Search Results (101)

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

The increasing demand for sustainable construction materials has motivated the optimization of mortar mix designs to reduce cement consumption and its environmental impact while maintaining adequate mechanical performance. This study develops a machine learning (ML) model for optimizing mortar mixtures used in concrete roofing tiles by integrating aggregate particle packing techniques with non-linear regression algorithms, using an industry-grade dataset generated in the Central Laboratory of Wienerberger Ltd. Unlike most previous studies, which mainly focus on compressive strength, this research targets the transverse strength of industrial roof tile mortar. The proposed approach combines Tarantula Curve gradation limits, experimentally derived packing density ($\eta$), and ML regression within a unified and application-oriented workflow, representing a research direction rarely explored in the literature for optimizing concrete mix transverse strength. Fine concrete aggregates were characterized through a sand sieve analysis and subsequently adjusted according to the Tarantula Curve method to optimize packing density and minimize void content. Physical properties of cements and fine aggregates were assessed, and granulometric mixtures were evaluated using computational methods to calculate fineness modulus summation (FMS) and packing density. Mortar samples were tested for transverse strength at 1, 7, and 28 days using a three-point bending test, generating a robust dataset for modeling training. Three ML models—Random Forest Regressor (RFR), XG-Boost Regressor (XGBR), and Support Vector Regressor (SVR)—were evaluated, confirming their ability to capture nonlinear relationships between mix parameters and transverse strength. The analysis of input variables, which consistently ranked as the highest contributors according to impurity-based and permutation-based importance metrics, revealed that the duration of curing, density, and the summation of the fineness modulus significantly influenced the estimated transverse strength derived from the models. The integration of particle size distribution optimization and ML demonstrates a viable pathway for reducing cement content, lowering costs, and achieving sustainable mortar mix designs in the tile manufacturing industry. Full article

To promote the sustainable utilization of desert sand as a regional resource in the infrastructure construction of saline-alkali areas, this paper proposes an accelerated test method based on the coupling of an external electric field (60 V) and a 2% Na₂SO₄ solution for rapid evaluation of its sulfate erosion resistance. The optimal mix proportion (FA 10%, water-to-binder ratio 0.33, cement-to-sand ratio 1:1.5, SF 10%) was determined through orthogonal experiments. By employing multi-scale analytical techniques including electrochemical impedance spectroscopy (EIS), scanning electron microscopy (SEM), X-ray diffraction (XRD) and thermal analysis (TG-DTG), the differentiated deterioration mechanisms driven by the electric field were systematically revealed. The results show that the charge-transfer resistance (Rct) decreased by about 95% within 28 d, demonstrating the characteristic of “micro-scale deterioration preceding macro-scale strength loss.” The anode region was dominated by dissolution of hydration products (porosity 5.1%), while the cathode region, due to enrichment of sulfate ions (S content 3.37 wt.%), generated a large amount of expansive products, leading to more pronounced structural damage (porosity 8.3%) and greater mass loss (cathode 12.56% > anode 9.85%). This study not only elucidates the deterioration mechanisms of desert sand concrete under coupled environmental action, but also provides a mechanism-explicit, rapid and efficient laboratory evaluation method for its sulfate resistance, offering practical guidance for durability design and prevention in engineering structures exposed to saline-alkali conditions. Full article

Recycling industrial solid waste phosphogypsum into phosphogypsum concrete (PGC) is a crucial pathway for achieving high-value solid waste utilization. However, the scarcity of experimental samples for PGC has led to inaccurate predictions of compressive strength by traditional models, severely hindering its application. This study proposes a dynamic weighted transfer learning-based method for predicting the strength of PGC, addressing the characterization bottleneck under small-sample conditions by transferring knowledge from the strength patterns of conventional concrete. First, feature differences between conventional concrete and PGC are eliminated through component proportion normalization and feature alignment. Then, a data augmentation technique based on Bootstrap Resampling is developed to generate enhanced samples that comply with mix proportion constraints, effectively expanding the training samples. Finally, an error feedback-driven dynamic weight calculation and weighted loss optimization framework for transfer learning is designed, prioritizing the learning of samples in the prediction blind spots of the target domain. This enables the adaptive acquisition of PGC-specific knowledge while inheriting the general knowledge of conventional concrete. Experimental results show that the transfer learning model achieves a prediction accuracy of R² = 0.95 on the target domain test samples, a 15.9% improvement over traditional methods, while maintaining robust performance (R² = 0.97) on an external validation samples. Feature importance analysis and Shapley Additive Explanations (SHAP) analysis reveal the nonlinear coupling effects of PGC-specific parameters on strength. This study establishes a scientific approach for accurate strength prediction of PGC under small-sample conditions. Full article

