Search Results (1,366)

In this study, the two-dimensional aligned steel fiber-reinforced micro-expansive concrete (2D) was prepared, aiming to address the inherent vulnerabilities of concrete, such as early-age shrinkage cracking and low tensile ductility. For this purpose, the steel fibers and expansive agent were utilized. Furthermore, the planar rotating magnetic field was used to randomly distribute the steel fibers in a two-dimensional plane. In order to verify its superior mechanical and shrinkage properties, the compressive, fracture and drying shrinkage tests were carried out. The results demonstrate that the 2D alignment method enhances the fiber utilization efficiency. Compared with fiber-free groups, the compressive strength and fracture parameters of specimens incorporating steel fibers were improved. Furthermore, compared with randomly distributed steel fiber-reinforced micro-expansive concrete (RD), the 2D alignment method made the cubic compressive strength and fracture energy improve 8–14.2% and 19.4–110%, respectively. Additionally, the advantage of the fiber 2D alignment method was also reflected in the inhibition of drying shrinkage. Compared with normal concrete, the 180-day shrinkage strain of the 2D1.2 group was reduced to 200 με (only 19.5% of that of normal concrete, or 30.6% of that of micro-expansive concrete). Mechanistically, these superior performances are fundamentally governed by a coupling effect: chemical shrinkage compensation and physical alignment constraint. Full article

Today, renewable energy is receiving increasing global attention. However, the operation of such energy systems is associated with several challenges, including natural uncertainty and intermittency at different times of the day. Furthermore, to overcome these challenges, there is an increasing interest in developing energy storage systems. Compressed air energy storage (CAES) is considered a promising, cost-effective, and environmentally friendly technology. The present study proposes a novel CAES system distinct from conventional designs. The proposed storage system can store energy by feeding the excess electrical energy to a motor to drive a large-diameter piston to compress and store air in a container. Then, the energy is extracted when needed by releasing the piston to drive the generator back. This study evaluates the feasibility via a thermodynamic model of all components. We examine the effects of (i) piston speed and piston-air volume ratio, (ii) initial pressure, and (iii) container volume. We also assess how container volume scales with the maintained initial pressure. Results are compared against an adiabatic baseline. The results demonstrate that near-isothermal compression/expansion can improve energy density and storage efficiency by generating two times more recoverable work than the adiabatic in the same volume, and an efficiency of 76% can be reached, while the realistic efficiency achieves around 50%. It also shows that the volume of the container for an amount of energy depends on the initial pressure maintained before the charging cycle. As a result, when the initial pressure increases, the volume of the container required decreases, and for the same volume, the results show that more energy can be stored by maintaining the initial pressure. Therefore, this system could be considered an attractive solution to the integration of intermittent renewable energy sources. Full article

Lithium-ion pouch cells exhibit significant irreversible expansion during long-term cycling, which determines overall performance and induces degradation failure without an appropriate mechanical fixture. However, the synergistic mechanism of mechanical pre-torque and battery state on battery electrochemical performance is unclear. To address this issue, this study reveals the electrochemical characteristic evolution of commercial lithium-ion pouch cells during cycling degradation, under varying mechanical pre-torques (0 N·m, 0.5 N·m, 1 N·m, and 1.5 N·m) and at different states of charge (SOCs, 0%, 25%, 50%, 75%, and 100%). Results indicate that moderate pressure (0.5 N·m) optimizes the electrode–electrolyte contact, reducing solid–electrolyte interphase resistance (R_SEI), ohmic resistance (R_O), charge transfer resistance (R_ct), and Warburg coefficient (W) by over 55%, 60%, 30% and 20%, respectively, compared with the free state. High pressure (1.5 N·m) induces impedance rebound due to pore compression, with the increment ranging from 20% to 40%. Furthermore, synergistic impact analysis proves that pressure alters impedance sensitivity to SOC, with changing rates amplifying from <5% per SOC unit under low pressure to 10–15% under high pressure, particularly exacerbating interface passivation at low SOC and side reactions at high SOC. Moreover, a Gaussian process regression (GPR) based adaptive SOC estimation model is developed, incorporating impedance features and pressure paths, achieving a root mean square error of 2.1% and enhancing accuracy by 10–15% over conventional methods in high-pressure scenarios. This study provides guidance for the next-generation pouch cell module design and management. Full article

