer. Initially, the raw ingredients were combined in a mixing container for 2–3 min. Prior to the addition of water, SP and water were combined in a flask. Afterward, 50% of the water quantity was included, and the blend was agitated for 2 min. The leftover 50% water was slowly provided in two consecutive portions; however, the mixer constantly whirled for 2 min. Samples measuring 50 mm on each side were fabricated to perform the f c ′ tests. A total of 408 cubes were produced and assessed, with 3 cubic samples being generated for each of the 136 formulations. After casting the samples, the samples were kept at ambient temperature for 24 h. The cubes were recovered from the molds and immersed in water for the intent of curing. After the curing period, the samples undergo testing for f c ′ at specific intervals, usually 28 days. The f c ′ testing was executed according to the recommendations provided in ASTM C109/C109M-20 [30], using a load-controlled compression testing device with a maximum capacity of 1,000 kN. Figure 1 displays visual depictions of the experimental configuration adopted for this study. The outcomes were carefully documented and examined to determine the influence of WR and other SCMs on the strength characteristics of the mortar cubes. This systematic approach offered a thorough comprehension of the material’s behavior, enabling the creation of improved mortar compositions that are more sustainable and perform better in construction applications.

Figure 1 Experimental flowchart of the study.
Figure 1

Experimental flowchart of the study.

2.3 Modeling

ML algorithms require diverse input data to predict the desired outcome [31] accurately. The f c ′ of mortar cubes encompassing SCMs was estimated utilizing the experimental results. A total of 408 data points were used to develop ML models. The process utilized water, cement, FA, SP, SF, WR, MP, and GP as inputs, resulting in the formation of a final product, f c ′ . The selection of these inputs was based on their availability in varying quantities. Most previous studies have also utilized these input precursors to run ML models [31,32,33]. Other factors, such as the W/C ratio, were not taken into consideration, as it remained constant in all mixtures. ML techniques necessitate using variable inputs to produce an output, and it is impossible to employ constant inputs [34].

Data preprocessing is a fundamental step in developing ML models. It involves several essential tasks, including handling missing data, encoding categorical data, identifying and addressing outliers, and splitting the dataset into training and testing sets [35]. This study considered eight essential input precursors: water, FA, SP, cement, SF, WR, MP, and GP. The output parameter was the f c ′ of samples. The database utilized in this study was relatively clean, with no missing data or obvious outliers. While a formal multivariate outlier detection technique was not explicitly applied, a rigorous data preprocessing strategy was implemented to ensure data quality. This included a univariate outlier analysis on each feature to identify and remove any data points that deviated significantly from the expected range.

The study’s aims were achieved by employing Python code in Orange (version: 3.36.2) and utilizing two novel ML methods. The ML techniques utilized in this research were SVM and RF. The primary purpose of ML approaches is to utilize input variables to generate estimations of desired outcomes. By employing these methodologies, it is possible to approximate the endurance, strength, and capacity to withstand variations in temperature of a given material [36]. While developing the model, the experimental data were divided into a 70% portion for testing and a 30% portion for training. This division was based on information obtained from prior studies [37,38]. The R 2 statistic of the projected result indicates the dependability of the algorithms used. The R 2 quantifies the level of disagreement; a lower number (close to 0) signifies less correlation between the forecasted and the actual results, while a higher value (close to 1) signifies a more remarkable correlation between the forecasted and the actual results [39]. The models subsequently underwent statistical analysis and error evaluation. The findings were presented for root mean square error (RMSE), objective function (OBJ), mean absolute error (MAE), scatter index (SI), mean square error (MSE), Nash Sutcliffe efficiency (NSE), and mean absolute percentage error (MAPE). Figure 2 displays a flowchart illustrating the process of the modeling approach. After the successful implication of ML models, the data were then arranged to study the impact of each variable individually on the f c ′ of rubberized mortar. For this purpose, PDP analysis methodology has been adopted, and results are generated in graphical form.

Figure 2 Procedure of ML techniques for predicting compressive strength.
Figure 2

Procedure of ML techniques for predicting compressive strength.

