Search Results (246)

Accurate prediction of the mechanical performance of fiber-reinforced cement mortars (FRCM) is challenging because fiber geometry and properties vary widely and interact with the cement matrix in a non-trivial way. In this study, we propose an interpretable, computationally light framework that combines principal component analysis (PCA) with multiple linear regression (MLR) to predict compressive strength (Cs) and flexural strength (Fs) from mix proportions and fiber parameters. The literature-based dataset of 52 mortar mixes reinforced with polypropylene, steel, coconut, date palm, and hemp fibers was compiled and analyzed, covering Cs = 4.4–78.6 MPa and Fs = 0.75–16.7 MPa, with fiber volume fraction V_f = 0–15% and fiber length F_l = 4.48–60 mm. PCA performed on the full dataset showed that PC1–PC2 explain 53.4% of the total variance; a targeted variable-selection strategy increased the captured variance to 73.0% for the subset used for regression model development. MLR models built using PC1 and PC2 achieved good accuracy in the low-to-mid strength range, while prediction errors increased for higher-strength mixes (approximately Cs ≳ 60 MPa and Fs ≳ 10 MPa). On an independent validation dataset (n = 10), the refined model achieved mean absolute percentage errors of 11.3% for Fs and 18.5% for Cs. The proposed PCA-MLR approach provides a transparent alternative to more complex data-driven predictors, and it can support preliminary screening and optimization of fiber-reinforced mortar designs for durable structural and repair applications. Full article

Conventional polycarboxylate superplasticizers (PCEs) suffer from uncontrollable adsorption, characterized by rapid initial uptake and limited subsequent release, which causes pronounced slump loss, particularly at elevated temperatures where hydration accelerates and dispersion efficiency declines. To overcome these limitations, we developed a series of chitosan-based upper critical solution temperature (UCST) responsive superplasticizers (Thermo-PCE_x, UCST = 40–42 °C) capable of temperature -adaptive dispersion during cement hydration. A vinyl-functionalized chitosan macromonomer (uCS-g-T₈) was synthesized by reacting cetyl polyoxyethylene glycidyl ether with chitosan, followed by methacrylate modification, and then copolymerized with acrylic acid and isopentenol polyoxyethylene ether to yield Thermo-PCE_x with tunable sugar-to-acid ratios. The polymers exhibited clear UCST-type phase-transition behavior in aqueous solution. When incorporated into cement paste, Thermo-PCE_x enabled continuous fluidity enhancement at 25 °C (<UCST), with increases of 43.6%, 52.9%, 62.3% and 63.6%, after 180 min for x = 0.5, 1, 1.5 and 2, respectively. Adjusting dosage and composition further regulated setting time, improved rheological stability, and enhanced mechanical strength. These findings demonstrate a viable pathway for designing bio-based, temperature-responsive superplasticizers with self-adaptive dispersibility for sustainable cement technologies. Full article

To address the lack of accurate strength evaluation methods of the TMS concrete, this study focused on establishing a multi-age correlation model between the RS and CS of the TMS concrete. Sixteen groups of the TMS concrete with differentiated mix proportions were designed, and XRF/XRD techniques were used to characterize the chemical and mineral compositions of the TMS. RS and CS tests were conducted on standard cubic specimens at 3 d, 7 d, and 28 d ages, and linear, quadratic polynomial, and exponential functions were adopted for fitting analysis. The optimal model for each age was screened using the coefficient of determination, F-test, Akaike information criterion, and Bayesian information criterion. To verify the model and eliminate size effect interference, a large-scale plate specimen was fabricated for tests. Results showed that the correlation between RS and CS of the TMS concrete varied with age. Linear function was optimal for 3 d, quadratic polynomial function for 7 d, and exponential function for 28 d. All models passed the F-test. The relative errors of the piecewise model in large-scale specimen verification were stably controlled within 5.0%, meeting engineering-allowable error requirements. Crucially, the validation confirmed that the size effect is negligible for TMS concrete components within the investigated mix proportion range, eliminating the need for size correction factors. Consequently, this model can be directly applied to the non-destructive strength testing of TMS concrete prepared with P.O 42.5 Portland cement at 3 d, 7 d, and 28 d ages without the need for parameter adjustment regarding component dimensions. Full article