This study examines the effectiveness of pervious concrete pavements as a sustainable and cost-effective stormwater management technique, particularly by incorporating locally sourced recycled materials into their design. It evaluates the stormwater treatment potential of three pervious concrete pavement types incorporating recycled plastic, glass, and crushed concrete aggregates, with six design variations produced using 25% and 50% replacements of coarse aggregates from these materials. The key properties of pervious concrete, namely compressive strength, porosity, unit weight, and infiltration, and key water quality indicators, namely pH, electrical conductivity (EC), total suspended solids (TSS), colour, turbidity, chemical oxygen demand (COD), nitrate (NO₃⁻), and orthophosphate (PO₄³⁻), were analysed. Results indicated an overall improvement in the quality of the stormwater runoff passed through all pervious concrete pavements irrespective of composition. Notable reductions in turbidity, TSS, colour, COD, PO₄³⁻, and NO₃⁻ underscored the effectiveness of pervious concrete containing waste materials in the treatment of stormwater runoff. Pervious concrete pavements with 25% recycled concrete exhibited optimal performance in reducing TSS, COD, and PO₄³⁻ levels, while the 50% recycled concrete variant excelled in diminishing turbidity. However, the study found that the use of recycled materials in pervious concrete pavements affects properties like compressive strength and infiltration rate differently. While incorporating 25% and 50% recycled concrete aggregates did not significantly reduce compressive strength, the effectiveness of stormwater treatment varied based on the mix design and type of recycled material used. Thus, this study highlights the potential of utilizing recycled waste materials in pervious concrete pavements for sustainable stormwater management. Full article

The pore structure of SCC and of all cement-based materials plays a crucial role on the mechanical and durability characteristics of the material. The pore structure is affected by mix design, water–binder ratio and the incorporation of SCM and/or nanomaterials, all of which can improve mechanical and durability characteristics by decreasing porosity. Computed tomography (CT) is a powerful, non-destructive imaging technique to investigate the internal pore structure of concrete. The main advantage compared to other investigation techniques used to assess the pore structure is in terms of sample size. More specifically, industrial CT can be used to scan large concrete samples and be able to assess the internal pore structure without damaging the specimen. CT provides accurate measurements of pore diameters, volumes and shapes and enables the assessment of the total porosity. The paper presents the results of an experimental program on the characterization of self-compacting concrete (SCC) at a very long age (7 years) in terms of static and dynamic elastic properties and compressive and splitting tensile strength, all of which are correlated with the internal pore structure assessed via the use of an industrial Nikon XTH 450 CT. The results highlight the influence of pore volume, maximum pore diameter and sphericity on the strength and elastic properties of SCC at the age of 7 years. Both the compressive strength and the static modulus of elasticity values tend to decrease with the increase in the internal total porosity, with stronger influence on the former. Full article

River sediments have attracted increasing attention as alternative raw materials for sustainable cementitious materials due to their abundant availability and silica–alumina-rich composition. In this study, a systematic literature search was conducted in Web of Science and Google Scholar using combinations of the keywords “river sediment,” “cementitious materials,” “activation,” and “pozzolanic activity,” covering publications up to July 2025. In addition, a citation network tool (Connected Papers) was employed to trace related works and ensure comprehensive coverage of emerging studies. This review systematically examines the properties of river sediments from diverse regions, along with activation and modification techniques such as alkali/acid activation, thermal calcination, and mechanical milling. Their applications in various cementitious systems are analyzed, with mix design models compared to elucidate the effects of replacing fine aggregates, coarse aggregates, and cement on workability, strength, and durability. Multi-scale characterization via XRD, FTIR, and TG-DSC reveals the mechanisms of C–S–H and C–A–S–H gel formation, pore refinement, and interfacial transition zone densification. The review highlights three key findings: (1) moderate sediment replacement (20–30%) improves strength without compromising flowability; (2) alkali–water glass activation and calcination at 600–850 °C effectively enhance pozzolanic activity; and (3) combining the minimum paste thickness theory with additives such as water reducers, fibers, or biochar enables high-performance and low-carbon concrete design. This review provides a comprehensive theoretical foundation and technical pathway for the high-value utilization of river sediments, carbon reduction in concrete, and sustainable resource recycling. Full article

