Search Results (7,767)

To enhance the utilization efficiency of coal gangue aggregate, coarse aggregates are chemically modified with 5% sodium silicate solution. The effects of this modification on the compressive strength and microstructural characteristics of concrete are systematically investigated through integrated macro-testing and micro-characterization. By evaluating the compressive performance of modified coal gangue concrete blocks, the optimal mix ratio of each strength grade of blocks is determined. Experimental results indicate that the apparent density, water absorption, and crushing index of the modified coal gangue coarse aggregate exhibit better mechanical properties than the control group. The modified coal gangue coarse aggregate demonstrates improved mechanical performance, with the compressive strength of 28-day concrete showing a 15.3% increase relative to the control group. Furthermore, using a sodium silicate solution effectively enhances the interface transition zone’s performance between coal gangue coarse aggregate and cement mortar, improving the compactness of this interface. The modified coal gangue concrete blocks exhibit higher compressive strength than the original material. When the substitution rate remains constant, the compressive strength of modified coal gangue concrete decreases with increasing water–cement ratio. Similarly, at a constant water–binder ratio, compressive strength decreases with higher modified gangue aggregate replacement. Finally, compressive tests are conducted on masonry constructed with hollow blocks of strength grades MU7.5, MU10, and MU15. Then, a calculation model for the average compressive strength of modified coal gangue concrete hollow block masonry is proposed, providing theoretical support for its engineering application. Full article

Tunnel linings play a vital role in underground infrastructure, yet their performance can be severely affected by pre-existing cracks. This study investigates the mechanical behavior and failure mechanisms of C30 concrete with artificial cracks under uniaxial compression, simulating various crack conditions observed in tunnel linings. Specimens were designed with varying crack lengths and orientations. Acoustic emission (AE) monitoring was employed to capture the evolution of internal damage and micro-cracking activity during loading. Fractal dimension analysis was performed on post-test crack patterns to quantitatively evaluate the complexity and branching characteristics of crack propagation. The AE results showed clear correlations between amplitude characteristics and macroscopic crack growth, while fractal analysis provided an effective metric for assessing the extent of damage. To complement the experiments, discrete element modeling (DEM) using PFC3D was applied to simulate crack initiation and propagation, with results compared against experimental data for validation. The study demonstrates the effectiveness of DEM in modeling cracked concrete and highlights the critical role of crack orientation and size in strength degradation. These findings provide a theoretical and numerical foundation for assessing tunnel lining defects and support the development of preventive and reinforcement strategies in tunnel engineering. Full article

Ultra-high-performance concrete (UHPC) is increasingly employed in long-span and heavily loaded structural applications; however, the accurate prediction of its flexural capacity remains a significant challenge because of the complex interactions among geometric parameters, reinforcement details, and advanced material properties. Existing design codes and single-architecture machine learning models often struggle to capture these nonlinear relationships, particularly when experimental datasets are limited in size and diversity. This study proposes a compact hybrid CNN–Transformer model that combines convolutional layers for local feature extraction with self-attention mechanisms for modeling long-range dependencies, enabling robust learning from a database of 120 UHPC beam tests drawn from 13 laboratories worldwide. The model’s predictive performance is benchmarked against conventional design codes, analytical and semi-empirical formulations, and alternative machine learning approaches including Convolutional Neural Networks (CNN), eXtreme Gradient Boosting (XGBoost), and K-Nearest Neighbors (KNN). Results show that the proposed architecture achieves the highest accuracy with an R² of 0.943, an RMSE of 41.310, and a 25% reduction in RMSE compared with the best-performing baseline, while maintaining strong generalization across varying fiber dosages, reinforcement ratios, and shear-span ratios. Model interpretation via SHapley Additive exPlanations (SHAP) analysis identifies key parameters influencing capacity, providing actionable design insights. The findings demonstrate the potential of hybrid deep-learning frameworks to improve structural performance prediction for UHPC beams and lay the groundwork for future integration into reliability-based design codes. Full article

