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Geopolymers have emerged as promising alternatives to traditional cement-based composites, offering enhanced sustainability and opportunities for recycling industrial waste. The incorporation of waste materials into the binding matrix of geopolymer concrete not only promotes environmental benefits but also significantly improves the overall performance, including mechanical strength, durability, and microstructural integrity of the matrix. This study explores the impact of incorporating varying dosages of nano-basic oxygen furnace slag (NBOFS) and nano-banded iron formation (NBIF) on the properties of high-performance geopolymer concrete (HPGC) that utilizes waste glass as 50% fine aggregate. The research focuses on evaluating both the fresh and mechanical properties, including compressive strength, splitting tensile strength, modulus of elasticity, and flexural strength. Additionally, this study investigated the transport properties of concrete under aggressive environments, such as resistance to chloride penetration, sulfate attack, and sorptivity. The microstructure was examined using scanning electron microscopy. The results demonstrated that the addition of 3% NBOFS and 2.5% NBIF significantly improved the fresh, mechanical, and transport properties of HPGC. These nanomaterials also enhance the splitting tensile strength, flexural strength, and elastic modulus under highly aggressive environmental conditions. The contribution of these nanomaterials to the strength and durability of concrete is particularly relevant in the construction of both substructures and superstructures. Additionally, geopolymer concrete significantly reduces CO emissions by eliminating the requirement for ordinary Portland cement and promoting the recycling of waste products, contributing to more environmentally friendly construction practices.

This research examines the efficiency of ultra-high-performance concrete (UHPC) when utilizing geranium plant (GP) ash, which is subjected to different curing temperatures ranging from 300 to 900°C for 3 h of burning time. The GP ash is used as a replacement for cement in varying amounts (10, 20, 30, 40, and 50 wt%). Crumb rubber powder is utilized as a substitute for fine aggregate. Polypropylene fibers have been used to improve concrete performance. The performance of UHPC is evaluated by assessing its mechanical qualities, such as flexural strength, splitting tensile strength, and compressive strength. The sorptivity test is also evaluated as a component of it. Scanning electron microscopy is used to analyze UHPC after exposure to temperatures as high as 900°C. The findings demonstrated a notable enhancement in the mechanical characteristics of all mixtures. The most favorable mixtures were achieved with proportions of 50, 40, 40, and 20% for mixtures including GP waste incinerated at temperatures ranging from 300 to 900°C. Furthermore, the optimal outcome is achieved when 40% substitution is performed at a temperature of 700°C, resulting in notable enhancements of 14% in compressive strength, 30% in flexural strength, and 17% splitting tensile strength, respectively. At a high temperature of 700°C, the decrease in strength increased to approximately 37–40% as a result of the initial removal of carbon dioxide from calcite at temperatures ranging from 600 to 900°C and reached 56% at 900°C. Great resistance to sorptivity, as well as a dense and compact microstructure with a high content of calcium and silicon, was obtained.

The incorporation of fracture mechanics into structural engineering has underscored the importance of fracture energy in understanding crack propagation in quasi-brittle concrete. Nevertheless, conventional laboratory testing remains labor-intensive, time-consuming, and costly. This study applies hybrid machine learning (ML) algorithms, including ANN-GA, SVR-GA, XGB-GA, GBR-GA, and a Stacking Ensemble, to efficiently predict key concrete properties such as initial fracture energy (IFEC), modulus of elasticity (ME), tensile strength (TS), water absorption (WA), and abrasion resistance (AR). Among individual models, IFEC-XGB-GA and IFEC-GBR-GA demonstrated the most stable performance, while the Stacking Ensemble improved overall prediction accuracy and robustness by approximately 5–10  compared with single models. For ME prediction, the ensemble approach reduced bias and variance, although GBR performed better under extreme conditions. TS-ANN-GA exhibited overfitting, whereas TS-Stacking Ensemble achieved the lowest testing errors (MAE0.145, RMSE0.232) and the highest explained variance (≈1.014) and correlation (0.973), confirming the advantages of model aggregation. Moreover, GBR-GA effectively predicted water absorption (R 0.998) and classified abrasion resistance with about 84  accuracy, highlighting strong potential for durability evaluation. A graphical user interface (GUI) was developed to integrate these models, providing engineers with a practical, data-driven, and physics-informed tool for accurately predicting the fracture energy of concrete.