In this study, the synergistic effects of a combination of nano additives (nano-clay (NC) and nano-silica (NS)) on the compressive strength (CS) of concrete exposed to temperatures ranging between 25 °C and 800 °C were modeled with two machine learning (ML) techniques: extreme gradient boosting (XGB) and random forest (RF) algorithms. A dataset comprising 169 compressive strength results (using four input parameters: NC dose, NS dose, temperature, and duration) was utilized for the raw data for the prediction models. The results indicated the superior performance of the XGB model in terms of the high accuracy attained in the prediction and the few errors present. Furthermore, SHAP analysis demonstrated that temperature has the highest negative impact on the prediction of the CS of nano-modified concrete. The individual conditional expectation (ICE) with partial dependence plots (PDPs) demonstrated that the optimum doses of NS and NC, leading to maximum compressive strength, were (2~3%) and (5~6%) by weight of cement. The developed models can be used as tools for optimizing mix designs to enhance fire resistance, thereby contributing to more durable and sustainable concrete construction and reducing the need for costly experimental trials. Full article

Vertical containment barriers—critical for intercepting contaminant transport in subsurface environments—demand materials that balance low permeability with adequate strength, particularly in stress-sensitive mountainous terrain. Plastic concrete, as a key barrier material, provides essential properties, including exceptional stress relaxation, to suppress fracture development under compressive loads, coupled with effective seepage control. This study examines its strength performance through experiments on varied mixing techniques (dry, wet, and 24 h hydration), unconfined compression under uncontaminated conditions (water–binder ratios: 1.3–2.1, bentonite content: 20–30%, ages: 14–90 days), barium ion immersion (1–5 g/L, pH 7–11), and dry–wet cycling (10 cycles). Key findings demonstrate that (1) the strength of samples prepared by dry mixing and wet mixing is lower than that of samples mixed for 24 h, and all specimens met the target design strength following 28 days of curing; (2) under pollution-free conditions, strength decreases with higher water–binder ratios and bentonite content, showing a linear relationship. Strength increases exponentially with age; (3) in the presence of Ba²⁺, strength gradually decreases as Ba²⁺ concentration and pH increase, particularly notably at 3 g/L Ba²⁺ and pH 11. Strength increases with age, following a power relationship; (4) under dry–wet cycles, ion concentration has minimal impact on sample quality and surface state but significantly affects strength, with higher ion concentrations leading to greater strength loss and susceptibility to cycles; (5) during solution immersion, higher ion concentrations and pHs result in greater strength loss and worse erosion resistance. Full article

This study develops a novel geopolymer foamed concrete using coal gangue and slag as precursors, along with a composite alkali activator comprising sodium silicate and sodium hydroxide, based on the physical foaming method. The Box–Behnken Design within Response Surface Methodology was applied to optimize the mix proportions of coal gangue–slag-based geopolymer foamed concrete. The effects of alkali activator dosage, sodium silicate modulus, water-to-binder ratio, and foam content on 28-day compressive strength and thermal conductivity were systematically investigated to determine the optimal mix for achieving a balance between mechanical and thermal performance. Scanning Electron Microscopy and other characterization techniques were used to analyze the microstructural features. The results show that foam content has the most significant influence on both mechanical and thermal performance, while the interaction between sodium silicate modulus and foam content exhibits the most pronounced combined effect. The optimized mix design consists of 9.1% alkali activator dosage, a sodium silicate modulus of 1.07, a water-to-binder ratio of 0.44, and foam content of 50%, resulting in a 28-day compressive strength of 2.30 MPa and thermal conductivity of 0.0781 W/(m·K). The observed performance enhancement is primarily attributed to the increased heterogeneity in the pore structure. This study provides theoretical and technical support for the development of integrated thermal insulation and load-bearing wall materials suitable for severely cold regions. Full article

