Search Results (418)

This paper presents an experimental and numerical investigation of continuous reinforced concrete (RC) beams made of self-compacting concrete (SCC) strengthened with fiber-reinforced polymer (FRP) bars using the Near-Surface Mounted (NSM) method. While the majority of previous studies have focused on simply supported beams, this work examines two-span continuous beams, which are more representative of real structural behavior. Four SCC beams were tested under static loading to evaluate the influence of the FRP reinforcement position on flexural capacity and deformational characteristics. The beams were strengthened using glass FRP (GFRP) bars embedded in epoxy adhesive within pre-cut grooves in the concrete cover. Experimental results showed that FRP reinforcement significantly increased the ultimate load capacity, while excessive reinforcement reduced ductility, leading to a more brittle failure mode. A three-dimensional finite element model was developed in Abaqus/Standard using the Concrete Damage Plasticity (CDP) model to simulate the nonlinear behavior of concrete and the bond–slip interaction at the epoxy–concrete interface. The numerical predictions closely matched the experimental load–deflection responses, with a maximum deviation of less than 3%. The validated model provides a reliable tool for parametric analysis and can serve as a reference for optimizing the design of continuous SCC beams strengthened by the NSM FRP method. Full article

This paper presents an experimental investigation on the flexural performance of continuous two-span reinforced concrete beams made with self-compacting concrete (SCC) incorporating natural and recycled coarse aggregates. A total of nine beams were tested under static loading conditions. The beams were divided into three groups based on different reinforcement ratios, and within each group, three aggregate replacement levels were used: 0%, 50%, and 100% recycled coarse aggregate. All beams were designed with identical cross-sections and subjected to two-point loading to simulate continuous support conditions. The study focused on evaluating cracking behavior, load–deflection response, and failure modes. The experimental results highlight that partial replacement with recycled aggregates (RAC50) can achieve comparable or even improved mechanical performance compared to natural aggregate beams, including enhanced compressive strength and ductility. Beams with 100% recycled aggregates (RAC100) showed slightly higher deflections and earlier crack initiation, particularly at lower reinforcement ratios, although overall flexural behavior remained consistent with natural aggregate concrete (NAC) beams. It was also observed that as reinforcement ratio increases, the influence of aggregate type diminishes, indicating that steel reinforcement predominantly governs the structural response at higher ratios. Crack widths and propagation patterns were systematically monitored, confirming that RAC beams maintain acceptable deformation and ductility under load. These findings emphasize the feasibility of using high-quality recycled aggregates in structural SCC elements, providing a sustainable alternative without compromising performance, and offering guidance for the design of continuous RAC beams. Full article

This study is based on in situ structural test sections and systematically explains the construction processes and key control points of different waterproofing methods by optimizing the self-waterproofing of structural concrete, controlling the installation process of external waterproofing membranes, and managing quality throughout the construction process. For various materials such as polymer-coated waterstops, steel-edged rubber waterstops, and composite grouting pipes with water-swelling strips, the waterproofing performance under the corresponding processes was analyzed through a combination of experiments and numerical simulations. The research focuses on investigating the influence of material selection and construction techniques on waterproofing effectiveness, clarifying the applicable conditions and performance differences among various materials and techniques. The results indicate that polymer-coated waterstops perform significantly better than other materials; self-compacting concrete causes minimal disturbance to waterstops, which is beneficial for waterproofing, but it exhibits deficiencies in early-age crack resistance; refined control of construction techniques plays a decisive role in the overall performance of the waterproofing system. Consequently, detailed construction quality control specifications for the main structure and its components were developed. Full article