SVM is a reliable and effective supervised learning algorithm frequently utilized for regression and/or classification functions [39]. SVM is considered a discriminative classifier since it can create a clear boundary within various clusters of data [40]. The intent of utilizing this method is to estimate the target variable by relying on the input values, which are comprised of K-dimensional patterns, x i pattern, and y i outcome, while also utilizing testing and training data [41]. SVM utilizes several K-functions, including radial basis, sigmoid, polynomial, or linear functions, to identify support vectors on a function’s surface throughout the training phase. The K-functions are dependent upon the specific K-class and the software being utilized. The SVM can be mathematically described as in Eq. (1) [42]

(1) f ( x ) = ∑ i = 1 n m i y i K ( o i , x ) + w .

The coefficients attributed to every support vector, denoted as m i , determine their impact on modeling the decision boundary. The core function is represented by K, while o i represents the support vectors.

RF has gained significant interest in various disciplines due to its ability to effectively handle a high number of factors with relatively small quantities of data [43]. After creating the bootstrap sample, a decision tree (DT) is trained using the sample [42]. Once trained, these trees can be employed to create predictions by applying the mathematical expression, as provided in Eq. (2) [44], to assess unidentified samples. This tree predictor utilizes the outcomes of a randomly sampled vector, obtained in an unbiased manner, and applies the same distribution to all trees inside the forest. As the forest’s tree population grows, the generalization inaccuracy will gradually converge toward a limit

(2) f ( x ) = 1 N ∑ i = 1 N f i ( x ° ) ,

where N reflects the total quantity of bootstrap samples and f i ( x ° ) denotes the function employed for every data point in the summation x ° .

2.4 Hyper-tuning of models

Hyperparameters are tunable parameters that regulate the learning process of an ML model. Unlike conventional model parameters like bias and weights, hyperparameters are predetermined before training and cannot be acquired by analyzing the data. They provide essential characteristics of the model, such as its intricacy, learning pace, or kernel size. Optimizing hyperparameters substantially influences a model’s accuracy, generalization, and other performance measures [45]. Both RF and SVM rely on hyperparameters that significantly impact their performance. Key hyperparameters for RF are max-depth (which controls tree depth), n-estimators (the number of trees), max-features, and min-samples-split. The parameters mentioned above are set to limit the RF model’s complexity. For instance, the maximum tree depth is capped at 11, preventing trees from growing beyond this limit. In the SVM model, C (balancing training/testing error), kernel (boundary type), gamma (boundary shape), and degree (for polynomial kernels) govern the activation and performance of the model. However, tolerance and iteration are used as stopping criteria for ML models. The optimal values of tolerance and iteration for the SVM model are provided in Table 2. Proper tuning improves the model’s efficiency and generalization. When creating an ML algorithm, it is essential to select the hyperparameter value that allows for a minimal loss while also providing the highest accuracy [46]. Both ML algorithms were fitted with a set of customizable hyperparameters. Researchers carefully examined and evaluated all potential hyperparameter combinations to gauge the model’s effectiveness. Table 2 shows the optimum settings of the hyperparameters employed in the modeling approach.

Table 2

Hyperparameters adopted for RF and SVM models

Algorithms Hyperparameter Assigned value
RF No. of trees 50
No. of attributes 8
Max. tree depth 11
Min. sample 2
SVM Cost (C) 1.50
Regression loss (ε) 2.40
Kernel Polynomial
Kernel equation g ( x × y + C ) d , g = 0.30, C = 1.50, d = 3.0
Tolerance 0.0010
Iteration limit 10,000

2.5 Validation of models

This study employed several statistical matrices to validate the model, including R 2, MAE, OBJ, MSE, SI, NSE, MAPE, and RMSE. These statistical indicators were selected based on literature studies [33,47,48]. Many past researchers have used these techniques to quantify the performance of ML models. The R 2 matrix quantifies the level of disagreement; a lower number (close to 0) signifies less correlation between the forecasted and the actual results, while a higher value (close to 1) signifies a more remarkable correlation between the forecasted and the actual results. The statistical analysis results indicate that the developed algorithms exhibit a high level of accuracy when the overall errors are lowered. The statistical valuation of the forecasting reliability of rubberized concrete models was carried out using (3)–(9), which were obtained from previous works [39,49,50]

(3) MAE = 1 n ∑ i = 1 n ∣ O i − E i ∣ ,

(4) OBJ = MAE + RMSE R 2 + 1 ,

(5) MSE = 1 n ∑ i = 1 n ( O i − E i ) 2 ,

(6) SI = RMSE E ′ ,

(7) MAPE = 100 1 n ∑ i = 1 n ∣ O i − E i ∣ x i ,

(8) RMSE = ∑ ( O i − E i ) 2 n ,

(9) NSE = 1 − ∑ i = 1 n ( O i − E i ) 2 ∑ i = 1 n ( O i − O i ¯ ) 2 ,

where O i represents the observed values during experimentation, E i represents the estimated values using the ML model, n represents the total number of data points, and E′ represents the mean/average of estimated values.