When conventional FRP composites are applied to confine rectangular concrete columns, strength enhancement is often limited due to the highly non-uniform lateral expansion of sections with a large aspect ratio (e.g., 2.0). High ductile FRP (HDFRP), a composite of glass fibers and polypropylene (PP) fibers, improves column strength while alleviating corner stress concentration in square sections, demonstrating its promising application potential for strengthening members with rectangular cross-sections. Yet existing studies on HDFRP have primarily focused on circular and square sections. To explore its applicability to rectangular cross-sections, this study conducted axial compression tests on HDFRP-confined rectangular short concrete columns (HDFRP-CRCC), investigating the effects of aspect ratio, corner radius, and FRP thickness on their mechanical behavior. The test results demonstrate that the HDFRP composite material can significantly enhance the overall strength and axial deformability of rectangular concrete columns, thereby effectively overcoming the limited strength enhancement associated with conventional FRP systems. Based on the experimental results, a design-oriented model is developed to offer theoretical support for the application of HDFRP in strengthening rectangular frame structures. Full article

Hybrid reinforced concrete (HRC) sections combining steel and fiber-reinforced polymer (FRP) bars provide a structural solution that balances durability, load-bearing capacity and energy dissipation. However, the absence of unified design provisions and the coexistence of distinct safety formats in European and American codes complicate the consistent assessment of ultimate limit state behavior under combined axial force and bending moment. In this study, a strain-based sectional model founded on compatibility and internal force equilibrium is implemented through a layer-by-layer numerical integration procedure to generate axial force–bending moment (N–M) interaction domains and moment–curvature (M–$\chi$) relationships. The formulation is extended to a dimensionless framework in terms of normalized axial load, bending moment, total hybrid mechanical reinforcement ratio ${\omega }_h$ and hybridization parameter R. The analysis is conducted within two regulatory formats: the European framework based on Eurocode 2 and CNR-DT 203 R1/2026 and the American framework based on ACI 318-25 and ACI 440.11-22. The results show that increasing ${\omega }_h$ leads to a progressive expansion of the interaction domain and modifies the transition between FRP rupture-controlled and steel-yielding-controlled limit states. Increasing R shifts balanced conditions towards higher axial compression and bending levels. Differences between the two regulatory approaches are observed in terms of predicted curvature capacity and design resistance within the N–M domain, reflecting the distinct safety formats adopted. The proposed dimensionless parametric formulation enables consistent comparison of hybrid configurations and provides basis for interpreting failure-mode transitions and deformation capacity of HRC sections under combined axial and flexural actions. Full article

The rapid expansion of sustainable construction practices has significantly increased research on recycled aggregate concrete (RAC) over the past four decades. However, despite the growing volume of studies, a comprehensive longitudinal assessment of the thematic and structural evolution of RAC research remains limited. This study presents a bibliometric and thematic evolution analysis of global research on recycled aggregate concrete from 1983 to 2025, based on 1624 documents indexed in Scopus and analyzed using PRISMA guidelines and VOSviewer mapping techniques. Results reveal four indicative stages of development: (i) an exploratory feasibility phase focused on compressive strength and replacement ratios (1983–2000); (ii) a mechanical validation phase emphasizing durability and interfacial transition zone performance (2000–2010); (iii) a performance enhancement phase integrating supplementary cementitious materials and service-life assessment (2010–2018); and (iv) a recent sustainability-driven phase characterized by life-cycle assessment, circular economy frameworks, and emerging AI-assisted optimization approaches (post-2018). China, India, and the United States dominate scientific production, while co-citation networks highlight the consolidation of specialized yet interconnected research communities. Keyword evolution analysis indicates a progressive shift from mechanical feasibility toward environmental impact mitigation and predictive modeling. Despite substantial advances, research gaps persist in tropical climate performance assessment, full-scale structural applications, and standardized mix-design methodologies for high-replacement RAC. The findings provide a structured understanding of the intellectual structure and evolution of the field, offering guidance for future research directions and performance-based sustainable concrete design strategies. Full article