Accurate prediction of the mechanical and rheological properties of self-compacting concrete (SCC) is critical for mixture design and engineering decision-making; however, conventional empirical approaches often struggle to capture the coupled nonlinear relationships among mixture variables. To address this challenge, this study develops an integrated and interpretable hybrid machine learning (ML) framework by coupling three ML models (RF, XGBoost, and SVR) with five metaheuristic optimizers (SSA, PSO, GWO, GA, and WOA), and by incorporating SHAP and partial dependence (PDP) analyses for explainability. Two SCC datasets with nine mixture parameters are used to predict 28-day compressive strength (CS) and slump flow (SF). The results show that SSA provides the most stable hyperparameter optimization, and the best-performing SSA–RF model achieves test R² values of 0.967 for CS and 0.958 for SF, with RMSE values of 2.295 and 23.068, respectively. Feature importance analysis indicates that the top five variables contribute more than 80% of the predictive information for both targets. Using only these dominant features, a simplified SSA–RF model reduces computation time from 7.3 s to 5.9 s and from 9.7 s to 6.1 s for the two datasets, respectively, while maintaining engineering-level prediction accuracy, and the SHAP and PDP analyses provide transparent feature-level explanations and verify that the learned relationships are physically consistent with SCC mixture-design principles, thereby increasing the reliability and practical applicability of the proposed framework. Overall, the proposed framework delivers accurate prediction, transparent interpretation, and practical guidance for SCC mixture optimization. Full article

This study investigates the synergistic influence of recycled concrete aggregate (RCA) and recycled steel fibers (RSF) on the fresh and hardened performance of eco-efficient fiber-reinforced self-compacting concrete (SCC). Twelve C30/37.5 mixtures were produced using demolition waste as coarse RCA at replacement levels of 25, 50, 75, and 100% by mass, combined with RSF recovered from scrap tires at volume fractions of 0.25, 0.50, and 0.75%. Fresh properties were assessed in accordance with EFNARC guidelines using slump-flow (T500), V-funnel, L-box, and J-ring tests, while hardened performance was evaluated through compressive, splitting tensile, and flexural strengths at 28 days, together with density and ultrasonic pulse velocity (UPV). Increasing RCA and RSF contents reduced workability, reflected in lower slump-flow diameters and higher T500 and V-funnel times, although most mixtures maintained satisfactory self-compacting behaviour. Compressive strength decreased with RCA content and, to a lesser extent, with higher RSF, with a maximum reduction of about 39% at 100% RCA relative to the control mix, yet values remained structurally acceptable. In contrast, RSF markedly enhanced tensile and flexural responses: at 25% RCA, 0.75% RSF increased splitting tensile and flexural strengths by approximately 41% and 29%, respectively, compared with the corresponding fiber-free mix. RCA reduced density and UPV by about 10–14%, but these reductions were partially mitigated by RSF addition. Overall, the results demonstrate that SCC with moderate RCA (25–50%) and RSF (0.50–0.75%) can achieve a favourable balance between rheological performance and enhanced tensile and flexural behaviour, offering a viable composite solution for sustainable structural applications. Full article

The increasing emphasis on sustainability in construction materials has led to a surge of research focused on recycled aggregate self-compacting concrete (RA-SCC). However, the critical gap in predicting the compressive strength of concrete remains challenging because of the nonlinear interactions among the mix’s constituents. The distinct contribution of this study is to develop an interpretable machine learning (ML) framework to accurately forecast the compressive strength of RA-SCC and identify the most influential mix parameters. A dataset comprising 400 experimental samples was compiled, incorporating eight input variables: age, cement strength, cement, fly ash, blast furnace slag, water, recycled aggregate, and superplasticizer, with compressive strength as the output variable. Four ML algorithms such as support vector regression (SVR), random forest (RF), Multilayer Perceptron (MLP), and extreme gradient boosting (XGBoost) were trained and optimized using Bayesian-based hyperparameter tuning combined with 10-fold cross-validation. Among the evaluated models, XGBoost demonstrated superior accuracy, with R² = 0.98 and RMSE = 2.95 MPa during training, and R² = 0.96 with RMSE = 3.25 MPa during testing, confirming its robustness and minimal overfitting. SHAP (SHapley Additive exPlanations) evaluation indicates that superplasticizer, cement, and cement strength were the most dominant factors influencing compressive strength, whereas higher water content showed a negative impact. The developed framework demonstrates that explainable ML can effectively capture the complex nonlinear behavior of RA-SCC, offering a reliable tool for mix design optimization and sustainable concrete production. These findings contribute to advancing data-driven decision making in eco-efficient materials engineering. Full article