3 Results and discussion

3.1 Compressive strength of rubberized mortar

The f c ′ of cubes utilizing WR as a replacement for FA was inferior to that of cubes employing conventional materials, denoted by CM, as depicted in Figure 3. The graphic displays bars corresponding to the f c ′ value and the line representing the decline in f c ′ in relation to the CM. The f c ′ of the rubberized mortar exhibits a negative correlation with the rise in rubber content inside the mortar, ranging from 0 to 25%. The graph illustrates a negative correlation between WR content and f c ′ , with each 5% increment in WR proportion resulting in a more pronounced decrease in the f c ′ . The graph illustrates that the initial WR content of 0% exhibits the highest strength value. However, when the WR content increases to 5, 10, 15, and 20%, and ultimately 25%, there is a corresponding fall in f c ′ of 17.5, 25.3, 31.2, 37.6, and 47.3%, respectively. These findings suggest that the inclusion of rubber has a notable detrimental effect on the structural soundness of the mortar. The reduction in f c ′ observed in rubberized mortar as the WR component increases can be ascribed to numerous factors. The specific surface area of larger WR particles is diminished, resulting in a decrease in water absorption by the rubber aggregate. This phenomenon has an impact on the overall W/C and may lead to a diminished strength of the concrete matrix [51]. WR particles exhibit more elasticity in comparison to conventional aggregates. The rubber particle’s elasticity can decrease the concrete’s f c ′ , as it may not efficiently transmit stress during compression [52]. Moreover, WR particles exhibit a lower affinity for the cement paste than conventional aggregates. The limited interfacial transition zone has the potential to result in a decrease in mechanical strength [53]. Hence, rubberized mortar is not recommended in scenarios with a significant emphasis on high-strength performance. Considering the environmental implications associated with the recycling of WR and the conservation of natural FA, it is recommended to employ rubberized mortar for purposes that necessitate reduced load-bearing capability.

Figure 3 Compressive strength of rubberized mortar.
Figure 3

Compressive strength of rubberized mortar.

3.2 Compressive strength of rubberized mortar featuring SF

Figure 4 illustrates a comparative examination of the f c ′ of rubberized mortar, considering different proportions of SF as an alternate for cement and WR as an alternate for FA. The incorporation of SF has been noted to enhance the f c ′ , potentially attributable to its pozzolanic reaction that contributes to a more compact microstructure. Nevertheless, it is worth noting that an increment in the WR proportion is associated with a significant decrease in f c ′ . The observed phenomenon can be ascribed to rubber’s comparatively reduced stiffness and bonding properties in relation to traditional aggregates, resulting in a less rigid matrix and diminished strength. The 5% WR and 15% SF combination strikes an optimal balance between enhanced workability and mechanical performance. However, it is essential to highlight that beyond this threshold, the strength properties of the mortar consistently diminish. At an SF level of 15% in all combinations, the f c ′ showed improvements of around 11.7, 18.1, 18.1, 15.9, and 18.4% at rubber contents ranging from 5 to 25%, with a continuous rise of 5%. This observation underscores the inherent trade-off between durability and flexibility in modified mortar composition. The initial reaction between SF and calcium hydroxide, produced as an outcome of the hydration reaction, leads to the formation of CSH. This hydrate contributes to the improvement of the mortar’s strength and density [54]. Furthermore, once the WR component exceeds the ideal level, there is an increase in the volume of less rigid material inside the matrix. This material does not form strong bonds with the cement paste, decreasing strength [55]. The inclusion of WR results in a decline in the effective cementitious material content per unit volume, hence causing a reduction in particle density and an increase in voids, ultimately leading to a decline in strength. Hence, the results suggest that incorporating 15% SF into the rubberized mortar as a replacement for cement is advisable to attain maximum strength. This replacement additionally helps to augment the material’s environmental sustainability by reducing cement consumption.

