Search Results (3,233)

Concrete cracks and sulfate degradation severely compromise structural durability, highlighting the need for sustainable solutions to enhance longevity and minimize environmental impact. This study assesses the efficacy of bacterial self-healing technology utilizing Bacillus megaterium (BM) and Bacillus sphaericus (BS) in enhancing the resistance of concrete to sulfate attacks and improving its mechanical properties. Bacterial suspensions (1% and 2.5% of cement weight) were mixed with concrete containing silica fume or fly ash (10% of cement weight) and cured in freshwater or sulfate solutions (2%, 5%, and 10% concentrations). Specimens were tested for compressive strength, flexural strength, and microstructure using a Scanning Electron Microscope (SEM), Energy-Dispersive X-ray Spectroscopy (EDS), and X-ray diffraction (XRD) at various ages. The results indicate that a 2.5% bacterial content yielded the best performance, with BM surpassing BS, enhancing compressive strength by up to 41.3% and flexural strength by 52.3% in freshwater-cured samples. Although sulfate exposure initially improved early-age strength by 1.97% at 7 days, it led to an 8.5% loss at 120 days. Bacterial inclusion mitigated sulfate damage through microbially induced calcium carbonate precipitation (MICP), sealing cracks, and bolstering durability. Cracked specimens treated with BM recovered up to 93.1% of their original compressive strength, promoting sustainable, sulfate-resistant, self-healing concrete for more resilient infrastructure. Full article

Periodic visual inspections are currently conducted to maintain the condition of railway bridges. These inspections rely on direct visual assessments by human inspectors, often requiring specialized equipment such as aerial ladders. However, this method is not only time-consuming and costly but also involves significant safety risks. Therefore, there is a growing need for a more efficient and reliable alternative to traditional visual inspections of railway bridges. In this study, we evaluated and compared the performance of damage detection using U-Net-based deep learning models on images captured by unmanned aerial vehicles (UAVs). The target damage types include cracks, concrete spalling and delamination, water leakage, exposed reinforcement, and paint peeling. To enable multi-class segmentation, the U-Net model was trained using three different loss functions: Cross-Entropy Loss, Focal Loss, and Intersection over Union (IoU) Loss. We compared these methods to determine their ability to distinguish actual structural damage from environmental factors and surface contamination, particularly under real-world site conditions. The results showed that the U-Net model trained with IoU Loss outperformed the others in terms of detection accuracy. When applied to field inspection scenarios, this approach demonstrates strong potential for objective and precise damage detection. Furthermore, the use of UAVs in the inspection process is expected to significantly reduce both time and cost in railway infrastructure maintenance. Future research will focus on extending the detection capabilities to additional damage types such as efflorescence and corrosion, aiming to ultimately replace manual visual inspections of railway bridge surfaces with deep-learning-based methods. Full article

In this study, an effective strengthening method was investigated to improve the seismic performance of masonry brick walls. The strengthening method comprised the use of shotcrete, which was applied in both glass fiber-reinforced and unreinforced forms for steel plates and tie rods. Thirteen wall specimens constructed with vertical perforated masonry block bricks were tested under diagonal compression in accordance with ASTM E519 (2010). Reinforcement plates with different thicknesses (1.5 mm, 2 mm, and 3 mm) were anchored using 6 mm diameter tie rods. A specially designed steel frame and an experimental loading program with controlled deformation increments were employed to simulate the effects of reinforced concrete beam frame system on walls under the effect of diagonal loads caused by seismic loads. In addition, numerical simulations were conducted using three-dimensional finite element models in Abaqus Explicit software to validate the experimental results. The findings demonstrated that increasing the number of tie rods enhanced the shear strength and overall behavior of the walls. Steel plates effectively absorbed tensile stresses and limited crack propagation, while the fiber reinforcement in the shotcrete further improved wall strength and ductility. Overall, the proposed strengthening techniques provided significant improvements in the seismic resistance and energy absorption capacity of masonry walls, offering practical and reliable solutions to enhance the safety and durability of existing masonry structures. Full article