The development of sustainable self-compacting concrete (SCC) requires alternative binders that minimise ordinary Portland cement (OPC) consumption while ensuring long-term performance. This study investigates sulfate-activated SCC (SA SCC) incorporating high volumes of industrial by-products, whereby 72% ground granulated blast furnace slag (GGBS) and 18% fly ash (FA) were activated with varying proportions of OPC and gypsum. Quarry dust was used as a fine aggregate, while granite and electric arc furnace (EAF) slag served as coarse aggregates. Among all formulations, the binder containing 72% GGBS, 18% FA, 4% OPC, and 6% gypsum was identified as the optimum composition, providing superior mechanical performance across all curing durations. This mix achieved slump flow within the EFNARC SF2 class (700–725 mm), compressive strength exceeding 50 MPa at 270 days, and flexural strength up to 20% higher than OPC SCC. Drying shrinkage values remained below Eurocode 2 and ASTM C157 limits, while EAF slag increased density, but slightly worsened shrinkage compared to granite mixes. Microstructural analysis (SEM-EDX) confirmed that strength development was governed by discrete C-S-H and C-A-S-H gels surrounding unreacted binder particles, forming a dense interlocked matrix. The results demonstrate that sulfate activation with a 4% OPC + 6% gypsum blend enables the production of high-performance SCC with 94–98% industrial by-products, reducing OPC dependency and environmental impact. This work offers a practical pathway for low-carbon SCC. Full article

Autonomous multi-UAV formation control in cluttered urban environments remains challenging due to partial observability, dense and dynamic obstacles, and conflicting objectives (task efficiency, energy use, and safety). Yet many MARL-based approaches still collapse vector-valued objectives into a single hand-tuned reward and lack selective information fusion, leading to brittle trade-offs and poor scalability in urban clutter. We introduce a model-agnostic MARL framework—instantiated on MADDPG for concreteness—that augments a CTDE backbone with three lightweight attention modules (self, inter-agent, and entity) for selective information fusion, and a Pareto optimization module that maintains a compact archive of non-dominated policies to adaptively guide objective trade-offs using simple, interpretable rewards rather than fragile weightings. On city-scale navigation tasks, the approach improves final team success by 13–27 percentage points for N = 2–5 while simultaneously reducing collisions, tightening formation, and lowering control effort. These gains require no algorithm-specific tuning and hold consistently across the tested team sizes (N = 2–5), underscoring a stronger safety–efficiency trade-off and robust applicability in cluttered, partially observable settings. Full article

This study presents a novel hybrid deep learning framework integrating Feature Tokenizer-Transformer (FT-Transformer) with Masked Multi-Layer Perceptron (Masked MLP) for predicting the compressive strength of recycled aggregate self-compacting concrete (RASCC). The framework addresses incomplete data challenges through a missingness-aware fusion strategy and two-stage stacking scheme with Ridge regression. Using a dataset of 289 experimental records with 18 input parameters, the hybrid model achieved robust predictive performance with enhanced generalization stability (Test R² = 0.940, RMSE = 4.219 MPa) while demonstrating consistent predictions under data missingness conditions up to 25%. SHAP analysis revealed that cement content, water-to-binder ratio, and curing age are the dominant factors influencing RASCC strength. The proposed uncertainty quantification via split conformal prediction provides 90% coverage with average interval width of 8.32 MPa, enabling practical engineering applications with quantified reliability. Full article