The growing demand for innovation and the use of diverse materials in cementitious composites necessitate predictive models that account for material variability. Numerical, code-based, and machine learning (ML) models have been developed to predict various concrete properties. However, their accuracy is significantly influenced by factors such as mix design, composition, intrinsic properties, and external conditions. Developing robust models that integrate these variables is essential for improving predictive accuracy and optimizing material performance. This paper presents a comprehensive review of numerical, code-based, and ML modelling techniques for predicting both fresh and long-term concrete properties. Since both numerical and ML models rely on experimental data—either to determine coefficients in numerical approaches or to train ML models—data gathering, preprocessing, and handling are crucial for model performance. Previous studies indicated that data variability significantly impacts accuracy, emphasizing the importance of effective preprocessing. While larger datasets generally improve reliability, some models achieve high accuracy even with very limited data. This review not only demonstrates the superior performance of ML models over traditional numerical approaches but also highlights the relative effectiveness of different ML algorithms based on reported accuracy metrics. ML-based approaches, including both ensemble and non-ensemble models, have exhibited strong predictive capabilities across a wide range of concrete property categories. In contrast, traditional numerical models often yield lower accuracy, although modified versions that incorporate additional parameters have shown improved performance. Furthermore, the integration of optimization algorithms and interpretability tools enhances both predictive reliability and model transparency—critical aspects that are often overlooked. Full article

The following investigation presents concrete cement obtained with the Viterbo O’Reilly Diaz method, introduced to quantify the concrete mixture by using an aggregate gradation method. This research uses this procedure to decrease the amount of cement in the mix, thus reducing the CO₂ footprint and production costs, which directly impact the environmental and economical sustainability of the material. The formulations used structural and general use Portland cements. As aggregates, fine sand and 3/4” gravel were included. Several characterization techniques were used, including granulometry testing for the aggregates, compression strength testing for the concrete samples, and granulometry testing for the raw materials. Compressive tests were conducted on samples after 28 days of curing, while scanning electron microscopy (SEM) with energy-dispersive spectroscopy (EDS) was used to understand the microstructure. The results revealed the optimal amounts of water, cement, and aggregates. Combinations of fine and coarse aggregates were determined as well. The main novelty in this manuscript is the use of the Viterbo O’Reilly mix design method to innovatively enhance concrete mixes by analyzing material properties and behavior in detail, an unexplored method in the literature. This research considers not only strength but also durability and workability, using mathematical tools for data analysis. This data-driven approach ensures effective aggregate gradation towards sustainability when compared to other traditional methods. Full article

Cement is widely used as the primary binder in concrete; however, growing environmental concerns and the rapid expansion of the construction industry have highlighted the need for more sustainable alternatives. Geopolymers have emerged as promising eco-friendly binders due to their lower carbon footprint and potential to utilize industrial byproducts. Geopolymer mortar, like other cementitious substances, exhibits brittleness and tensile weakness. Basalt fibers serve as fracture-bridging reinforcements, enhancing flexural and tensile strength by redistributing loads and postponing crack growth. Basalt fibers enhance the energy absorption capacity of the mortar, rendering it less susceptible to abrupt collapse. Basalt fibers have thermal stability up to about 800–1000 °C, rendering them appropriate for geopolymer mortars designed for fire-resistant or high-temperature applications. They assist in preserving structural integrity during heat exposure. Fibers mitigate early-age microcracks resulting from shrinkage, drying, or heat gradients. This results in a more compact and resilient microstructure. Using basalt fibers improves surface abrasion and impact resistance, which is advantageous for industrial flooring or infrastructure applications. Basalt fibers originate from natural volcanic rock, are non-toxic, and possess a minimal ecological imprint, consistent with the sustainability objectives of geopolymer applications. This study investigates the mechanical and thermal performance of a geopolymer mortar composed of metakaolin and red mud as binders, with basalt powder and limestone powder replacing traditional sand. The primary objective was to evaluate the effect of basalt fiber incorporation at varying contents (0.4%, 0.8%, and 1.2% by weight) on the durability and strength of the mortar. Eight different mortar mixes were activated using sodium hydroxide (NaOH) and sodium silicate (Na₂SiO₃) solutions. Mechanical properties, including compressive strength, flexural strength, and ultrasonic pulse velocity (UPV), were tested 7 and 28 days before and after exposure to elevated temperatures (200, 400, 600, and 800 °C). The results indicated that basalt fiber significantly enhanced the performance of the geopolymer mortar, particularly at a content of 1.2%. Specimens with 1.2% fiber showed up to 20% improvement in compressive strength and 40% in flexural strength after thermal exposure, attributed to the fiber’s role in microcrack bridging and structural densification. Subsequent research should concentrate on refining fiber type, dose, and dispersion techniques to improve mechanical performance and durability. Examinations of microstructural behavior, long-term durability under environmental settings, and performance following high-temperature exposure are crucial. Furthermore, investigations into hybrid fiber systems, extensive structural applications, and life-cycle evaluations will inform the practical and sustainable implementation in the buildings. Full article