For the first time, comprehensive investigations into the tensile load-bearing behavior and crack formation of reinforced concrete based on ultrasound are presented. Uniaxial tensile tests are performed on reinforced concrete tension members equipped with embedded ultrasonic transducers. Key mechanical parameters across all ranges of tensile behavior are continuously quantified by recording ultrasonic signals and evaluated with coda wave interferometry. The investigations include member configurations of different lengths to cover different numbers of cracks. For reference, crack patterns and crack widths are analyzed using digital image correlation, while the strain in the reinforcement is monitored with distributed fiber optic sensors. For the first time, a direct proportional relationship between the relative velocity change in ultrasonic signals and crack widths is established in the ranges of crack formation and stabilized cracking. In the non-cracked state, linear correlations are found between the velocity change and the average strain, as well as the length of the specimens. The experimental results significantly enhance the general understanding of the phenomena related to ultrasonic signals in flexural reinforced concrete members, particularly concerning cracking in the tensile zone. Consequently, this study contributes to the broader objective of employing coda wave interferometry to evaluate the condition of infrastructure. Full article

Ferronickel slag and ground granulated blast-furnace slag (GGBFS) are solid waste by-products from the metallurgical industry. When incorporated into concrete, they help promote resource utilization, reduce hydration heat, and lower both solid waste emissions and the carbon footprint. To facilitate the application of ferronickel slag–GGBFS concrete in 3D printing, this study examines how aggregate size and nozzle diameter affect its performance. The investigation involves in situ printing, rheological characterization, mechanical testing, and scanning electron microscopy (SEM) analysis. Results indicate that excessively large average aggregate size negatively impacts the smooth extrusion of concrete strips, resulting in a cross-sectional width that exceeds the preset dimension. Excessively small average aggregate size results in insufficient yield stress, leading to a narrow cross-section of the extruded strip that fails to meet printing specifications. The extrusion performance is closely related to both the average aggregate size and nozzle diameter, which can significantly influence the normal extrusion stability and print quality of 3D printed concrete strips. The thixotropic performance improves with an increase in the aggregate size. Both compressive and flexural strengths improve with increasing aggregate size but decrease with an increase in the printing nozzle size. Anisotropy in mechanical behavior decreases progressively as both parameters mentioned increase. By examining the cracks and pores at the interlayer interface, this study elucidates the influence mechanism of aggregate size as well as printing nozzle parameters on the mechanical properties of 3D printed ferronickel slag–GGBFS concrete. This study also recommends the following ranges. When the maximum aggregate size exceeds 50% of the nozzle diameter, smooth extrusion is not achievable. If it falls between 30% and 50%, extrusion is possible but shaping remains unstable. When it is below 30%, both stable extrusion and good shaping can be achieved. Full article

Rapid pavement repair demands materials that combine accelerated strength gains, dimensional stability, long-term durability, and sustainability. However, finding materials or formulations that offer these balances remains a critical challenge. This study systematically evaluates two polymer-modified belitic calcium sulfoaluminate (CSA) concretes—CSAP (powdered polymer) and CSA-LLP (liquid polymer admixture)—against a traditional Type III Portland cement (OPC) control under both laboratory and realistic outdoor conditions. Laboratory specimens were tested for fresh properties, early-age and later-age compressive, flexural, and splitting tensile strengths, as well as drying shrinkage according to ASTM standards. Outdoor 5 × 4 × 12-inch slabs mimicking typical jointed plain concrete panels (JPCPs), instrumented with vibrating wire strain gauges and thermocouples, recorded the strain and temperature at 5 min intervals over 16 weeks, with 24 h wet-burlap curing to replicate field practices. Laboratory findings show that CSA mixes exceeded 3200 psi of compressive strength at 4 h, but cold outdoor casting (~48 °F) delayed the early-age strength development. The CSA-LLP exhibited the lowest drying shrinkage (0.036% at 16 weeks), and outdoor CSA slabs captured the initial ettringite-driven expansion, resulting in a net expansion (+200 µε) rather than contraction. Approximately 80% of the total strain evolved within the first 48 h, driven by autogenous and plastic effects. CSA mixes generated lower peak internal temperatures and reduced thermal strain amplitudes compared to the OPC, improving dimensional stability and mitigating restraint-induced cracking. These results underscore the necessity of field validation for shrinkage compensation mechanisms and highlight the critical roles of the polymer type and curing protocol in optimizing CSA-based repairs for durable, low-carbon pavement rehabilitation. Full article