Accurate prediction of shear strength at the interface between new and old concrete is vital for the structural performance of repaired and composite systems. However, the underlying shear transfer mechanism is highly nonlinear and influenced by multiple interdependent factors, which limit the applicability of conventional empirical models. To address this challenge, an interpretable machine-learning (ML) framework is proposed. The latest database of 247 push-off specimens was compiled from the recent literature, incorporating diverse interface types and design parameters. The hyperparameters of the adopted ML models were optimized via a grid search to ensure the predictive performance on the updated database. Among the evaluated algorithms, eXtreme Gradient Boosting (XGBoost) demonstrated the best predictive performance, with R² = 0.933, RMSE = 0.663, MAE = 0.486, and MAPE = 12.937% on the testing set, outperforming Support Vector Regression (SVR), Random Forest (RF), and adaptive boosting (AdaBoost). Compared with the best empirical model (AASHTO, R² = 0.939), XGBoost achieved significantly lower prediction errors (e.g., RMSE was reduced by 67.8%), enhanced robustness (COV = 0.176 vs. 0.384), and a more balanced mean ratio (1.054 vs. 1.514). The SHapley Additive exPlanations (SHAP) method was employed to interpret the model predictions, identifying the shear reinforcement ratio as the most influential factor, followed by interface type, interface width, and concrete strength. These results confirm the superior accuracy, generalizability, and explainability of XGBoost in modeling the shear behaviors of new–old concrete interfaces. Full article

This article explores the use of waste from polymetallic combines in South Kazakhstan, specifically tailings from the Achisay and Ansay deposits, as aggregates (crushed stone, sand) and mineral additives (dispersed barite powder) for producing concrete with specified operational properties. These secondary raw materials are now abundant in relation to their use, which makes them an affordable and accessible alternative for the manufacturing of concrete while also promoting environmental sustainability. X-ray diffraction, differential thermal analysis, and scanning electron microscopy of enriched barite ores in these tailings revealed valuable components, such as calcite, quartzite, and dolomite, suitable for use as aggregates and mineral additives. The calcite and quartzite content in the Ansay samples exceeds that in the Achisay samples. Concrete mixes with various proportions of crushed stone and sand from these tailings were prepared, and their working characteristics were analyzed. The impacts of filler content and grain composition on the characteristics of concrete mixtures were identified, and the requirements for optimizing aggregate grain composition to produce heavy concrete with desired qualities were determined. Heavy concrete with densities from 2300 to 2839 kg/m³ and compressive strengths from 41.6 to 58.2 MPa was developed. Physical and mechanical properties, including density, water absorption, frost resistance, and compressive strength, were also evaluated, confirming the feasibility of using technogenic waste in composite heavy concrete production. Full article

The skeleton curve plays a crucial role in evaluating the seismic capacity of damaged structures. The research explored the application of data-driven machine learning approaches to predict the skeleton curves of earthquake-damaged reinforced concrete (RC) columns. Various machine learning methods, including Lasso regression, K-nearest neighbor (KNN), support vector machine (SVM), decision tree, and AdaBoost, were employed to develop a machine learning prediction model (MLPM) for seismic-damaged RC columns. A substantial dataset for the MLPM was derived from finite element (FE) analysis results. The input parameters for the machine learning models included the design specifications of the numerical column model and the damage index (DI), while the coordinates of key points on the skeleton curves served as the output parameters. The findings indicated that the K-nearest neighbor algorithm exhibited the best predictive performance, particularly for the yielding and peak points. The most influential input feature for predicting peak strength was the shear span-to-effective depth ratio, followed by the DI. The ML-based models demonstrated higher efficiency than numerical simulations and theoretical calculations in predicting the skeleton curves of damaged RC columns. Full article

