Search Results (132)

This paper presents an experimental investigation of the shear connection between outer layers of lightweight precast concrete sandwich panels (PCSP) made of high-performance concrete (HPC). The shear-transfer mechanism is based on reinforcing ribs composed of rigid polymer-based thermal insulation combined with carbon-fibre-reinforced polymer (CFRP) shear reinforcement. A total of seven full-scale sandwich panels were tested in four-point bending. This study compares three types of rigid thermal insulation used in the shear ribs—Purenit, Compacfoam CF400, and Foamglass F—and investigates the influence of the amount of CFRP shear reinforcement on the structural behavior of the panels. Additional specimens were used to evaluate the effect of reinforcing ribs and of polymer-based thermal insulation placed between the ribs. The experimental results show that panels with shear ribs made of Purenit and Compacfoam CF400 achieved significantly higher load-bearing capacities compared to Foamglass F, which proved unsuitable due to its brittle behavior. Increasing the amount of CFRP shear reinforcement increased the load-bearing capacity but had a limited effect on panel stiffness. The experimentally determined composite interaction coefficient ranged around α ≈ 0.03, indicating partial shear interaction between the outer concrete layers. A simplified strut-and-tie model was applied to predict the load-bearing capacity and showed conservative agreement with experimental results. The findings demonstrate that polymer-based materials, particularly CFRP reinforcement combined with rigid polymer insulation, enable efficient shear transfer without thermal bridging, making them suitable for lightweight and thermally efficient precast concrete sandwich panels. Full article

Textile-reinforced concrete (TRC) sandwich panels with lightweight cores are a promising solution for sustainable and slender building envelopes. However, their structural performance depends strongly on the shear connection between the outer shells. This study investigates the flexural behavior of TRC sandwich panels with glass fiber-reinforced polymer (GFRP) rod connectors under four-point bending. Three full-scale specimens were manufactured with high-performance concrete (HPC) face layers, an expanded polystyrene (EPS) core, and 12 mm GFRP rods as shear connectors. The panels were tested up to failure, with measurements of load–deflection behavior, crack development, and interlayer slip. Additionally, a linear-elastic finite element model was developed to complement the experimental campaign, capturing the global stiffness of the system and providing complementary insight into the internal stress distribution. The experimental results revealed stable load-bearing behavior with ductile post-cracking response. A degree of composite interaction of γ = 0.33 was obtained, indicating partially composite action. Slip measurements confirmed effective shear transfer by the GFRP connectors, while no brittle failure or connector rupture was observed. The numerical analysis confirmed the elastic response observed in the tests and highlighted the key role of the GFRP connectors in coupling the TRC shells, extending the interpretation beyond experimental results. Overall, the study demonstrates the potential of TRC sandwich panels with mechanical connectors as a safe and reliable structural solution. Full article

Concrete compressive strength is a fundamental indicator of the mechanical properties of High-Performance Concrete (HPC) with multiple components. Traditionally, it is measured through laboratory tests, which are time-consuming and resource-intensive. Therefore, this study develops a machine learning-based regression framework to predict compressive strength, aiming to reduce experimental costs and resource usage. Under three different data preprocessing strategies—raw data, standard score, and Box–Cox transformation—a selected set of high-performance ensemble models demonstrates excellent predictive capacity, with both the coefficient of determination (${\mathrm{R}}^2$) and explained variance score (EVS) exceeding 90% across all datasets, indicating high accuracy in compressive strength prediction. In particular, stacking ensemble (${\mathrm{R}}^2$-$0.920$, $\mathrm{EVS}$-$0.920$), XGBoost regression (${\mathrm{R}}^2$-$0.920$, $\mathrm{EVS}$-$0.920$), and HistGradientBoosting regression (${\mathrm{R}}^2$-$0.913$, $\mathrm{EVS}$-$0.914$) based on Box–Cox transformation data show strong generalization capability and stability. Additionally, tree-based and boosting methods demonstrate high effectiveness in capturing complex feature interactions. Furthermore, this study presents an analytical workflow that enhances feature interpretability through visualization techniques—including Partial Dependence Plots (PDP), Individual Conditional Expectation (ICE), and SHapley Additive exPlanations (SHAP). These methods clarify the contribution of each feature and quantify the direction and magnitude of its impact on predictions. Overall, this approach supports automated concrete quality control, optimized mixture proportioning, and more sustainable construction practices. Full article