In high geothermal tunnels (>28 °C), curing temperature critically affects early-age concrete mechanics and durability. Uniaxial compression tests under six curing conditions, combined with CT scanning and machine learning-based crack analysis, were used to evaluate the impacts of curing age, temperature, and fiber content. The test results indicate that concrete exhibits optimal development of mechanical properties under ambient temperature conditions. Specifically, the elastic modulus increased by 33.85% with age in the room-temperature group (RT), by 23.35% in the fiber group (F), and decreased by 26.75% in the varying-temperature group (VT). A Weibull statistical damage-based constitutive model aligned strongly with the experimental data (R² > 0.99). Fractal analysis of CT-derived cracks revealed clear fractal characteristics in the log(Nr)–log(r) curves, demonstrating internal damage mechanisms under different thermal histories. Full article

To address the inefficiency of traditional concrete crack detection methods and the heavy reliance of supervised learning on extensive labeled data, in this study, an intelligent assessment method of concrete damage based on pseudo-label semi-supervised learning and fractal geometry theory is proposed to solve two core tasks: one is binary classification of pixel-level cracks, and the other is multi-category assessment of damage state based on crack morphology. Using three-channel RGB images as input, a dual-path collaborative training framework based on U-Net encoder–decoder architecture is constructed, and a binary segmentation mask of the same size is output to achieve the accurate segmentation of cracks at the pixel level. By constructing a dual-path collaborative training framework and employing a dynamic pseudo-label refinement mechanism, the model achieves an F1-score of 0.883 using only 50% labeled data—a mere 1.3% decrease compared to the fully supervised benchmark DeepCrack (F1 = 0.896)—while reducing manual annotation costs by over 60%. Furthermore, a quantitative correlation model between crack fractal characteristics and structural damage severity is established by combining a U-Net segmentation network with the differential box-counting algorithm. The experimental results demonstrate that under a cyclic loading of 147.6–221.4 kN, the fractal dimension monotonically increases from 1.073 (moderate damage) to 1.189 (failure), with 100% accuracy in damage state identification, closely aligning with the degradation trend of macroscopic mechanical properties. In complex crack scenarios, the model attains a recall rate (Re = 0.882), surpassing U-Net by 13.9%, with significantly enhanced edge reconstruction precision. Compared with the mainstream models, this method effectively alleviates the problem of data annotation dependence through a semi-supervised strategy while maintaining high accuracy. It provides an efficient structural health monitoring solution for engineering practice, which is of great value to promote the application of intelligent detection technology in infrastructure operation and maintenance. Full article