Figure 4 Compressive strength of rubberized mortar containing SF as cement replacement.
Figure 4

Compressive strength of rubberized mortar containing SF as cement replacement.

3.3 Compressive strength of rubberized mortar featuring GP

GP was employed as a substitute for cement in varying proportions varying from 5 to 25% with a 5% increase to investigate its influence on the f c ′ of rubberized mortar. Figure 5 presents the f c ′ outcomes derived from the samples customized with GP. The finding suggests that adding GP enhanced the f c ′ compared to the mortar samples based on WR. It has been determined that the optimal substitution level of GP for each rubber ingredient in the mixtures is 15%. When 5% rubber element was utilized as a replacement for cement, the f c ′ increased by roughly 0.8, 4.6, and 8.8, and 12.3% at GP levels of 5, 10, and 15%. At the GP level of 15% in all combinations, the CS showed improvements of around 8.8, 17.0, 13.8, 11.5, and 12.4% at rubber contents ranging from 5 to 25%, with a continuous rise of 5%. This observation underscores the inherent trade-off between durability and flexibility in modified mortar composition. The GP can potentially participate in the pozzolanic process, which reacts with calcium hydroxide to produce CSH, augmenting the mortar’s strength. Tiny GP particles can occupy empty spaces inside cement particles, resulting in a more compact and denser microstructure. This phenomenon plays a significant role in the initial enhancement of strength [56]. An excessive amount of GP might cause a dilution effect, resulting in a thin spreading of the cementitious material and a decrease in the overall f c ′ of the mortar. Additionally, there is a potential for an alkali-silica reaction when using larger quantities of GP, which can result in the formation of microcracks and a gradual decrease in f c ′ [57]. Therefore, the utilization of a maximum of 15% GP as an alternative for cement presents notable advantages in achieving a high f c ′ .

Figure 5 Compressive strength of rubberized mortar containing GP as cement replacement.
Figure 5

Compressive strength of rubberized mortar containing GP as cement replacement.

3.4 Compressive strength of rubberized mortar featuring MP

MP was added as an alternative for binder in rubberized mortar to evaluate its effect on f c ′ . Mortar samples based on WR were fabricated by replacing the cement volume with MP concentrations ranging from 5 to 25%, with an incremental increase of 5%. The findings of the MP-modified rubberized mortar samples are depicted in Figure 6. The research finding indicated that the incorporation of MP led to an enhancement in the f c ′ of rubberized mortar. Maximum f c ′ was attained when 10% of MP was employed as a substitute for cement. By incorporating 5% WR aggregates into the mixtures, the f c ′ increased by roughly 2, 4, and 6.6% at MP proportion ranging from 5 to 15%, with continual increments of 5%. The influence of MP was likewise detected in combinations with higher rubber percentages. The f c ′ increased by 6.6, 8.8, 9.2, 12.8, and 11.2% for samples having 5–25% rubber content, respectively, at a concentration of 10% MP. The observed enhancement in f c ′ upon the incorporation of MP as a substitute for cement and WR as a substitute for FA in mortar can be ascribed to the filler effect and the pozzolanic reaction. MP has the ability to fill up the empty spaces between cement particles, leading to the creation of a more compact microstructure. This, in turn, enhances the initial strength [58]. However, the inclusion of an excessive amount of MP might result in a dilution effect characterized by the dispersion of cementitious material in a thin manner, hence diminishing the overall strength of the mortar. Therefore, utilizing a maximum of 10% MP as a substitute for cement presents notable advantages in achieving high f c ′ .

Figure 6 Compressive strength of rubberized mortar containing MP as cement replacement.
Figure 6

Compressive strength of rubberized mortar containing MP as cement replacement.