Fire safety is a crucial issue for buildings, especially with the rise of modular construction, which demands materials that combine lightness with mechanical performance and stability. This study investigates a new concept for single-story modular constructions, made up of 3D cells assembled from thermally and acoustically pre-insulated concrete panels. These panels comprising four walls and two slabs forming the module, are stiffened, with thicknesses of only 5 cm for the walls and 7 cm for the slabs. Their constituent material is a self-compacting, high-volume steel-fiber concrete, containing 80 kg/m³ of steel fibers and 0.3 kg/m³ of polypropylene fibers. Experimental tests on a full-scale wall and slab revealed that adding 0.3 kg/m³ of polypropylene fibers effectively prevents concrete from splintering and achieves the necessary 30 min fire resistance. Standardized full-scale fire tests on walls and slabs confirmed that these thin structures meet fire resistance, insulation, and airtightness standards. The high volume of steel fibers provides ductility, maintaining structural integrity despite concrete spalling. The maximum spalling depth observed in some areas ranged 35 to 50 mm, without compromising structural performance. Overall, the modular system satisfies the fire safety requirements for structural stability (no collapse) and performance in single-story modular construction. Full article

The time-dependent structuration of cement pastes is a key parameter governing the fresh-state behavior of modern concretes. This study investigates the influence of four supplementary cementitious materials (SCMs): fly ash (FA), slag (S), limestone filler (LF), and metakaolin (MK) on both the total and irreversible structural build-up of cement pastes, under various temperatures (5, 20, 30 °C) and a constant replacement level of 30% at w/b = 0.45. Static yield stress was measured using a vane rheometer with or without re-shear to distinguish between the total (without re-shear) and irreversible (with re-shear) structural build-up. Complementary tests, including mini slump flow, isothermal calorimetry, and bleeding analysis, were conducted to assess the effect of SCMs on rheology, hydration and stability. Results show that all SCMs significantly reduced the rate and intensity of structural build-up compared with reference cement paste: after 90 min at 20 °C, the static yield stress (total structural build-up) was 1740 Pa for the reference mix and between 420 and 840 Pa for the blended systems. The irreversible fraction remained low (<10%) for all blended systems, confirming that early-age structuration is mainly governed by reversible flocculation rather than by hydration-driven bonding. Temperature significantly accelerated the total structural build-up in all mixtures; at 30 °C, the total build-up of slag-, LF-, and MK-blended pastes approached that of plain cement. However, while the reference cement paste exhibited a clear increase in irreversible structuration (from 25% at 20 °C to 35% at 30 °C), SCM-containing systems remained largely governed by reversible mechanisms, with the irreversible fraction consistently below 10%. These findings highlight the distinct roles of particle morphology, clinker dilution, and hydration kinetics in governing early structuration. Understanding these coupled mechanisms is essential for optimizing low-clinker binders used in self-compacting and 3D-printable concretes, where balancing flowability and early stability is critical. Full article

To promote the sustainable application of self-compacting lightweight aggregate concrete (SCLC) in cold regions and mitigate river sand shortages by substitution, this study investigates the impact of manufactured sand (MS) content on its freeze–thaw resistance. However, the micro-damage mechanism and a reliable damage model for MS-SCLC under freeze–thaw conditions remain lacking. Five groups of SCLC with varied manufactured sand content (0%, 30%, 60%, 80%, and 100%) were prepared. This study examined the behavior of SCLC under freeze–thaw conditions, with a focus on its frost durability and microstructural evolution. Furthermore, an SCLC freeze–thaw damage model for the manufactured sand content was established based on the Weibull distribution. Increasing the manufactured sand content conferred benefits on the compressive strength loss rate and relative dynamic elastic modulus; however, it had adverse consequences for the apparent morphology and mass loss rate. In conclusion, the SCLC mixture containing 60% manufactured sand displayed superior frost resistance, demonstrating a mass loss rate of 4.79%, a relative dynamic elastic modulus of 0.624, and a compressive strength loss rate of 38.46% after 200 freeze–thaw cycles. The identified optimal MS content (60%) and the established Weibull-based damage model provide crucial quantitative guidance for designing durable MS-SCLC structures in freeze–thaw environments. Full article