Reducing CO₂ emissions from cement production is currently one of the major challenges faced by the cement industry. One approach to lowering these emissions is to reduce the clinker factor by incorporating alternative mineral additives into cement. Consequently, there is a growing interest in the use of copper slags (CSs) as supplementary cementitious materials. Therefore, this study investigates the properties of cements containing substantial amounts of copper slag (up to 60%) and, for comparison, the same proportions of granulated blast furnace slag. The inclusion of substantial amounts of CS results both from the lack of studies in this area and from the potential benefits associated with the utilization of larger quantities of copper slag. The chemical, phase, and particle size composition of CS and granulated blast furnace slag added to CEM I 42.5 cement from the Odra cement plant in amounts of 20%, 40%, and 60% by weight were compared. The pozzolanic activity index of the copper slag and the hydraulic activity index of the blast furnace slag were determined. The high pozzolanic activity of the CS was attributed to its high degree of vitrification (nearly 100%). In contrast, the lower hydraulic activity of the blast furnace slag was explained by its lower glass phase content (about 90% by mass). A gradual decrease in the total heat of hydration released within the first two days was observed with increasing slag content in the cement, slightly more pronounced for copper slags. However, at later stages (2–28 days), XRD analysis indicated higher hydration activity in cements containing copper slag, resulting from its strong pozzolanic reactivity. Cements with copper slag also showed slightly lower water demand compared to those with blast furnace slag. An increase in setting time was observed with higher slag content, more noticeable for blast furnace slag. The type and amount of slag in cement reduce both yield stress and plastic viscosity. Greater reductions were observed at higher slag content. Moreover, copper slag caused greater paste fluidity, attributed to the lower amount of fine particles fraction. The addition of slag decreased flexural and compressive strength in the early period (up to 7 days), this reduction being proportional to slag content. After 90 days, mortars containing 20% and 40% copper slag achieved strength values exceeding that of the reference mortar by 4%. In contrast, at a 60% CS content, a 5% decrease was observed, while for cement with 60% BFS the decrease was 11%. This indicates that a lower copper slag content in the cement (40%) is more favorable in terms of strength. Full article

In the background of sustainable development in the construction industry, recycled cement mortar (RCM) has emerged as a research hotspot due to its eco-friendly features, where mechanical properties serve as critical indicators for evaluating its engineering applicability. This study proposes an artificial neural network (ANN) model optimized by intelligent algorithms, including the GWO (grey wolf optimizer), PSO (particle swarm optimization), and a GA (genetic algorithm), to predict the compressive strength of recycled mortar. By integrating experimental and prediction data, we establish a comprehensive database with eight input variables, including the water–cement ratio (W/C), cement–sand ratio (C/S), fly ash content (FA), aggregate replacement rate (ARR), and curing age. The predictive performance of neural network models with different database sizes (database 1: experimental data of RCM; database 2: experimental data of RCM and ordinary mortar; database 3: model prediction data of RCM, experimental data of RCM, and ordinary mortar) is analyzed. The results show that the intelligent optimization algorithms significantly enhance the predictive performance of the ANN model. Among them, the PSO-ANN model demonstrates optimal performance, with R² = 0.92, MSE = 0.007, and MAE = 0.0632, followed by the GA-ANN model and the GWO-ANN model. SHAP analysis reveals that the W/C, C/S, and curing age are the key variables influencing the compression strength. Furthermore, the size of the dataset does not significantly influence the computation time for the above models but is primarily governed by the complexity of the optimization algorithms. This study provides an efficient data-driven method for the mix design of RCM and a theoretical support for its engineering applications. Full article

This research investigates the impact of coarse aggregate (CA) type, shape, and specimen size on the compressive behavior of concrete, aiming to better understand how these factors affect its mechanical performance. Eight concrete mixtures were designed according to four different concrete mix design (CMD) codes using two types of coarse aggregates: crushed basalt and naturally rounded, both with a 15 mm size. A total of 96 concrete samples were tested to evaluate their failure mode, compressive strength (CS), energy accumulation (G_A), and post-peak fracture energy (G_F). The results show that concrete made with basalt CA offered significantly higher CS (by 7% to 40%), G_A (by 34% to 57%), and G_F (10% to 48%) compared to concrete made with natural CA across different CMD codes and specimen dimensions. Larger cylinders demonstrated higher CS than smaller cylinders, ranging from 7% to 19%. The incorporation of basalt CA enhanced the toughness and ductility of concrete, leading to superior post-peak behavior. In addition to the experimental program, four machine learning algorithms, i.e., Extreme Gradient Boosting (XGB), Gradient-Enhanced Regression Tree (GBR), Random Forest (RF), and Support Vector Regression (SVR), were employed to forecast the concrete’s CS. RF (R² = 0.93) and gradient boosting models (R² = 0.92) showed remarkable accuracy, whereas SVR underperformed. The feature importance and SHAP analysis identified cement content and CA type as the primary determinants of CS, while the water–cement ratio served as a crucial regulator. Moreover, a graphical user interface tool was developed to practically allow engineers to rapidly estimate concrete CS, bridging the gap between experimental validation and practical use. Full article