The use of high-volume industrial by-products in high-performance concrete (HPC) production offers a promising and sustainable strategy for reducing ordinary Portland cement (OPC) consumption. However, each pozzolanic material has a unique chemical composition and physical characteristics, making ternary and quaternary binder systems an effective approach for optimizing performance. In this study, quaternary binders comprising OPC partially replaced with Class F fly ash (FA), ground granulated blast-furnace slag (GGBFS), and silica fume (SF) were designed using the Taguchi method, and the mechanical and durability properties of fine-grained HPC were evaluated. Sixteen concrete mixtures were developed considering three factors—FA, GGBFS, and SF replacement levels—each at four dosage levels. The results show that incorporating SF significantly enhanced both mechanical performance and durability. An optimal blend containing 60% OPC, 30% GGBFS, and 10% SF exhibited superior performance compared with the 100% OPC control mix. Additionally, a mixture of 40% OPC, 40% GGBFS, 10% Class F FA, and 10% SF achieved comparable compressive strength to the control, exceeding 100 MPa at 28 days. SEM observations confirmed the dense microstructure of this HPC mix. ANOVA analysis indicated that FA and SF had a significantly greater influence on HPC strength development than GGBFS. Overall, these findings demonstrate the potential of high-volume industrial by-products to produce fine-grained HPC, providing a high-performance and environmentally friendly alternative to conventional OPC-based concrete. Full article

The prediction of concrete compressive strength (CSMPa) is fundamental in experimental civil engineering as it enables the optimization of mix design and complements laboratory testing through predictive tools. This study presents a systematic and reproducible methodology for comparing eight regression algorithms—including linear models, neural networks, and boosting methods—applied to three experimental datasets that represent different types of concrete: high-performance concrete (HPC), conventional concrete, and recycled-aggregate concrete (RAC). In order to make such comparison, some performance metrics were calculated (RMSE, MAE, MAPE, $R^2$, and nRMSE) through hyperparameter optimization using RandomizedSearchCV and homogeneous cross-validation. The boosting methods achieved the best performance, with CatBoost standing out by reaching $R^2$ values between 0.92 and 0.95 and RMSE between 3.4 and 4.4 MPa, confirming its inter-dataset stability and generalization capability. These results indicate consistent predictive accuracy across concretes of different compositions and production contexts. As an applied contribution, three interactive inference systems were developed in Google Colab to estimate CS from mix parameters, promoting reproducibility, open access, and practical use in quality-control processes. Full article

The salt lake and saline–alkali soil regions of high plateaus are characterized by widespread Alkali–silica reactive (ASR) aggregates, which severely threaten the durability of constructed infrastructure, including railways, highways, and buildings. The research systematically investigates the uniaxial compressive mechanical behavior and stress–strain constitutive relationship of high-performance concrete (HPC) with ASR mitigation measures (performance grades C40, C45, C50, and C60) after ten years of immersion in a standard alkali solution. A corresponding three-dimensional random aggregate mesoscopic concrete model was developed, and mesomechanical numerical simulations were performed to explore the failure process, failure patterns, and underlying mesoscopic damage mechanisms of the specimens. Results show that While the uniaxial compressive strength and elastic modulus of HPC show an expected increase with the concrete strength grade following long-term alkali exposure, both properties demonstrate a clear decline as the equivalent alkali content rises. Comparing and analyzing the C50 specimens of different admixtures, it was found that the air-entraining agent provided the most effective ASR suppression and obtained the highest uniaxial compressive strength compared with the rust inhibitor. By normalizing the stress–strain curves, the long-term constitutive behavior of HPC under alkali corrosion was summarized. Furthermore, mesoscopic model visualizations indicate that cracks initially appear in the mortar and gradually propagate inward during loading, leading to compressive failure characterized by diagonal cracks. Tracking the mesoscopic damage patterns within the specimens demonstrates that microcracks originate in the mortar and progressively extend through aggregates, revealing the underlying micro-damage mechanism. By studying the SEM-EDS images, it is found that HPC with a specific mix ratio designed in this paper can effectively inhibit the ASR effect, and it still has good corrosion resistance in long-term alkali immersion. Full article