The utilization of waste glass with micro- and nanoparticles in ultra-high-performance concrete (UHPC) has garnered significant interest due to its potential to enhance sustainability and material performance. This study focuses on the implications of integrating microwaste glass (MG) and nanowaste glass in the presence of waste foundry sand and its impact on the properties of UHPC. The particular emphasis of the current work is on compressive strength, tensile strength, sorptivity, and microstructure. It is found that MG enhances compressive strength, decreased tensile strength, reduced sorptivity, and a more compact microstructure. The results indicate that replacing cement with 20% microglass achieves the optimal compressive strength by increasing up to 11.6% at 7 days, 9.5% at 28 days, and 10.18% at 56 days. Nanowaste glass, owing to its increased reactivity and larger surface area, accelerates calcium silicate hydrate formation and improves compressive strength. At the same time, the effective utilization of nanowaste glass improves long-term resilience with an optimum compressive strength at 1.5% replacement ratios of 17.5, 18.9, and 16% at 7, 28, and 56 days, respectively. Splitting tensile strength increased by 16% at 20% MG and 21% at 1.5% nanowaste glass, respectively. Utilizing MG and nanowaste glass in UHPC with waste foundry sand is a promising method for boosting material performance and minimizing environmental impact.

Currently, there is a lack of research comparing the efficacy of machine learning and response surface methods in predicting flexural strength of Concrete with Eggshell and Glass Powders. This research aims to predict and simulate the flexural strengths of concrete that replaces cement and fine aggregate with waste materials such as eggshell powder (ESP) and waste glass powder (WGP). The response surface methodology (RSM) and artificial neural network (ANN) techniques are used. A dataset comprising previously published research was used to assess predictive and generalization abilities of the ANN and RSM. A total of 225 research article samples were collected and split into three subsets for model development: 70% for training (157 samples), 15% for validation (34 samples), and 15% for testing (34 samples). ANN used seven independent variables to model and improve the model, whereas RSM used three variables (cement, WGP, and ESP) to improve the model. The -fold cross-validation validated the generalizability of the model, and the statistical metrics demonstrated favorable outcomes. Both ANN and RSM techniques are effective instruments for predicting flexural strength, according to the statistical results, which include the mean squared error, determination coefficient ( ), and adjusted coefficient ( adj). RSM was able to achieve an of 0.7532 for flexural strength, whereas the accuracy of the results for ANN was 0.956 for flexural strength. Moreover, the correlation between the ANN and RSM models and the experimental data was high. However, the ANN model exhibited superior accuracy.

Concrete compressive strength is usually determined 28 days after casting via crushing of samples. However, the design strength may not be achieved after this time-consuming and tedious process. While the use of machine learning (ML) and other computational intelligence methods have become increasingly common in recent years, findings from pertinent literatures show that the gradient-boosting ensemble models mostly outperform comparative methods while also allowing interpretable model. Contrary to comparison with other model types that has dominated existing studies, this study centres on a comprehensive comparative analysis of the performance of four widely used gradient-boosting ensemble implementations [namely, gradient-boosting regressor, light gradient-boosting model (LightGBM), extreme gradient boosting (XGBoost), and CatBoost] for estimation of the compressive strength of quaternary blend concrete. Given components of cement, Blast Furnace Slag (GGBS), Fly Ash, water, superplasticizer, coarse aggregate, and fine aggregate in addition to the age of each concrete mixture as input features, the performance of each model based on R2, RMSE, MAPE and MAE across varying training–test ratios generally show a decreasing trend in model performance as test partition increases. Overall, the test results showed that CatBoost outperformed the other models with R2, RMSE, MAE and MAPE values of 0.9838, 2.0709, 1.5966 and 0.0629, respectively, with further statistical analysis showing the significance of these results. Although the age of each concrete mixture was found to be the most important input feature for all four boosting models, sensitivity analysis of each model shows that the compressive strength of the mixtures does increase significantly after 100 days. Finally, a comparison of the performance with results from different ML-based methods in pertinent literature further shows the superiority of CatBoost over reported the methods.

The construction sector is quickly adopting 3D printing because of its many benefits, such as the capacity to build complex geometries, speed up timeframes, increase sustainability, and improve safety. Making changes to the mixture composition of 3D-printed fiber-reinforced concrete (3DP-FRC) involves a lot of trial and error due to the many interdependent variables. In order to estimate the compressive strength (CS) and flexural strength (FS) of 3DP-FRC, the present study used gene expression programming (GEP) and Multi expression programming (MEP) for machine learning (ML). We ran a sensitivity analysis to go further into how important the input parameters were. Among the models, MEP had better predictive performance for FS and CS than GEP did, with values of 0.958 and 0.978, respectively. In contrast, the GEP model found lower values of 0.945 for CS and 0.928 for FS. Sensitivity analysis exposed that for CS, water-binder ratio, silica fume, and water content were the most influential parameters, while load distribution, sand content, and fly ash had the highest impact for FS. The developed ML models provide a reliable means of estimating the strength characteristics of 3DP-FRC for sustainable building design based on various input parameter values, offering significant time and cost savings compared to traditional laboratory testing.