The increasing global production of plastic (P) waste presents a critical environmental challenge, while the construction industry’s demand for sustainable materials continues to grow. The building industry’s reliance on natural aggregates, a contributor to environmental degradation, requires sustainable alternatives. Utilizing plastic waste as a partial aggregate substitute in concrete offers dual advantages: preserving limited resources and redirecting waste from landfills. This research uses advanced machine learning (ML) to forecast the mechanical properties of P waste concrete. Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) models with particle swarm optimization (PSO) were developed to predict compressive and tensile strengths of P waste concrete. A comprehensive dataset comprising 196 datapoints for compressive strength (CS) and 100 datapoints for tensile strength (TS) of P waste concrete was collected from the literature. The input parameters encompassed the plastic (P), cement (C), water-to-cement ratio (W/C), coarse aggregate (CA), fine aggregate (FA), and curing age (Age), while the outputs were CS and TS of P waste concrete. The constructed models were assessed utilizing various statistical metrics. The findings indicate that coefficient of determination of both XGBoost (CS, R² = 0.9911, and TS, R² = 0.9947) and RF (CS, R² = 0.9757, and TS, R² = 0.9737) models performed well, with XGBoost indicating better performance with fewer prediction errors. SHAP analysis emphasizes the substantial effect of P waste on concrete strength properties followed by C and Age. Furthermore, GUIs for predicting TS and CS of concrete containing P waste utilizing both RF and XGBoost models were developed. Overall, this study not only achieves superior accuracy through hybrid PSO-ML models but also contributes to sustainable construction materials and computational material science, offering a data-driven framework for optimizing mix designs that incorporate plastic waste, which can accelerate its adoption in eco-friendly engineering applications. Full article

To address the issue of restricted grout diffusion caused by seepage effects during grouting in sandy soil layers, this study proposes an optimised grouting method for water-based polyurethane-cement composite grout (WPU-CS) under vacuum-pressure synergy. By establishing a porous medium flow model based on the mass conservation equation and linear filtration law, the influence mechanism of cement particle seepage effects was quantitatively characterised. An orthogonal test (L₉(3⁴)) optimised the grout composition, determining the optimal parameter combination as the following: water-to-cement ratio 1.5:1, polyurethane-to-cement ratio 5~10%, magnesium aluminium silicate content 1%, and hydroxypropyl methylcellulose content 0.15%. Vacuum permeation grouting tests demonstrated that compared to pure cement slurry, WPU-CS reduced filter cake thickness by 80%, significantly suppressing the leaching effect (the volume fraction $\delta$ of cement particles exhibited exponential decay with increasing distance $r$ from the grouting end, and the slurry front velocity gradually decreased). Concurrently, the porosity $\varphi$ in the grouted zone showed a gradient distribution (with more pronounced porosity reduction near the grouting end). When vacuum pressure increased from −10 kPa to −30 kPa, slurry diffusion distance rose from 11 cm to 18 cm (63.6% increase). When grouting pressure increased from 20 kPa to 60 kPa, diffusion distance increased from 8 cm to 20 cm (150% increase). The study confirms that synergistic control using WPU-CS with moderate grouting pressure and high vacuum effectively balances seepage suppression and soil stability, providing an innovative solution for efficient sandy soil reinforcement. Full article