This study investigates the long-term mechanical and microstructural behavior of high-performance concrete (HPC), incorporating hybrid mixtures of coal bottom ash (CBA) and waste foundry sand (WFS) as sustainable mineral additives. The experimental program included evaluation of physical parameters (porosity, water absorption, and density), mechanical properties (compressive, splitting tensile, flexural strength, and elastic modulus), ultrasonic pulse velocity (UPV), and microstructural observations by scanning electron microscopy (SEM). The incorporation of CBA and WFS up to 30 wt% modified the pore structure and densified the matrix, leading to improved long-term strength and durability. The BA25FS5 and BA20FS10 mixtures exhibited the most balanced performance, showing compressive strengths up to 86 MPa at 730 days and UPV exceeding 4.5 km/s (measured at 730 days). SEM analysis confirmed a dense C–S–H network and strong ITZ bonding in hybrid concretes. Empirical models, including Ryshkevich, Balshin, and ACI–fib correlations, accurately described the relationships between porosity, density, and mechanical properties, achieving coefficients of determination above 0.9. The results demonstrate that the combined use of CBA and WFS enhances microstructural refinement, stiffness, and long-term performance while promoting sustainable utilization of industrial by-products in high-performance concrete. Full article

This study investigates the static mechanical behavior of a novel eco-friendly high-performance concrete (HPC) reinforced with fibers under different moisture conditions, reflecting the humidity variations commonly encountered in engineering practice. Three saturation levels—natural, dry, and water saturated—were considered. The optimal dosages of basalt and glass fibers were first identified through tests in the natural state, and empirical relationships between fiber volume fraction, compressive strength, and fracture energy were established. Comparative experiments were then conducted at the optimal dosages under varying saturation conditions. Results show that basalt fiber provides superior compressive strength, exceeding that of glass fiber by 0.86% in the dry state and 10.66% in the saturated state. Conversely, glass fiber exhibits a greater enhancement in flexural strength, with improvements of 14.91% and 3.38% over basalt fiber under dry and saturated conditions, respectively. Although preliminary models were proposed to correlate fiber volume fraction with strength in dry and saturated environments, their predictive accuracy proved limited. Overall, the findings highlight the distinct reinforcing effects of basalt and glass fibers on HPC under different moisture conditions, offering guidance for the design and application of fiber-reinforced recycled concrete in humid service environments. Full article

The growing adoption of steel–concrete composite beams has spurred extensive use of high-strength steel (HSS) and high-performance concrete (HPC) in composite structures, capitalizing on their superior mechanical properties. To address the limited shear capacity of conventional stud connectors and unitary steel dowels, this study proposed a dowel–stud hybrid connector for advanced composite systems. Push-out tests were conducted on one conventional shear stud specimen, one monolithic steel dowel specimen, and four dowel–stud hybrid connector specimens. Experimental and finite element analyses were employed to evaluate the effects of the stud length, diameter, and layout on the failure modes and shear performance of composite connectors. The findings demonstrated that the hybrid connectors exhibited significantly enhanced shear capacity and ductility compared to those of both conventional stud connectors and monolithic steel dowels. Failure primarily occurred at the roots of the steel dowels and shear studs, with the underlying concrete exhibiting crushing failure. Increasing the diameter from 6 mm to 22 mm marginally influenced the ultimate shear capacity (the variation was <4%) but notably improved the initial stiffness. For composite connectors with 13 mm diameter studs, increasing the stud length from 40 mm to 80 mm and 120 mm raised the ultimate capacity by 4.7% and 8.8%, respectively. Conversely, for composite connectors with 16 mm diameter studs, length variations exerted negligible influence (<4%) on the ultimate capacity. In addition, the study layout critically influenced the performance. At a fixed 16 mm diameter, relocating studs from the dowel center to the sub-root region increased the shear capacity by 23%. Full article