Glass powder, silica fume, and marble powder (MP) were investigated for their potential as sustainable additives to enhance mechanical properties, reduce environmental impact, and improve resource utilization in mortar formulations. This study utilized gene expression programming (GEP) and multi-expression programming (MEP) with experimental data to develop flexural strength models using these materials as eco-friendly mortar cement substitutes. The models were evaluated using ² values, statistical tests, sensitivity analysis, partial dependence plots (PDPs), Taylor’s diagram generation, and test and predicted results. The statistical measures demonstrated that MEP was the more accurate model compared to GEP. The sensitivity study revealed that plastic and sand had the most significant influence on flexural strength prediction, emphasizing the importance of their proportions in the mixture. PDPs further showed that cement, silica fume, and MP positively impact flexural strength, while sand and plastic exhibit optimal levels for enhanced performance. The study also highlighted the particle interaction sensitivity of glass powder, underlining the importance of mix design optimization to achieve improved mechanical behavior. The findings support the use of equation-based modeling and sustainable industrial byproducts to optimize mortar formulations, contributing to greener construction practices and reduced dependence on conventional cement.

In today’s digital age, innovative artificial intelligence (AI) methodologies, notably machine learning (ML) approaches, are increasingly favored for their superior accuracy in anticipating the characteristics of cementitious composites compared to typical regression models. The main focus of current research work is to improve knowledge regarding application of one of the new ML techniques, i.e., gene expression programming (GEP), to anticipate the ultra-high-performance concrete (UHPC) properties, such as flowability, flexural strength (FS), compressive strength (CS), and porosity. In addition, the process of training a model that predicts the intended outcome values when the associated inputs are provided generates the graphical user interface (GUI). Moreover, the reported ML models that have been created for the aforementioned UHPC characteristics are simple and have limited input parameters. Therefore, the purpose of this study is to predict the UHPC characteristics while taking into account a wide range of input factors (i.e., 21) and use a GUI to assess how these parameters affect the UHPC properties. This input parameters includes the diameter of steel and polystyrene fibers (µm and mm), the length of the fibers (mm), the maximum size of the aggregate particles (mm), the type of cement, its strength class, and its compressive strength (MPa) type, the contents of steel and polystyrene fibers (%), and the amount of water (kg/m3). In addition, it includes fly ash, silica fume, slag, nano-silica, quartz powder, limestone powder, sand, coarse aggregates, and super-plasticizers, with all measurements in kg/m3. The outcomes of the current research reveal that the GEP technique is successful in accurately predicting UHPC characteristics. The obtained R2, i.e., determination coefficients, from the GEP model are 0.94, 0.95, 0.93, and 0.94 for UHPC flowability, CS, FS, and porosity, respectively. Thus, this research utilizes GEP and GUI to accurately forecast the characteristics of UHPC and to comprehend the influence of its input factors, simplifying the procedure and offering valuable instruments for the practical application of the model’s capabilities within the domain of civil engineering.

This study presents a novel data-driven approach to improving the compressive strength (C-S) of environmentally friendly rubberized mortar that incorporates ingredients that are in line with current sustainability objectives in construction: glass powder, marble powder, and silica fume. Our predictive models were built using state-of-the-art machine learning (ML) approaches, specifically gene expression programming (GEP) and multi-expression programming (MEP), employing a thorough experimental dataset. Thorough evaluations of the models were conducted using important statistical metrics, such as the coefficient, root mean square error, and mean absolute error. The use of individual conditional expectation plots and partial dependence plots allowed for both individual and average variable effect studies, which were conducted to improve interpretability. Despite the good performance of the GEP model ( = 0.91), the MEP model proved to be more effective in capturing complicated, nonlinear connections with its superior accuracy and generalization ( = 0.95). ML has the ability to greatly improve sustainable construction practices by reducing the need for experiments, speeding up the process of mix optimization, and encouraging the creation of cementitious composites that are less harmful to the environment. The findings contribute to the construction sector by integrating digital innovation with material sustainability.