Ion exchange resins are commonly utilized for treating liquid radioactive waste within nuclear power plants; however, the disposal of these waste resins presents a new challenge. In this study, magnesium silicate hydrate cement (MSHC) was used to immobilize the waste resin, and the immobilization effectiveness of the MSHC-solidified body were assessed by mechanical properties, durability, and leaching performance. Hydration heat, X-ray diffraction (XRD), thermogravimetric analysis (TGA), scanning electronic microscopy (SEM), and mercury intrusion porosimetry (MIP) were used to study the hydration process of the MSHC-solidified body containing Cs⁺, Sr²⁺, and Cs⁺/Sr²⁺ waste resins. The results demonstrated that the presence of waste resins slightly delayed the hydration reaction process of MSHC and reduced the polymerization degree of the M-S-H gel, and the composition of the hydration products were not changed. The immobilization mechanism for radionuclide ions in resin included both mechanical encapsulation and surface adsorption, and the leaching of Cs⁺ and Sr²⁺ from MSHC-solidified body followed the FRDIM. When the content of the waste resin was 25%, the MSHC-solidified body exhibited satisfactory compressive strength, freeze-thaw resistance, soaking resistance, and impact resistance. These results strongly indicated that MSHC possessed the ability to effectively immobilize ion exchange resins. Full article

Recycled aggregate concrete (RAC) is a sustainable alternative to conventional concrete, reducing environmental hazards and conserving resources. Accurate compressive strength (CS) prediction is critical for its broader acceptance. This study uses machine learning (ML) models (elastic net regression, KNN, ANN, SVR, RF, XGBoost, CatBoost, symbolic regression, stacking) trained on 1030 conventional concrete mixtures from UCI to support RAC’s CS prediction. The best model achieved R² = 0.92; performance order: CatBoost > XGBoost > RF > SVR > ANN > symbolic regression > KNN > elastic net regression. Stacking improved RMSE by 6% over CatBoost. During the testing, sensitivity analysis revealed that CS exhibits pronounced sensitivity to the cement (C) content and testing age (TA). This aligns with existing experimental research. External validation, which is often neglected by prediction model research, was performed, from which a high-quality evaluating model was used for generalizability and reliability, enhancing the heterogenicity of its usefulness. Lastly, a user-friendly graphical interface was developed that allows users to input custom parameters to obtain sustainable RAC mixtures. This study offers insights into optimizing concrete mix designs for RAC, improving its performance and sustainability. It also advances the knowledge of cementitious materials, aligning with industrial and environmental objectives. Full article

Purpose: The present study evaluates the cytocompatibility of chitosan (CS)-modified glass ionomer cement (GIC) diluents for a Balb/c 3T3 fibroblast cell line. Methods: Three different commercially available hand-mix GIC materials were used in this experiment: Fuji IX GP, Ketac Universal, and Riva Self Cure. The diluents for cell viability tests were produced from DMEM exposed to sterile CS-modified glass ionomer material specimens for three different time periods (0–1, 1–7, and 7–21 days). The resultant diluents were exposed to a 3T3 fibroblast cell line using the indirect contact technique in 96-well plates. In order to assess the physical cell response, five material specimens (1 mm high and 3 mm in diameter) of each material (n = 45) were produced and 3T3 cells were seeded on the specimens. SEM evaluation of the cells was conducted. Results: All the Ketac Universal materials resulted in a decrease in cell viability on day 1. Fuji IX and the CS-modified GICs are the most consistent regarding cell viability. None of the CS-modified GICs exhibited improved cumulative cell biocompatibility. Conclusion: Two materials—Riva Self Cure modified with 5% and 10% CS—retained a decreased cell viability at day 21 compared to the viability of 3T3 cells exposed to the control DMEM. Full article

This study aims to develop a high-performance grouting material for mine goaf backfilling, creating a green and low-carbon cementitious alternative by utilizing coal gangue and sludge as the primary precursors. Based on an orthogonal experimental design, the effects of four factors including the coal gangue/sludge ratio, activator modulus, water–binder ratio, and sodium-to-aluminum ratio on the compressive strength of the geopolymer were systematically investigated. The mineral composition and microstructure of the geopolymer were analyzed using microscopic test methods such as XRD and SEM. The test results indicate that the water–binder ratio has the most significant effect on the polymerization performance of the coal gangue/sludge-based geopolymer (CSG), with compressive strength increasing as the water–binder ratio decreases. The Ca²⁺ provided by the sludge to the reaction system directly promotes the formation of new calcium-containing products such as anorthite and calcium silicate hydrate, which play an important role in improving the strength of geopolymers. Moreover, the developed CSG exhibits a significantly lower carbon footprint compared to conventional cement-based grouting materials, aligning with the goals of sustainable and green construction. When the coal gangue/sludge ratio is 7:3, the water–binder ratio is 0.3, the sodium-to-aluminum ratio is 0.64, and the activator modulus is 1.0, the 3-day compressive strength (CS) of the geopolymer reaches 34.5 MPa, demonstrating its potential as an effective and environmentally friendly grouting material for goaf applications. Full article