The bond strength of corroded reinforced concrete (CRC) structures is critical for structural safety and long-term durability. However, the corrosion-induced bond degradation process is influenced by multiple, coupled factors and exhibits complex, nonlinear behavior, making it difficult for traditional theoretical models to provide accurate predictions. To address this challenge, this study proposes a novel, unified prediction framework based on machine learning techniques. A total of 391 experimental datasets were collected and compiled, covering key parameters including bond strength, reinforcing bar diameter, yield strength, concrete cover thickness, concrete compressive strength, mass loss rate due to corrosion, and the presence of stirrups. Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost) algorithms were employed to develop predictive models for bond strength. Model training and testing were performed using 10-fold cross-validation. Furthermore, the SHapley Additive exPlanations (SHAP) approach was introduced to enhance model interpretability and quantitatively assess the influence of each input feature, revealing that mass loss rate and bar diameter are the dominant factors. This study effectively bridges the research gap between high-precision black-box algorithms and the need for physical interpretability in engineering. The results demonstrate that (1) the proposed XGBoost model significantly outperforms traditional empirical formulations, achieving a high coefficient of determination (R² = 0.893) and a much lower coefficient of variation (25.85%) on the testing set, and (2) the SHAP analysis reveals that the machine learning predictions are highly consistent with established physical mechanisms, successfully capturing the negative impact of splitting tensile stresses caused by rust expansion and the positive confinement effect of stirrups. Overall, the proposed models demonstrate superior accuracy, robustness, and generalization capability, providing an effective tool and theoretical basis for evaluating bond behavior and designing durable CRC structures with broad engineering applicability. Full article

Text-attributed graphs (TAGs) require models to jointly exploit node text and graph structure, yet doing so effectively remains difficult when node text is sparse and the structural context is large. Here, we propose STAGE (Semantic and Topological Augmented Graph Embedding), a two-stage framework for representation learning on TAGs. In Stage I, a frozen large language model is used offline to generate explanatory text that enriches compressed node attributes without introducing online LLM training cost. In Stage II, STAGE performs structure-aware representation learning under a fixed global token budget by combining random-walk-based structural context with graph-conditioned token reduction before PLM encoding. This design preserves informative semantic content while preventing unconstrained sequence expansion. Experiments on seven benchmark datasets show that STAGE consistently outperforms strong baselines under the same evaluation setting and maintains favorable efficiency under bounded input-length constraints. Full article

Efficiency improvements in gas turbines have been realized in recent decades by raising the turbine inlet temperature. This work devotes attention to pressure-gain combustion (PGC), which is a technology capable of yielding the same time-averaged combustor outlet temperature as conventional Brayton–Joule cycles but at a higher pressure. Here, PGC is implemented in a thermodynamic cycle wherein the compression system operates at a lower pressure ratio compared to the reference Brayton–Joule cycle. Focusing on E-, F- and H-class gas turbines, representative of three different technologies, the possible PGC advantages in both simple- and combined-cycle modes are investigated by means of in-house simulation code. Specifically, this work includes the energy penalty related to the PGC system cooling in the cycle analysis. In detail, the effects of different coolant amounts on the PGC system, as well as the lower efficiency at the first expansion stage compared to conventional gas turbine systems, are analyzed. Among the three classes of gas turbines, E is the one wherein the advantages are more significant, with ultimate efficiency values in simple-cycle mode calculated in the range of 38% to 41%. The higher the gas turbine technology and power class, the lower the benefit, and current H-class gas turbines already start from a higher efficiency level. Anyway, focusing on the latter, performance improvements for the PGC combined cycle seem to be possible, with efficiency greater than 65%, exceeding the current state-of-the-art systems. Full article