3.5 Compressive strength of rubberized mortar encompassing GP, SF, and MP in combinations

The following segment documents the collective influence of industrial garbage, specifically MP, SF, and GP. GP and SF as replacements for cement were initially implemented, with mass proportions of 5–15% for each respective ingredient. Figure 7 illustrates the collective impact of SF and GP. The analysis revealed that the cumulative influence of SF and GP had a virtually comparable effect on the f c ′ of rubberized mortar compared to their individual impacts. The study determined that the most effective concentrations of SF and GP, when used as substitutes for cement in combination, were 10% SF and 10% GP, respectively. For example, when the rubber component accounted for 5% of the total, the f c ′ exhibited an increase from 40.90 to 43.21 MPa when the cumulative proportions of GP and SF were 5 and 10%, respectively. The best combination (10% SF + 10% GP) resulted in a 9.1% increase compared to the rubberized mortar cubes with 5% rubber content. A similarity in the impact of SF and GP was observed when the rubber contents were raised. However, the increase in f c ′ exhibited a relatively diminished trend when the WR content reached 20 and 25%. At the ideal concentrations of 10% GP + 10% SF, the f c ′ of the specimens increased by 15.1% when they contained 10% rubber. Similarly, the f c ′ exhibited enhancements of 18.0, 19.0, and 23.1% at rubber aggregate levels of 15, 20, and 25%, respectively.

Figure 7 Compressive strength of rubberized mortar containing SF and GP as cement replacement.
Figure 7

Compressive strength of rubberized mortar containing SF and GP as cement replacement.

An identical method was employed to evaluate the collective impact of SF + MP and GP + MP. The f c ′ findings for the samples containing MP and SF are depicted in Figure 8. The study found that replacing 10% of SF and 10% of MP increased the f c ′ of rubberized mortar. The maximum f c ′ was achieved while employing a dosage of 10% for both SF and MP. The f c ′ exhibited an increase of about 8.3, 13.6, 13.8, 18.3, and 14.6% in comparison to the mortar samples based on rubber aggregates, which had a rubber content ranging from 5 to 25% with a consecutive increment of 5%. Likewise, similar outcomes were observed when GP and MP were combined in equal percentages, as illustrated in Figure 9. The addition of 10% GP and 10% MP as replacements for cement led to an increase in f c ′ by approximately 5.2, 12.8, 15.9, 10.2, and 8.8% compared to mortar formulations based on WR, which contained 5–25% rubber with a 5% increment, respectively.

Figure 8 Compressive strength of rubberized mortar containing SF and MP as cement replacement.
Figure 8

Compressive strength of rubberized mortar containing SF and MP as cement replacement.

Figure 9 Compressive strength of rubberized mortar containing MP and GP as cement replacement.
Figure 9

Compressive strength of rubberized mortar containing MP and GP as cement replacement.

The effect of all three recyclable materials (GP, MP, and SF) on the f c ′ of rubberized mortar was examined by blending them in 5–10% ratios. Figure 10 demonstrates that adding these recyclables in lower proportions of 5% yielded advantages, while at higher proportions, mainly 10%, the f c ′ declined. Adding 5% of each recyclable item to the mixture resulted in a considerable improvement in the f c ′ of rubberized mortar. Specifically, the f c ′ increased by roughly 5.5, 9.8, 9.4, 13.0, and 9.2% for formulations comprising 5–25% rubber, with a 5% increment of WR, respectively. However, it was shown that when the doses of GP, MP, and SF were fixed at 10%, the f c ′ in cubic samples with higher rubber concentration was approximately equal or potentially even lesser. The results suggest that replacing a fraction of the binder with an amalgamation of GP + SF, GP + MP, and MP + SF is advantageous, with a maximum overall benefit of 20%. Moreover, employing a blend of GP + MP + SF is advantageous, with a maximum overall percentage of 5%. The improvement in f c ′ attained by integrating GP, MP, and SF is ascribed to the impacts of chemical and filler procedures described in the prior divisions. The decrease in f c ′ is ascribed to the cement dilution phenomenon found at higher proportions of GP, MP, and SF, employed singly or in conjunction.

Figure 10 Compressive strength of rubberized mortar containing GP, MP, and SF and as cement replacement.
Figure 10

Compressive strength of rubberized mortar containing GP, MP, and SF and as cement replacement.

3.6 ML model outcomes

3.6.1 SVM model

The findings of SVM models are displayed in Figure 11. The R² value of 0.943 from Figure 11(a) reflects a robust association between the estimated and the actual data, as it is close to 1. To clarify, the model’s estimations account for nearly 94.3% of the variability observed in the experimental data. The graphical representation demonstrates the effectiveness of the SVM model in accurately forecasting the f c ′ of rubberized mortar. This is evident from the high R² value and the close proximity of the data values to the regression line. The error plot in Figure 11(b) demonstrates that the extreme error calculated using the SVM model is 3.46 MPa, whereas the model yields a mean error of 1.13 MPa. Moreover, it has been observed that around 86.52% of absolute errors lay below 2 MPa. The conclusions of the SVM model testify to the utilization of ML methodologies for accurately forecasting the properties of rubbered mortar.