Normal concrete exhibits poor resistance to chloride penetration, often leading to reinforcement corrosion and premature structural failure. In contrast, ultra-high-performance concrete (UHPC) demonstrates superior resistance to corrosion caused by chloride salts. The chloride diffusion behaviour of UHPC was investigated via long-term immersion (LTI) and rapid chloride migration (RCM) tests. Additionally, this study presents the first development of a time-dependent diffusion model for UHPC under chloride corrosion, as well as the proposal of a performance-based design method for calculating the protective layer thickness. Results show that the incorporation of steel fibers reduced the chloride diffusion coefficient ($D$) by 37.9%. The free chloride content (FCC) in UHPC increased by 92.0% at 2 mm after 300 d of the action of LTI. $D$ decreased by up to 91.0%, whereas the surface chloride concentration ($C_{\mathrm{s}}$) increased by up to 92.5% under the action of LTI. The time-dependent models of $D$ and $C_{\mathrm{s}}$ followed power and logarithmic functions, respectively. An increase in UHPC surface temperature, relative humidity, and tensile stress ratio significantly diminishes the chloride resistance of UHPC. The minimum UHPC protective layer thicknesses required for UHPC-HPC composite beams with design service lives of 100 years, 150 years, and 200 years are 30 mm, 37 mm, and 43 mm, respectively. Full article

The objective of this study was to evaluate the technical properties and assess the durability of a novel high-performance concrete with aggregates composed entirely of reactive powders derived from chalcedonite—a mineral previously not utilized in HPC technology. Since there is insufficient information on chalcedonite-based concretes in the scientific literature, the presented research aims to address these knowledge gaps. The characterization of the chalcedonite powder involved the determination of specific gravity, particle size distribution, specific surface area, and particle morphology through microscopic analysis. The hardened chalcedonite-based and reference quartz-based high-performance concretes were subjected to a comprehensive suite of tests to determine their physical properties (bulk density, water absorption, and capillary absorption) and mechanical properties (flexural and compressive strength). Durability was further assessed based on compressive strength criteria, including frost resistance and carbonation resistance. To simulate long-term performance and better evaluate the durability of the high-performance concretes, specimens were tested following standard water curing and after additional maturation processes, including thermal treatment, which in the extreme case resulted in a seven-day compressive strength of 176.9 MPa, a value higher by 56.7 MPa (corresponding to an increase of 47.1%) compared to the strength of the identical concrete not subjected to thermal treatment. To explore the potential for architectural applications, particularly in outdoor environments, capillary absorption testing was of particular importance, as it provided insight into the material’s resistance to eventual pigment leaching from the mineral matrix. Full article

High-performance concrete (HPC) is an essential construction material used for modern buildings and infrastructure assets, recognized for its exceptional strength, durability, and performance under harsh situations. Nonetheless, the HPC production process frequently correlates with elevated carbon emissions, principally attributable to the high quantity of cement utilized, which significantly influences its carbon footprint. In this study, data-driven modeling and optimization strategies are employed to minimize the carbon footprint of high-performance concretes while keeping their performance properties. Starting from an experimental dataset, artificial neural networks (ANNs), ensemble techniques (ETs), and Gaussian process regression (GPR) are employed to yield predictive models for compressive strength of HPC mixes. The model’s input variables are the various components of HPC: cement, water, superplasticizer, fly ash, blast furnace slag, and coarse and fine aggregates. Models are trained using a dataset of 356 records. Results proved that the GPR-based model exhibits excellent accuracy with a determination coefficient of 0.90. The prediction model is used in a double objective optimization task formulated to identify mix configurations that allow for high mechanical performance aligned with a reduced carbon emission. The multi-objective optimization task is undertaken using genetic algorithms (GAs). Promising results are obtained when the machine learning prediction model is associated with GA optimization to identify strong yet sustainable mix configurations. Full article