The black-winged kite algorithm (BKA) integrates the Cauchy mutation strategy and the leader selection strategy to simulate high-altitude circling exploration, fixed-point diving attack, and group cooperative migration of the black-winged kites to approximate the global optimal solution. The BKA exhibits deficiencies in ponderous convergence efficacy, inefficient calculation precision, and insufficient population diversity. To strengthen the convergence property and computational practicability, an enhanced BKA with multiple strategies (MSBKA) is advocated to accommodate global optimization and constrained engineering applications. The objective is to systematically verify its advancement and competitiveness and accurately actualize the global optimal solution. The ranking-based differential mutation can strengthen population information interaction, accelerate convergence efficiency, restrain premature convergence, diminish homogenization competition, promote exploration and exploitation, intensify elite individual guidance, downscale ineffective iterations, and materialize orderly population renewal. The simplex method can execute the local refinement operations of reflection, expansion, compression and contraction, strengthen local mining efficiency, ameliorate solution accuracy, abate parameter sensitivity, eschew local optimal traps, accelerate accurate convergence, and preserve the optimal individual potential. The elite opposition-based learning strategy can fabricate reverse solutions, expand the monolithic detection space, shorten the convergence process, elevate the quality of initial and iterative solutions, boost population diversity, guide intelligent search direction, and relieve premature convergence. The MSBKA utilizes deficiency orientation, strategy adaptation, and collaborative search to accomplish the realistic demands of high-precision, high-efficiency and strong constraint adaptation, surmount the static trade-off dilemma, endow a strong directional abscond mechanism to replace random perturbation, and actualize the inertia of directional exploration and the blind spots of solution exploitation. Twenty-three benchmark functions and six real-world engineering designs are employed to authenticate theoretical superiority and engineering practicability. The experimental results demonstrate that the MSBKA incorporates strong practicability and reliability to strengthen information interaction, restrain search stagnation, diminish convergence oscillation and fluctuation, facilitate globalized discovery and localized extraction, expedite convergence efficacy, ameliorate solution precision, and consolidate stability and robustness. Full article

Due to the strong nonlinearity and large deformation characteristics of dielectric elastomer actuators (DEAs), the dynamic performance design of their actuators faces the challenge of complex multi-parameter coupling. This paper establishes a unified parameterized dynamic equation based on analytical mechanics, focusing on the influence of electric field, excitation frequency, driving waveform, material properties, geometric dimensions, and pre-stretch ratio on their dynamic performance indicators. The study finds that the pre-stretch ratio, by changing the system’s potential energy and stiffness, not only directly affects the system’s dynamic performance. More importantly, throughout a complete driving voltage waveform cycle, the DEA exhibits alternating compression and expansion—a phenomenon rarely reported in existing studies. Accordingly, this study defines two new performance indicators: maximum stretch ratio (characterizing expansion) and minimum stretch ratio (characterizing compression). Based on this, the paper proposes a visualization design method using radar charts. By normalizing the performance indicators and plotting performance indicator radar charts, the interaction of various parameters can be intuitively presented, providing a new approach for the customized dynamic design of DEAs. Full article

Developing sustainable and fire-resistant infrastructure is a critical technological, economic, and environmental challenge for modern construction stakeholders. Traditional cementitious composites experience severe microstructural degradation under extreme temperatures and their high carbon footprint exacerbates global environmental concerns. While the individual high-temperature behaviors of supplementary cementitious materials and fibers have been widely studied, the long-term synergistic mechanisms of high-volume fly ash combined with steel fibers under extreme thermal shock remain critically underinvestigated. To address this urgent need and bridge this scientific gap, hybrid mortars incorporating high-volume fly ash (FA) and steel fibers (SF) were tested under prolonged curing (150 days) and extreme heat (up to 600 °C). In terms of engineering and construction effects, the optimal CFA50-F hybrid composite delivered the highest residual compressive and flexural capacities (retaining nearly 60% of its late-age compressive strength at 32.00 MPa), preserved acoustic continuity (restricting UPV loss to 41.4%), and severely restricted high-temperature capillary permeability (limiting the water absorption increase to 49.7%) compared to traditional plain matrices. Scientifically, this superior resistance is governed by a two-step protective mechanism. High-volume FA chemically stabilizes the matrix by consuming vulnerable portlandite and preventing the formation of expansive calcium oxide. Simultaneously, ultra-fine FA particles physically densify the interfacial transition zones, securely anchoring the steel fibers and preventing premature high-temperature pull-out, while enabling the fibers to bridge thermally induced macro-cracks successfully. Environmentally and economically, an annualized service-life Life Cycle Assessment (LCA) revealed that substituting 50% of the cement with FA completely subsidizes the production-stage carbon penalty of the metallic reinforcement. By extending the operational lifespan to 40 years, the CFA50-F composite achieves a net 27% reduction in annualized global warming potential, providing a highly sustainable and cost-effective material solution. Full article