Figure 11 SVM model results (a) regression plot and (b) error plot.
Figure 11

SVM model results (a) regression plot and (b) error plot.

3.6.2 RF model

The findings of RF models are shown in Figure 12. The R 2 value of 0.983 from Figure 12(a) reflects a robust association between the estimated and the actual data, as it is close to 1. To clarify, the model’s estimations account for nearly 98.3% of the variability observed in the experimental data. The graphical representation demonstrates the effectiveness of the RF model in accurately forecasting the f c ′ of rubberized mortar. This is evident from the high R 2 value and the close proximity of the data values to the regression line. The error plot in Figure 12(b) demonstrates that the extreme error calculated using the RF model is 2.70 MPa, whereas the model yields a mean error of 0.61 MPa. Moreover, it has been observed that around 9.51% of absolute errors lay below 2 MPa. The outcomes of the RF model testify to the utilization of ML methodologies for accurately forecasting the properties of rubbered mortar.

Figure 12 RF model results (a) regression plot and (b) error plot.
Figure 12

RF model results (a) regression plot and (b) error plot.

3.6.3 Validation and comparison of models

This research employed an analytical matrix known as the R 2 score to evaluate the prediction capability of established ML structures. The R 2 test indicates that the RF algorithm showed a higher level of accuracy, resulting in the R 2 value of 0.983, compared to the SVM algorithm’s R 2 value of 0.943. The viability of the developed model is further validated using other statistical parameters, including MAE, OBJ, MSE, SI, MAPE, NSE, and RMSE. The findings from the error distribution are displayed in Table 3. The results indicate that the RF algorithm had excellent performance compared to the SVM algorithm, as suggested by the lower values of MAE, OBJ, MSE, SI, MAPE, NDR, and RMSE, which were measured at 0.61 MPa, 0.68 MPa, 0.56 MPa, 0.022, 1.80%, 0.983, and 0.75 MPa, respectively. The SVM model exhibits the following values for the MAE, OBJ, MSE, SI, MAPE, NDR, and RMSE: 1.13 MPa, 1.28 MPa, 1.85 MPa, 0.040, 3.40%, 0.943, and 1.36 MPa, respectively. Additionally, Figure 13 presents the study of MSE, RMSE, and MAE errors. The findings suggest that the error of the RF model encompasses a smaller area compared to the SVM model’s error. Therefore, the plot validates the precision of the RF algorithm in foreseeing the rubberized mortar’s f c ′ .

Table 3

Summary of statistical error

Model MSE (MPa) RMSE (MPa) MAE (MPa) OBJ (MPa) MAPE (%) SI NSE
SVM 1.85 1.36 1.13 1.28 3.40 0.040 0.943
RF 0.56 0.75 0.61 0.68 1.80 0.022 0.983
Figure 13 Graphical representation of statistical errors.
Figure 13

Graphical representation of statistical errors.

RF models excel at identifying intricate, non-linear correlations within the data. This is especially beneficial in forecasting the f c ′ , which may be affected by various interacting variables. RF models, which consolidate the predictions of numerous DTs, generally exhibit greater resilience to overfitting than SVM. The ensemble methodology of RF diminishes the model’s variance [59]. These types of models are typically more scalable and economical in managing extensive datasets and can manage high-dimensional data more efficiently than SVMs, which may become computationally burdensome with extensive feature sets [60].

3.6.4 Partial dependence plots for model insights

After the ML models were created and validated, the data were arranged for PDP analysis. This strategy is commonly utilized to examine the impact of the input variable on the expected output in ML applications. Moreover, individual conditional expectation (ICE) plots, represented by each line, illustrate the outcomes derived from altering each occurrence and examine the corresponding variation in the projected output resulting from that alteration [61]. Consequently, a PDP is derived by calculating the line’s mean generated by ICE plots.