This study examines the effects of incorporating recycled carbon fibers obtained from decommissioned wind turbine blades into cementitious composites. An extensive experimental program was carried out, varying fiber content (0–8 kg/m³), fiber length (25, 38, 50 mm), water-to-cement ratio (0.4, 0.5), and cement type (CEM I 42.5, CEM II 42.5R/A-V). The mechanical properties of the fiber-reinforced concretes, including compressive strength, flexural strength, splitting tensile strength, and modulus of elasticity, were evaluated. The addition of recycled carbon fibers significantly improved flexural and splitting tensile strengths, with increases exceeding 60% and 100%, respectively, at the highest fiber dosage (8 kg/m³), attributed to efficient crack-bridging capability. Compressive strength was mainly influenced by the water-to-cement ratio, while the modulus of elasticity showed slight reductions in some mixes due to fiber clustering and increased micro-porosity. Regression analysis indicated that shorter fibers (25 mm) were more effective in enhancing flexural strength, whereas longer fibers (50 mm) improved splitting tensile strength. Classical predictive models generally underestimated the flexural capacity of recycled-carbon-fiber-reinforced concretes, highlighting the need for recalibration. Optical microscopy confirmed uniform fiber dispersion at lower dosages and a dominant pull-out failure mechanism. The findings demonstrate the feasibility of using recycled carbon fibers to enhance the mechanical performance of concrete while supporting sustainability through waste diversion and circular economy strategies. Full article

This study explores the influence of various nano-inclusions on the electrical and thermal properties of cement-based materials. Specifically, it investigates the incorporation of Multi-Walled Carbon Nanotubes (MWCNTs) and Graphene Nanoplatelets (GNPs) as reinforcement materials in cement composites. These advanced nanomaterials enhance the mechanical strength, durability, and functional properties of cementitious matrices. A series of experimental tests was conducted to evaluate the thermal and electrical behavior of nano-reinforced concrete, employing nondestructive evaluation techniques, such as Infrared Thermography (IRT) and Electrical Resistivity measurements. The results indicate that increasing the concentration of nanomaterials significantly improves both the thermal and electrical conductivity of the composites. Optimum performance was observed at a CNT dosage of 0.6% and a GNP dosage of 1.2% by weight of cement in cement paste, while in concrete, both nanomaterials showed a significant decrease in resistivity beginning at 1.0%, with optimal performance at 1.2%. The study also emphasizes the critical role of proper dispersion techniques, such as ultrasonication, in achieving a homogeneous distribution of nanomaterials within the cement matrix. These findings highlight the potential of carbon nanotubes (CNTs) and GNPs to enhance the multifunctional properties of cement-based materials, paving the way for their application in smart and energy-efficient construction applications. Full article

Ultra-High-Performance Concrete (UHPC) is a game-changing, innovative material with the merits of exceptional tensile strength, making it suitable for stirrup-free UHPC beams. In this study, two 4.0 m-long large-scale stirrup-free I-shaped UHPC beams were experimentally explored in bending tests and shear tests. Cracking patterns, failure modes, and ultimate load-bearing capacity were obtained. Experimental findings revealed that the shear capacity of the stirrup-free I-shaped UHPC beams with a web thickness of merely 50.0 mm reached more than 20.0 MPa and demonstrated excellent post-cracking shear behavior. Finite element models were established and verified with experimental results to investigate the shear behaviors of stirrup-free I-shaped UHPC beams, considering the parameters of shear span-depth ratio and longitudinal reinforcement strength. The results demonstrated that as the shear span-depth ratio increases, the shear capacity of UHPC beams exhibits a declining trend, accompanied by increased mid-span deflection and a degradation in stiffness. French code and PCI report were suggested for design purposes, due to rationally conservative prediction and explicit physical indication. Full article

Heat-curing processes are often used to ensure the production rate of precast concrete elements, as this process increases the early strength of the material. However, the increase in curing temperature can negatively affect the final mechanical properties since cracking, and especially high porosity, may occur under these conditions. In order to compensate for the expected loss in mechanical and durability-related properties, the cement content is typically increased. This solution raises the cost of the final product and reduces its sustainability. Thus, in this study, the development of expansive self-compacting concretes (SCCs) is proposed to achieve higher final mechanical properties without increasing cement contents. The mechanical properties, expansive performance, and porous microstructure have been evaluated under different curing regimes. The obtained results show that it is possible to obtain similar or even better mechanical performance in expansive concretes cured at high temperatures than in those cured in standard conditions, particularly when using ettringite-based expansive agents (EAs). Moreover, the use of limestone filler (LF) proved to be more suitable than the use of fly ashes in the working conditions evaluated in the present study. In this sense, the compressive strength at 28 days of SCC with LF and ettringite-based EAs is 4.3% higher than the one obtained under standard curing; moreover, the total porosity is reduced (5%), and the drying shrinkage is also limited. These aspects have not been previously reported in non-expansive heat-cured concretes and represent a unique opportunity to reduce the cement content and, therefore, the carbon footprint of precast concretes without reducing their mechanical properties. When using CaO-based EAs, the results are also better than those of non-expansive SCC, although the improvement is less pronounced than in the previous case. Full article