Timely and accurate identification of surface damage in hydraulic structures is essential for maintaining structural integrity and ensuring operational safety. Traditional manual inspections are time-consuming, labor-intensive, and prone to subjectivity, especially for large-scale or inaccessible infrastructure. Leveraging advancements in aerial imaging, unmanned aerial vehicles (UAVs) enable efficient acquisition of high-resolution visual data across expansive hydraulic environments. However, existing deep learning (DL) models often lack architectural adaptations for the visual complexities of UAV imagery, including low-texture contrast, noise interference, and irregular crack patterns. To address these challenges, this study proposes a lightweight, robust, and high-precision segmentation framework, called LFPA-EAM-Fast-SCNN, specifically designed for pixel-level damage detection in UAV-captured images of hydraulic concrete surfaces. The developed DL-based model integrates an enhanced Fast-SCNN backbone for efficient feature extraction, a Lightweight Feature Pyramid Attention (LFPA) module for multi-scale context enhancement, and an Edge Attention Module (EAM) for refined boundary localization. The experimental results on a custom UAV-based dataset show that the proposed damage detection method achieves superior performance, with a precision of 0.949, a recall of 0.892, an F1 score of 0.906, and an IoU of 87.92%, outperforming U-Net, Attention U-Net, SegNet, DeepLab v3+, I-ST-UNet, and SegFormer. Additionally, it reaches a real-time inference speed of 56.31 FPS, significantly surpassing other models. The experimental results demonstrate the proposed framework’s strong generalization capability and robustness under varying noise levels and damage scenarios, underscoring its suitability for scalable, automated surface damage assessment in UAV-based remote sensing of civil infrastructure. Full article

The aim of this paper is to present the results of an experimental and numerical investigation into the degradation of reinforced concrete (RC) specimens subjected to an accelerated corrosion process using impressed current in the presence of chloride ions. The corrosion of the rebars was carried out using three current densities (50, 100, and 200 µA/cm²) and various exposure times. The experimental results characterised the internal degradation of the RC specimens through measurement of the corrosion product thicknesses at the steel–concrete interface; the widths, lengths and orientations of internal concrete cracks; and the external concrete crack widths. In addition, numerical modelling of the corroded RC specimens was conducted to describe the crack patterns. The comparison between the experimental and numerical results demonstrated a high degree of correlation, providing insights into the degradation process of RC specimens due to corrosion. Full article

The operation of heavy-haul railway trains with large loads results in significant cracking issues in reinforced concrete beams. Atmospheric carbon dioxide, oxygen, and moisture from the atmosphere penetrate into the beam interior through these cracks, accelerating the carbonation of the concrete and the corrosion of the steel bars. The rust-induced expansion of steel bars further exacerbates the cracking of the beam. The interaction between environmental factors and beam cracks leads to a rapid decline in the durability of the beam. To address this issue, a multi-physics field coupling durability assessment method was proposed, considering concrete beam cracking, concrete carbonation, and steel bar corrosion. The interaction among these three factors is achieved through sequential coupling, using crack width, carbonation passivation time, and steel bar corrosion rate as interaction parameters. Using this method, the deterioration morphology and stiffness degradation laws of 8 m reinforced concrete beams under different load conditions, including those of heavy and light trains in heavy-haul railways, are compared and assessed. The analysis reveals that within a 100-year service cycle, the maximum relative stiffness reduction for beams on the heavy train line is 20.0%, whereas for the light train line, it is only 7.4%. The degree of structural stiffness degradation is closely related to operational load levels, and beam cracking plays a critical role in this difference. Full article

The utilization of recycled concrete aggregate presents an effective solution for construction waste mitigation. However, concrete service in sulfate environments leads to sulfate ion retention in recycled aggregates, substantially impairing their quality and requiring modification approaches. A critical question remains whether traditional recycled aggregate modification techniques can effectively enhance the performance of these sulfate-containing recycled aggregates (SRA). Cement paste wrapping in various proportions was used in this investigation to enhance SRA. The performance of both SRA and modified aggregates was systematically assessed through measurements of apparent density, water absorption, crushing value, and microhardness. Microstructural analysis of the cement wrapping modification mechanism was conducted by scanning electron microscopy coupled with mercury intrusion porosimetry. Results revealed that internal sulfate addition decreased the crushing value and increased the water absorption of recycled aggregates, primarily due to micro-cracks formed by expansion. Additionally, the pores were occupied by erosion products, leading to a slight increase in the apparent density of aggregates. The performance of SRA was effectively enhanced by cement paste wrapping at a 0.6 water–binder ratio, whereas it was negatively impacted by a ratio of 1.0. The modifying effect became even more effective when 15% fly ash was added to the wrapping paste. Scanning electron microscopy observations revealed that the interface of SRA was predominantly composed of gypsum crystals. Cement paste wrapping greatly enhanced the original interface structure, despite a new dense interface formed in the modified aggregates. Full article