This study investigates the flexural performance, tensile splitting strength, and fracture behaviour of self-compacting concrete (SCC) reinforced with polypropylene (PP) and basalt (BF) fibres. A total of eleven SCC mixtures with varying fibre types and volume fractions (0.025–0.25%) were tested at 7 and 28 days. In this study, the term high-performance concrete (HPC) refers to SCC mixtures with a 28-day compressive strength exceeding 60 MPa, as commonly accepted in European standards and literature. The control SCC achieved 68.2 MPa at 28 days. While fibre addition enhanced the tensile and flexural properties, it reduced workability, demonstrating the trade-off between mechanical performance and flowability in high-performance SCC. The experimental results demonstrate that both fibre types improve the tensile behaviour of SCC, with distinct performance patterns. PP fibres, owing to their flexibility and crack-bridging capability, were particularly effective at early ages, enhancing the splitting tensile strength by up to 45% and flexural toughness by over 300% at an optimal dosage of 0.125%. In contrast, BF fibres significantly increased the 28-day toughness (up to 15.7 J) and post-cracking resistance due to their superior stiffness and bonding with the matrix. However, high fibre contents adversely affected workability, particularly in BF-reinforced mixes. The findings highlight a dosage-sensitive behaviour, with optimum performance observed at 0.05–0.125% for PP and 0.125–0.25% for BF. While PP fibres improve crack distribution and early-age ductility, BF fibres offer higher stiffness and energy absorption in post-peak regimes. Statistical analysis (ANOVA and Tukey’s test) confirmed significant differences in the mechanical performance among fibre-reinforced mixes. The study provides insights into selecting appropriate fibre types and dosages for SCC structural applications. Further research on hybrid fibre systems and long-term durability is recommended. The results contribute to sustainable concrete design by promoting enhanced performance with low-volume, non-metallic fibres. Full article

Accurately predicting the compressive strength of high-performance concrete (HPC) is critical for ensuring structural integrity and promoting sustainable construction practices. However, HPC exhibits highly complex, nonlinear, and multi-factorial interactions among its constituents (such as cement, aggregates, admixtures, and curing conditions), which pose significant challenges to conventional predictive models. Traditional approaches often fail to adequately capture these intricate relationships, resulting in limited prediction accuracy and poor generalization. Moreover, the high dimensionality and noisy nature of HPC mix data increase the risk of model overfitting and convergence to local optima during optimization. To address these challenges, this study proposes a novel bio-inspired hybrid optimization model, AP-IVYPSO-BP, which is specifically designed to handle the nonlinear and complex nature of HPC strength prediction. The model integrates the ivy algorithm (IVYA) with particle swarm optimization (PSO) and incorporates an adaptive probability strategy based on fitness improvement to dynamically balance global exploration and local exploitation. This design effectively mitigates common issues such as premature convergence, slow convergence speed, and weak robustness in traditional metaheuristic algorithms when applied to complex engineering data. The AP-IVYPSO is employed to optimize the weights and biases of a backpropagation neural network (BPNN), thereby enhancing its predictive accuracy and robustness. The model was trained and validated on a dataset comprising 1030 HPC mix samples. Experimental results show that AP-IVYPSO-BP significantly outperforms traditional BPNN, PSO-BP, GA-BP, and IVY-BP models across multiple evaluation metrics. Specifically, it achieved an R² of 0.9542, MAE of 3.0404, and RMSE of 3.7991 on the test set, demonstrating its high accuracy and reliability. These results confirm the potential of the proposed bio-inspired model in the prediction and optimization of concrete strength, offering practical value in civil engineering and materials design. Full article

This study investigates the combined influence of superabsorbent polymers (SAPs) with distinct absorption kinetics and extended mixing sequences on the rheological, mechanical, and transport properties of high-performance concrete (HPC). Two SAPs—an ionic acrylamide-co-acrylic acid copolymer (SAP-P) and a non-ionic acrylamide polymer (SAP-B)—were incorporated at an internal curing level of 100%. The impact of extended mixing times (3, 5, and 7 min) following SAP addition was systematically evaluated. Results showed that longer mixing durations led to increased superplasticizer demand and higher plastic viscosity due to continued water absorption by SAPs. However, yield stress remained relatively stable owing to the dispersing effect of the added superplasticizer. Both SAPs significantly enhanced the static yield stress and improved fresh stability, as evidenced by reduced surface settlement. Despite the rheological changes, mechanical properties—including compressive and flexural strengths and modulus of elasticity—were consistently improved, regardless of mixing duration. SAP incorporation also led to notable reductions in autogenous and drying shrinkage, as well as enhanced electrical resistivity, indicating better durability performance. These findings suggest that a 3 min extended mixing time is sufficient for effective SAP dispersion without compromising performance. Full article