Owing to its excellent crack resistance and durability, High-Performance Fiber-Reinforced Concrete (HPFRC) has been extensively applied in engineering structures exposed to extreme loading conditions. The Mode I dynamic fracture strength of HPFRC under high-strain-rate conditions exhibits significant strain-rate sensitivity and nonlinear response characteristics. However, existing experimental methods for strength measurement are limited by high costs and the absence of standardized testing protocols. Meanwhile, conventional data-driven models for strength prediction struggle to achieve both high-precision prediction and physical interpretability. To address this, this study introduces a dynamic fracture strength prediction method based on a feature-weighted linear ensemble (FWL) mechanism. A comprehensive database comprising 161 sets of high-strain-rate test data on HPFRC fracture strength was first constructed. Key modeling variables were then identified through correlation analysis and an error-driven feature selection approach. Subsequently, six representative machine learning models (KNN, RF, SVR, LGBM, XGBoost, MLPNN) were employed as base learners to construct two types of ensemble models, FWL and Voting, enabling a systematic comparison of their performance. Finally, the predictive mechanisms of the models were analyzed for interpretability at both global and local scales using SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) methods. The results demonstrate that the FWL model achieved optimal predictive performance on the test set (R² = 0.908, RMSE = 2.632), significantly outperforming both individual models and the conventional ensemble method. Interpretability analysis revealed that strain rate and fiber volume fraction are the primary factors influencing dynamic fracture strength, with strain rate demonstrating a highly nonlinear response mechanism across different ranges. The integrated prediction framework developed in this study offers the combined advantages of high accuracy, robustness, and interpretability, providing a novel and effective approach for predicting the fracture behavior of HPFRC under high-strain-rate conditions. Full article

Fly ash, as the main solid waste of coal-fired power plants, is an environmental problem that needs to be solved due to its massive accumulation. The mechanical properties and optimization mechanism of lightweight aggregate concrete prepared by using new single-cylinder rotary kiln fly ash ceramic granules as aggregate were systematically investigated. Through orthogonal experimental design, combined with macro-mechanical testing and microscopic characterization techniques, the effects of cement admixture and ceramic granule admixture on the properties of concrete, such as compressive strength, split tensile strength, and modulus of elasticity, were analyzed, and the optimization scheme of key parameters was proposed. The results show that the new single rotary kiln fly ash ceramic particles significantly improve the mechanical properties of concrete by optimizing the porosity (water absorption ≤ 5%), and its 28-day compressive strength reaches 46~50.9 MPa, which is 53.3~69.7% higher than that of the ordinary ceramic concrete, and the apparent density is ≤1900 kg/m³, showing lightweight and high-strength characteristics. X-ray diffraction (XRD) analysis shows that the new ceramic grains form a more uniform, dense structure through the synergistic effect of internal mullite crystals and dense glass phase; computed tomography (CT) scanning shows that the total volume rate of cracks of the new ceramic concrete was reduced by up to 63.8% compared with that of ordinary ceramic concrete. This study provides technical support for the utilization of fly ash resources, and the prepared vitrified concrete meets the demand of green building while reducing structural deadweight (20~30%), which has significant environmental and economic benefits. Full article