Recycling construction and demolition (C&D) waste into recycled aggregate (RA) and recycled aggregate concrete (RAC) is conducive to natural resource conservation and industry decarbonization, which have been attracting much attention from the community. This paper aims to present a synthesis of recent scientific insights on RA and RAC by conducting a systematic review of the latest advances in their properties, test techniques, modeling, modification and improvement, as well as applications. Over 100 papers published in the past three years were examined, extracting enlightening information and recommendations for engineering. The review shows that consistent conclusions have been drawn about the physical properties in that RA can reduce the workability and the setting time of fresh RAC and increase the porosity of hardened RAC. Its impact on drying and autogenous shrinkage is governed by its size and the strength of the parent concrete. RA generally acts negatively on the durability and mechanical properties of concrete, but such effects remain controversial as many opposite observations have been reported. Apart from the commonly used multiscale test techniques, real-time monitoring also plays an important role in the investigation of deformation and fracture processes. Analytical models for RAC were usually modified from the existing models for NAC or established through regression analysis, while for numerical models, the distribution of attached mortar should be considered to improve their accuracy. Machine learning models are effective in predicting RAC properties. Modification of RA can be implemented by either removing or strengthening the attached mortar, while the modification of RAC is mainly achieved by improving its microstructure. Current exploration of RAC applications mainly focuses on the optimization of concrete design and mix procedures, structural components, as well as multifunctional construction materials, revealing the room for its further exploitation in the industry. Full article

Addressing the shortcomings of currently available concrete reinforcement techniques, a new method using sprayed Glass Fibre Epoxy Mortar (GFEM) reinforcement is proposed. To investigate the effect of this method on the frost durability of concrete, a total of 156 specimens in four groups were designed, and related freezing and thawing cycle tests were conducted. The apparent morphology, mass loss rate, ultrasonic velocity, freeze–thaw damage, and strength loss rate of each group of specimens after different freeze–thaw cycles were analysed comparatively. The test results show that the concrete specimens reinforced with GFEM have a better mass loss rate after freeze–thaw cycles and ultrasonic wave velocity than the unreinforced concrete specimens. The compressive strength of specimens in group A is 24.04 MPa, and the compressive strengths of specimens in groups B, C, and D are 35.28 MPa, 35.73 MPa, and 36.37 MPa, respectively, which is higher than that of group A by 46.76%, 48.63%, and 51.29%, respectively, and 46.76%, 48.63%, and 51.29% higher than group A, respectively. It can be seen that the concrete specimens reinforced with sprayed Glass Fibre Epoxy Mortar can effectively improve the frost durability of concrete; the reinforcing effect is obvious, and in a certain range of fibre mixing, the larger the better the frost resistance. The integration of GFEM is cost-effective and improves viscosity, and the best glass fibre mix percentage is about 0.8%. A freeze–thaw damage model for GFEM-reinforced concrete was developed using the Weibull distribution theory, and an improved strength attenuation model under freeze–thaw cycles was established. By correlating the strength attenuation model with the freeze–thaw damage model, a damage evolution equation for the reinforced specimens was formulated, allowing for the prediction of freeze–thaw damage based on the number of cycles and the relative compressive strength. Full article