Figure 14(a)–(f) illustrates the PDP analysis correlating cement, FA, rubber, SF, MF, and GF with the early f c ′ of rubberized mortar. The observed input factors favorably influenced the target variable f c ′ . As shown in Figure 14(a), the early f c ′ of rubberized mortar increases with the corresponding increment in cement quantity; however, after 700 kg·m−3, the f c ′ starts declining. Similarly, the strength development can be observed as the FA content increases from 920 to 1,240 kg·m−3. However, replacing WR as a substitute for FA tends to decline the early f c ′ of mortar cubes. The MP, GP, and SF, primarily used as a substitute for cement, had almost an identical effect on the f c ′ of rubberized mortar. At initial substitution levels, i.e., from 0 to 120 kg·m−3, the f c ′ is observed to be increased as well; however, the strength starts declining after further substitution of cement with MP, GP, or SF.

Figure 14 Partial dependence plots: (a) cement, (b) FA, (c) rubber, (d) SF, (e) MP, and (f) GP.
Figure 14

Partial dependence plots: (a) cement, (b) FA, (c) rubber, (d) SF, (e) MP, and (f) GP.

3.6.5 Comparative study with literature

Over the past few decades, AI-based ML models have been widely used to access construction material’s properties, particularly mortar and concrete. Since experimentally accessing the behavior of concrete by modifying it with SCMs consumes time and effort, ML models provide ease in forecasting concrete’s durability and strength-related properties. An overview of past studies on predicting the properties of mortar or concrete modified with industrial waste is provided in Table 4. Table 4 clearly demonstrates that the values of key indicators, such as RMSE and R 2, show a significant similarity between the existing models and the optimal models proposed in this work. Consequently, the proposed models can be efficiently employed to assess rubberized concrete’s f c ′ .

Table 4

Comparison of the suggested models with previous research

Ref. Model Target variable R 2 RMSE
Current study SVM Compressive strength 0.943 1.36
RF 0.983 0.75
[62] Artificial neural network Flexural strength 0.955 0.167
DT 0.986 0.124
[18] Adoptive boosting Compressive strength 0.939 1.519
DT 0.924 1.747
[63] Adoptive boosting Water absorption 0.862 0.370
Multilayer perceptron neural network 0.738 0.517
SVM 0.776 0.461

4 Discussions

In order to neutralize CFP and reduce energy and material consumption, a sustainable industry prioritizes implementing recycling and reuse as significant actions. However, the process of recycling waste at plants requires the use of energy-intensive remelting techniques and modern technologies, which may result in the release of greenhouse gas emissions. The use of industrial waste as pozzolanic or filler substance in cement provides a practical option, reducing the requirement for remelting and the resulting CFP. Utilizing WR and other recyclable products such as GP, MP, and SF in construction presents various benefits in terms of sustainability. Figure 15 presents the inclusive insight of remolding industrial waste to create sustainable materials. This study’s findings highlight the challenges and trade-offs in improving the f c ′ of rubberized mortar by partially substituting FA with WR and replacing cement with different pozzolanic additives, specifically MP, GP, and SF. The results demonstrate that although WR decreases f c ′ , incorporating particular additives in ideal ratios substantially improves this reduction, enhancing both the modified mortar’s structural performance and environmental sustainability. The reduction in f c ′ after adding WR as aggregates is due to the intrinsic characteristics of WR, which is less resilient and possesses a diminished bonding capacity with cement relative to standard aggregates. The absence of stiffness and bonding leads to a less dense microstructure, undermining the mortar’s strength and making rubberized mortar inappropriate for high-strength applications. Nevertheless, the environmental advantages of rubberized mortar in recovering WR and reducing FA render it appropriate for situations where diminished load-bearing capacity is permissible. Adding GP, MP, and SF can be advantageous in enhancing the strength characteristics of rubberized mortar. The outcomes of this study suggest that the tiny structures of MP, GP, and SF fill up the voids at the microstructural level, creating a condensed structure that ultimately boosts the f c ′ of rubberized mortar.

Figure 15 Utilization of industrial waste in the construction industry.
Figure 15

Utilization of industrial waste in the construction industry.

The study’s results have practical significance for the design of rubberized mortar, especially in environmentally sustainable applications where structural strength is not the primary concern. The modified mortar utilizes waste components like WR, GP, SF, and MP, so it aligns with sustainability objectives, decreases cement and FA consumption, and offers a practical recycling option for industrial waste. Nonetheless, limits are present, especially with diminished structural integrity and the risk of alkali-silica reaction in high GP concentrations. Future research may investigate supplementary additives or treatments for WR to improve its compatibility with cement, along with the improved mortar’s long-term durability and alkali-silica reaction resistance.