The development of sustainable concrete capable of trading off the mechanical performance and cost remains a persistent scientific and engineering challenge. Although previous research has employed multi-objective optimization for binary and ternary cement blends, the simultaneous optimization of quaternary-blended systems, incorporating multiple supplementary cementitious materials, has received little systematic attention. This study addresses this gap by introducing an interpretable artificial intelligence (AI)-driven approach that integrates the Category Boosting (CatBoost) algorithm with the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to model and optimize the compressive strength (CS) and total cost of quaternary-blended concretes. A curated database of 810 experimentally documented mixtures was used to train and validate the model. CatBoost achieved superior predictive performance (R² = 0.987, MAE = 1.574 MPa), while Shapley additive explanations identified curing age, water-to-binder ratio, and Portland cement content as the dominant parameters governing CS. Multi-objective optimization produced Pareto-optimal elite mixtures achieving CS of 51–80 MPa, with a representative 60 MPa mix requiring approximately 62% less cement than conventional designs. The findings establish a scientifically grounded, interpretable methodology for data-driven design of low-carbon, high-performance concretes and demonstrate, for the first time, the viability of AI-assisted multi-criteria optimization for complex quaternary-blended systems. This framework offers both methodological innovation and practical guidance for implementing sustainable construction materials. Full article

The conversion of rough marble blocks into building products is environmentally intensive in terms of energy and water consumption and the generation of solid fragments and marble sludge (MS). This LCA study evaluates the environmental impact of two marble processing plants (for sawing and cutting) with respect to alternative scenarios of MS management including its (a) land disposal (baseline scenario—BS), (b) land disposal after filter pressing (current scenario—CS) and (c) partial valorization in cement mortars associated with the application of solar energy (eco-friendly scenario—ES). In this context, a “gate-to-gate” methodology is applied, while three main steps are considered: the sawing and cutting of marble blocks (main process) and the MS disposal and reuse. The LCA results indicate that terrestrial acidification (TAP), freshwater eutrophication (FEP), climate change and ozone depletion decreased by 10.8 to 37.1% by the adaptation of the BS and by 18 to 38.2% by the adaptation of the ES. At the same time, cumulative energy demand increases by 25.3% and 28.9%, respectively. The contribution analysis showed that the main process has the dominant effect on the examined categories. The contribution of the disposal step on TAP and FEP decreased by 61.6% and 47.9% via the application of the valorization technique. Full article

The rapid development in building construction has stimulated the replacement of cement in concrete with construction waste materials such as marble waste powder (MWP) and granite waste powder (GWP) to reduce the negative impact of cement production and to save natural resources. Therefore, the inclusion of these materials in concrete contributes to environmental sustainability by reducing cement consumption and promoting the reuse of industrial waste. The present study employs Response Surface Methodology (RSM) and, for the first time in a comparable context, the Neural Network Algorithm (NNA) as an advanced optimization and predictive tool to evaluate the synergistic effect of using GWP and MWP as partial cement replacements in concrete exposed to elevated temperatures. The study involved four independent variables: replacement level of GWP up to 9%, replacement level of MWP up to 9%, the degree of temperature (T) up to 800 °C, and the exposure duration (D) up to 2 h, while the dependent variable (output) was the compressive strength (CS). The ANOVA results revealed that the quadratic model outperformed the linear model in predicting the CS of concrete. The Quadratic model, derived from RSM, demonstrated superior performance in predicting CS values. However, the NNA model also showed high predictive accuracy (R² = 0.949; RMSE = 1.5297 MPa), effectively capturing the complex and nonlinear relationships among temperature, duration, and the cement replacement levels with GWP and MWP. The optimization results revealed that the maximum compressive strength of 39.4 MPa can be achieved at 8.92% GWP, 1.89% MWP, T of 247 °C, and D of 0.64 h with a desirability of 1. The proposed models provided valuable insights into the synergistic effects of granite and marble waste powders, supporting the design of sustainable, high-performance concrete with reduced environmental footprint and improved resource efficiency. Full article