(1) Background: Posterior crossbite associated with maxillary transverse deficiency is commonly managed with maxillary expansion, yet the correspondence between laboratory activation behavior and the clinical response of nickel–titanium leaf-spring expanders remains insufficiently defined; therefore, this study aimed to compare in vitro and in vivo performance of the Leaf Expander^® and to assess their agreement. (2) Methods: A retrospective sample of 15 mixed-dentition patients (7–10 years) treated at two university centers with a Leaf Expander^® (6 mm screw; 900 g) was evaluated; interpremolar (E–E), intermolar (6–6), and intercanine (C–C) distances were recorded at baseline (T0, digital models) and at follow-up visits (T1–T5, caliper measurements), while mechanical compression testing (Instron 3365) quantified force release across the activation sequence; normality (Shapiro–Wilk), parametric analyses, and Pearson correlation were used. (3) Results Posterior crossbite correction was achieved in all completed cases, with mean total increases (T0–T5) of 5.4 mm (E–E), 4.4 mm (6–6), and 6.0 mm (C–C); early expansion (T1–T0) averaged 2.5 mm at E–E, and laboratory curves showed an activation peak followed by sustained force release (~6.5–9 N) and a residual-load phase. Agreement between declared activation and clinical response was higher for E–E and 6–6 than for C–C, which showed greater variability. (4) Conclusions: These findings support the Leaf Expander^® as an effective compliance-free slow expansion device and indicate that laboratory force behavior can help interpret the clinical expansion timeline, including delayed expression after activation. Full article

Predicting the concrete breakout strength of an expansion anchor embedded in short carbon fiber-reinforced concrete (SCFRC) is challenging due to the nonlinear and heterogeneous nature of fiber–matrix interaction. This study develops an Artificial Neural Network (ANN) model to estimate the breakout capacity of a single expansion anchor installed in SCFRC. Experimental data from 48 cases covering variations in compressive strength, tensile strength, fiber volume fraction, and fiber length were used to train and validate multiple ANN architectures in MATLAB’s Regression Learner. A 4-4-1 trilayered ANN with Rectified Linear Unit (ReLU) activation and 5-fold cross-validation achieved the most reliable performance, yielding R² values of 0.6726 (validation) and 0.9376 (test), with correspondingly low RMSE, MAE, and scatter index (SI < 0.1). SHAP-based sensitivity analysis identified tensile strength as the dominant predictor, contributing 70.78% to model output influence. ANN predictions were compared with the Concrete Capacity Design (CCD) model adopted by ACI and the National Structural Code of the Philippines (NSCP) and a multiple linear regression (MLR) model, showing that while the ANN is not the most precise model, it provides acceptable accuracy and captures nonlinear concrete breakout behavior more effectively than linear approaches. Results demonstrate that the ANN framework offers a viable data-driven tool for estimating concrete breakout strength in SCFRC anchorage systems. Full article

Coal gangue (CG) and iron tailings (ITs) are major industrial solid wastes, and their high-value reuse is crucial for sustainable construction materials. This study explores the feasibility of fabricating sintered porous bricks using CG and ITs as primary constituents, with shale as an auxiliary component. To evaluate durability in cold regions, laboratory freeze–thaw (F-T) cycling experiments were conducted. A degradation assessment framework based on the Wiener stochastic process was developed to predict frost-resistance service life by integrating experimental data with regional climatic conditions. Results show that the fabricated bricks exhibit satisfactory initial properties, with a compressive strength of 10.6 MPa and water absorption of 13.3%. With increasing F-T cycles, compressive strength decreases significantly, accompanied by increased mass loss and water absorption. Stress–strain analysis reveals progressive stiffness reduction and a transition from brittle to ductile failure. Microstructural observations confirm degradation of the glassy phase, pore expansion, and enhanced interconnectivity. The Wiener process-based model effectively describes the stochastic accumulation of F-T damage. By establishing equivalence between laboratory and natural F-T cycles, the long-term service life of coal gangue–iron tailing sintered porous bricks (CG-IT SPBs) in cold regions is theoretically evaluated. This work provides an integrated understanding of F-T damage behavior and establishes a scientific foundation for durability-oriented design and application of such bricks in extremely cold environments. Full article