The superb dynamic performance of steel-plate composite (SC) structures under unexpected impact loading depends on the good design of the connection between the SC wall and foundation. This study investigated the flexural behavior and dynamic responses of SC wall-to-foundation connections subjected to low-velocity impact. Impact tests were performed on three SC connection specimens to evaluate failure mode, impact force, deflection, and strain responses. The effects of concrete strength grade and impact energy were analyzed in detail. All specimens exhibited flexural failure, with three distinct stages observed during impact. The experimental results demonstrated that compared to the specimen with C30 concrete, the specimen with C50 concrete significantly reduced wall damage, decreased deflections, and enhanced deflection recovery ability. It can be concluded that increasing the concrete strength grade effectively improves the impact resistance of SC wall-to-foundation connections. In addition, peak impact force, global deflection response, residual strains, and interface crack length were highly sensitive to changes in impact energy, whereas deflection recovery exhibited lower sensitivity. Furthermore, a finite element model was developed and validated against experimental results. Parametric studies explored the influence of key parameters with expanded ranges on the impact responses of SC wall-to-foundation connections. Full article

This study investigates the impact of partially replacing cement with fly ash (FA) on the mechanical performance of fiber-reinforced rubberized concrete (FRRC) incorporating waste tyre rubber and recycled macro-synthetic fibers (MSF). FRRC mixtures were prepared with varying fly ash replacement levels (0%, 25%, and 50%), rubber aggregate contents (0%, 10%, and 20% by volume of fine aggregate), and macro-synthetic fiber dosages (0% to 1% by total volume). The fresh properties were evaluated through slump tests, while hardened properties including compressive strength, splitting tensile strength, and flexural strength were systematically assessed. Results demonstrated that fly ash substitution up to 25% improved the interfacial bonding between rubber particles, fibers, and the cementitious matrix, leading to enhanced tensile and flexural performance without significantly compromising compressive strength. However, at 50% replacement, strength reductions were more pronounced due to slower pozzolanic reactions and reduced cement content. The inclusion of MSF effectively mitigated strength loss induced by rubber aggregates, improving post-cracking behavior and toughness. Overall, an optimal balance was achieved at 25% fly ash replacement combined with 10% rubber and 0.5% fiber content, producing a more sustainable composite with favorable mechanical properties while reducing carbon and ecological footprints. These findings highlight the potential of integrating industrial by-products and waste materials to develop eco-friendly, high-performance FRRC for structural applications, supporting circular economy principles and reducing the carbon footprint of concrete infrastructure. Full article

Polypropylene (EPP) concrete offers advantages such as low density and good thermal insulation properties, but its relatively low strength limits its engineering applications. Waste steel fibers (WSFs) obtained during the sorting and processing of machining residues can be incorporated into EPP concrete (EC) to enhance its strength and toughness. Using the volume fractions of EPP and WSF as variables, specimens of EPP concrete (EC) and waste steel fiber-reinforced EPP concrete (WSFREC) were prepared and subjected to cube compressive strength tests, splitting tensile strength tests, and four-point flexural strength tests. The results indicate that EPP particles significantly improve the toughness of concrete but inevitably lead to a considerable reduction in strength. The incorporation of WSF substantially enhanced the splitting tensile strength and flexural strength of EC, with increases of at least 37.7% and 34.5%, respectively, while the improvement in cube compressive strength was relatively lower at only 23.6%. Scanning electron microscopy (SEM) observations of the interfacial transition zone (ITZ) and WSF surface morphology in WSFREC revealed that the addition of EPP particles introduces more defects in the concrete matrix. However, the inclusion of WSF promotes the formation of abundant hydration products on the fiber surface, mitigating matrix defects, improving the bond between WSF and the concrete matrix, effectively inhibiting crack propagation, and enhancing both the strength and toughness of the concrete. Full article