The authors of this research make efforts to develop ML to accurately predict the f c ′ of rubberized mortar. In order to reduce the effort and resources, ML techniques have widely been used to assess the characteristics of construction materials. The working principle of ML models widely depends on the characteristics of the database. Since data play a crucial role in developing ML models, a single outreach data point can generate false results. Moreover, the strength development of concrete is a complex phenomenon, and many other factors like temperature and humidity, which are not taken into consideration, also affect the f c ′ of concrete. Hence, further research can be conducted on samples by considering these two parameters and formalizing a more descriptive database to compute the f c ′ of rubberized mortar modified with MP, SF, and GP.

5 Conclusions

The objective of this investigation is to determine the effectiveness of industrial waste, including GP, SF, and MP, to improve the compressive strength ( f c ′ ) of mortar made from WR. Experimental testing was carried out to quantify and evaluate the f c ′ of rubberized mortar specimens, including GP, MP, and SF alone and in multiple arrangements. Moreover, the outcome of the experiment was utilized for modeling purposes by employing ML approaches, specifically SVM and RF. The research yielded the following findings:

  • Substituting FA with WR in mortar decreased f c ′ by 17.5–47.3% as WR content increased from 5 to 25% due to insufficient interaction between rubber and cement paste.

  • The utilization of GP, MP, and SF as alternatives for cement in rubberized mortar successfully controlled the decline in f c ′ . The study claimed that the best substitute ratios for GP, MP, and SF in rubberized mortar mixtures, in terms of enhancing strength, were found to be 15, 10, and 15%, respectively.

  • Cement substitution with 15% SF, GP, or MP increased f c ′ by 18.4, 17.0, and 12.8%, respectively, outperforming rubber-only mortars. However, SF yielded the highest f c ′ improvement.

  • GP, SF, and MP had comparable effects on f c ′ . Combining any two at a 10% total replacement rate was optimal at a 20% total replacement ratio. The highest strength was achieved with a 15% total replacement using a 5% blend of all three.

  • The ML algorithms yielded outcomes that strongly correlated with the data collected. The accuracy of RF model outputs was demonstrated to be superior to SVM, as indicated by higher R 2 values and lower error estimates. The RF and SVM models had R 2 values of 0.983 and 0.943, respectively.

  • The mean error computed for the RF model is 0.61 MPa, while for the SVM model is 1.13 MPa. The RF model exhibited reduced errors compared to the SVM model, suggesting that the RF technique provides superior reliability for the f c ′ of rubberized mortar.

  • Partial dependence plots indicate that increasing FA and cement improves early f c ′ while WR reduces it. SF, GP, and MP initially increase strength at lower substitutions but significantly decrease it at higher levels.

The study demonstrates the potential of using waste materials to create a more sustainable rubberized mortar, particularly in applications where structural strength is not critical. While this approach offers environmental benefits, addressing limitations like reduced structural integrity and potential alkali-silica reactions is essential. Future research could focus on improving the compatibility of WR with cement and enhancing the long-term durability of the mortar.

Acknowledgments

The authors acknowledge the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia (Grant No. KFU242921). The authors extend their appreciation for the financial support that made this study possible.

  1. Funding information: This work was supported by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia (Grant No. KFU242921).

  2. Author contributions: M.N.A.: funding acquisition, investigation, supervision, project administration, writing, reviewing, and editing. R-U-D.N.: data acquisition, validation, supervision, visualization, writing, reviewing, and editing. K.K.: conceptualization, funding acquisition, formal analysis, resources, writing, reviewing, and editing. S.U.A.: software, validation, formal analysis, writing-original draft, reviewing, and editing. M.K.: data acquisition, formal analysis, writing-original draft, reviewing, and editing. M.T.Q.: investigation, resources, funding acquisition, writing, reviewing, and editing 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: 2024-09-23
Revised: 2024-11-21
Accepted: 2024-12-04
Published Online: 2024-12-31

© 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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https://doi.org/10.1515/rams-2024-0081
Keywords for this article
rubberized mortar; compressive strength; industrial waste
Creative Commons
BY 4.0

Articles in the same Issue

  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
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  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
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  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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