Natural plant fibres have gained growing research interest as a construction material due to efforts to reduce the negative environmental impacts caused by construction activities. Many researchers have investigated the suitability of utilising plant fibres as reinforcement in self-compacting concrete (SCC) as a substitute for synthetic fibres, recognising that the production of synthetic fibres generates significant amounts of CO₂. In this study a bibliometric analysis was conducted to investigate the current research achievements and map the scientific studies where plant fibres were used in SCC. A detailed discussion on the effects of various plant fibres and their underlying mechanisms on the properties of SCC is also provided. The findings indicated that using plant fibres typically reduces the flowability, filling ability, and passing ability of SCC due to the high water absorption of plant fibres, fibre and aggregate interlocking, and the fibre agglomeration effect. Incorporating plant fibres increases the viscosity and enhances the segregation resistance of SCC due to the strong cohesion between plant fibres and the cement matrix. The inclusion of plant fibres usually improves the mechanical properties of SCC because of the synergetic effects of plant fibres on crack-bridging and strain redistribution across the cross-section of SCC. Adding plant fibres to SCC also reduces drying shrinkage and cracking due to the fibre bridging effect, while generally lowering the resistance to sulphate attack, acid attack, and freeze–thaw cycles and increasing the water absorption rate of SCC due to the increased porosity of the mix. A comprehensive overview of research gaps and future perspectives for further investigations is also provided in this study. Full article

The concrete pouring process is difficult to observe inside formwork. With increasingly complex formwork systems and denser reinforcement layouts, honeycomb defects and surface pores are prone to form at beam–column joint core locations. The modeling of pouring processes that were performed earlier is insufficient and there is relatively little research on simulating concrete void defects at typical joints. Therefore, a refined numerical model based on the three-dimensional particle flow method was established to simulate the flow of fresh concrete within formwork and predict concrete voids after pouring. The feasibility of the particle flow method was verified through numerical simulations of slump flow and J-ring tests. Several groups of joint models were set up based on different influencing factors, and the developed particle flow model was used for pouring simulations to investigate the influence of various factors on concrete void formation. The results show that the void volume and distribution patterns obtained from experiments and simulations are basically consistent. The numerical model can accurately simulate the working performance of self-compacting concrete and predict the size and location distribution of pouring defects. Based on both experimental and numerical studies, the following suggestions are proposed to avoid potential void defects in practical concrete pouring projects: reasonably select the number and diameter of joint longitudinal bars; appropriately increase the spacing of column stirrups; appropriately reduce the maximum coarse aggregate particle size; and choose concrete with better fluidity and filling ability. Full article

The growing demand for radiation-shielded infrastructure highlights the need for materials that balance shielding performance with environmental and economic sustainability. Heavyweight self-compacting concretes (HWSCC), commonly produced with barite (BaSO₄) or magnetite (Fe₃O₄) aggregates, lack systematic life cycle comparisons. The aim of this study is to systematically compare barite- and magnetite-based HWSCC in terms of life cycle environmental impacts, life cycle cost, functional performance (strength and shielding), and end-of-life circularity, in order to identify the more sustainable and cost-effective material for radiation shielding infrastructure. This study applies cradle-to-grave life cycle assessment (LCA) and life cycle cost analysis (LCC), in accordance with ISO 14040/14044 and ISO 15686-5, to evaluate barite- and magnetite-based HWSCC. Results show that magnetite concrete reduces global warming potential by 19% eutrophication by 24%, and fossil resource depletion by 23%, while lowering life cycle costs by ~23%. Both concretes achieve comparable compressive strength (~48 MPa) and shielding efficiency (µ ≈ 0.28–0.30 cm⁻¹), meeting NCRP 147 and IAEA SRS-47 standards. These findings demonstrate that magnetite-based HWSCC offers a more sustainable, cost-effective, and ethically sourced alternative for radiation shielding in healthcare, nuclear, and industrial applications. In addition, the scientific significance of this work lies in establishing a transferable methodological framework that combines LCA, LCC, and performance-normalized indicators. This enables scientists and practitioners worldwide to benchmark heavyweight concretes consistently and to adapt sustainability-informed material choices to their own regional contexts. Full article