To avoid cumbersome casting procedures in the production of pervious concrete, a new type of casting method through coating cementitious paste onto the preplaced aggregate skeleton is developed. To optimize the key performances and reveal their governing mechanism, aggregate skeleton–cementitious paste-coating pervious concrete (ACPC) mixes with different porosity, water/cement (w/c) ratio and sand ratio were produced and had their permeability and strength tested. This study demonstrated that it is successful to produce pervious concrete by the novel casting method. Vibration of aggregate skeleton and high w/c ratio should not be adopted to avoid the formation of a layer of hardened paste at the bottom of the mix to block the vertical passage of water. In contrast to conventional concrete, a higher w/c ratio (from 0.23 to 0.34) generally resulted in a higher strength (from 3.77 to 8.71 MPa) of ACPC. A small amount of sand increased both the permeability and strength through the balling bearing effect and filling effect, respectively. Both the optimum sand ratio to achieve the highest vertical permeability and strength were found to be 0.05, which offered this porous structure concurrently satisfactory permeability (permeability coefficient higher than grade K2) and acceptable strength (compressive strength higher than 5 MPa). Key influencing factors of permeability and strength of ACPC were analyzed. This study can advance the technology of casting concrete and the production of pervious concrete as road pavement in the construction of “sponge city”. Full article

This study explores the potential use of Polyethylene Terephthalate (PET) plastic bottles as void makers in short reinforced concrete columns under pure axial compression. Such a scheme promotes sustainability by decreasing the consumption of concrete and reducing the pollution that comes with the disposal of PET bottles. The experimental component of this study consisted of testing 16 reinforced concrete columns divided into two groups, based on the cross-section dimensions. One group contained eight columns of a length of 900 mm with a net cross-sectional area of about 40,000 mm², while the second group contained eight columns of a length of 1100 mm with a net cross-sectional area of about 62,500 mm². The diameter of the void within the small cross-section group was 100 mm and within the large cross-section group was 265 mm. The experimental program includes pairs of solid and corresponding void specimens with consideration of the size of the longitudinal steel reinforcement, lateral tie spacing, and concrete compressive strength. The tests are conducted using a universal test machine under displacement-controlled loading conditions with the help of strain gauges and Linear Variable differential transformers (LVDTs). The analysis of the test results showed that the columns that were embedded with a small void that occupied about 30% of the core area exhibited reductions of 9% in the ultimate capacity, 14% in initial stiffness, 20% in ductility, and 1% in residual strength. On the other hand, the columns that contained a large void occupying about 60% of the core area demonstrated reductions of 24% in the ultimate capacity, 34% in initial stiffness, and 26% in ductility, although the residual strength was slightly increased by 5%. The reason for the deficiency in the structural response in the latter case is because the void occupied a significant fraction of the concrete core. The theoretical part of this study showed that the ACI 318 code provisions can reasonably predict the uniaxial compressive strength of columns embedded with PET bottles if the void does not occupy more than 30% of the concrete core. This study confirmed that short columns embedded with relatively small voids made from PET bottles and subjected to pure axial compression create a balance between sustainability benefits and a structural performance tradeoff. Full article

To overcome brittle failure in conventional cold-formed steel–concrete (CFS-C) corner columns, this paper used fiber-reinforced concrete to replace ordinary concrete, investigating failure mechanisms and performance through systematic numerical simulations. A finite element model (FEM) was established and validated by experiments, and the errors for ultimate capacity were within 10%. A series of numerical models was established for parametric analyses focusing on the effects of the parameters of polypropylene fiber (PF), carbon fiber (CF), steel fiber (SF), and bamboo fiber (BF) with different volume dosages and the thickness of cold-formed steel (CFS) on the axial compression ultimate capacity and corresponding displacement of CFS composite corner columns. The results indicated that (1) PF effectiveness was dependent on steel thickness: thicker steel suppressed micro-defects, activated the toughening potential of PF, and increased the ultimate capacity of the columns by 24.8%. (2) CF had a critical dosage of 0.4%: at this dosage, CF increased the column’s ultimate capacity by 14.1% through stress redistribution, while when the dosage exceeded this value, fiber agglomeration caused a reduction in the column’s strength, with a maximum decrease of 16.2%. (3) SF effectiveness showed a linear increase: at a dosage of 1.6%, SF formed a synergistic three-dimensional bridging network and generated a confinement effect, increasing the column’s ultimate capacity by 36.5% and displacement by 92.2%. (4) BF mainly improved the ductility of columns: through crack bridging and pull-out energy dissipation, BF increased column displacement by 33.2%. (5) The modified Eurocode 4 formula could reduce the calculation error of ultimate capacity from 6.3% to within 1%. The findings guide optimal fiber selection and dosage in practice, promoting such columns’ use in seismic and load-bearing structures. Full article