Search Results (535)

Water-based elastomeric waterproofing membranes based on styrene–acrylic (S/A) copolymers have emerged as an important class of materials for modern construction due to their combination of flexibility, adhesion, environmental compatibility, and long-term durability. These membranes form seamless protective layers capable of accommodating substrate movement while preventing water ingress across a wide range of building structures. Recent advances in polymer chemistry and emulsion technology have significantly improved the performance of S/A systems, particularly in terms of crack-bridging capability, weather resistance, and UV stability. In addition, optimized formulations incorporating functional fillers, rheology modifiers, and hybrid polymer architectures enable improved mechanical performance and impermeability. This review provides a comprehensive overview of S/A elastomeric waterproofing membranes, covering polymer chemistry, formulation strategies, physico-mechanical properties, durability mechanisms, and real-world construction applications. The review also compares S/A systems with alternative waterproofing technologies such as polyurethane (PU), cementitious coatings, and bituminous membranes. Finally, emerging developments in advanced polymer architectures, nano-reinforced coatings, and sustainable formulations are discussed, highlighting future research directions for high-performance waterproofing systems. Full article

Real-time monitoring of surface cracks in reinforced concrete (RC) beams is critical to structural safety and service performance evaluation. Current structural crack monitoring still faces prominent scientific and technical bottlenecks: conventional unidirectional sensors cannot achieve multi-directional collaborative sensing, rigid piezoelectric materials exhibit poor compatibility with the large deformation of concrete, and there is a lack of quantitative mapping relationships from sensing signals to crack parameters, making it difficult to simultaneously measure crack width, angle, and morphology. This paper presents a novel ring-shaped piezoelectric sensor based on polyvinylidene fluoride (PVDF) and an annular piezoelectric sensing mechanism for real-time monitoring of crack angle, width, and morphology. The sensor incorporates a laminated structure with four strip sensing units for multi-directional strain detection. Experiments were conducted on RC beams under various loading conditions, and finite element analysis was performed using COMSOL Multiphysics. An innovative crack damage index (B) was introduced to assess structural damage quantitatively. Results demonstrate high sensor sensitivity and stable output. Voltage signals increase both with crack width and crack angle, showing responses of 0.045 mV, 0.041 mV, and 0.023 mV for crack angles of 60°, 45°, and 30°, respectively, at a crack width of 9 mm. Strong consistency between experimental and simulation data validates the effectiveness of the mechanism in monitoring the direction, width, and types of cracks. The crack damage index B exhibits a positive correlation with the structural stress response, enabling a quantitative assessment of damage. This study is applicable to the prestressed concrete box girders and T-beams commonly used in large-span bridges, which are typically with a main span of 20–50 m, a beam length of 6–30 m, a section height of 1.2–2.5 m, and designed for Grade C35–C50 concrete. The findings provide a practical foundation for real-time crack monitoring in large-scale bridge beam members. Full article

High-purity copper features excellent electrical conductivity but generally low mechanical properties. Adding a three-dimensional graphene network as reinforcement to make a copper–graphene metal matrix composite is promising for a wide range of applications with better mechanical performance and functional capabilities. However, direct application in a metal matrix is difficult due to unfavorable wetting, which causes poor dispersion and weak interfacial bonding in the graphene–metal system. Here, the powder metallurgy method was used to construct a three-dimensional continuous graphene network in the copper matrix combined with high-pressure torsion. Optimized deformation/thermomechanical treatment enhanced the microstructural development processed by the severe plastic deformation method of high-pressure torsion. The primary advantage of this hybrid process is that it enables us to achieve grains with a size in the ultra-fine or even nanoscale. A homogeneous equiaxed nanostructure without segregation was observed during microstructural characterization, with a grain size of ~300 nm. This study investigated structural development during progressive deformation, and the samples were evaluated from the viewpoint of grain size and grain boundaries. The process significantly increased the microhardness of the copper–graphene composite. The tensile strength reached ~500 MPa at room temperature. The interpenetrating structural feature of graphene promoted interfacial shear stress to a high level, whereas plastic deformation increased the dislocation density and grain boundaries, thus resulting in significantly enhanced load transfer strengthening and crack-bridging toughness simultaneously. Full article

Unsaturated polyester (UP) composites are widely utilized in engineering applications, including vehicle body structures, due to their ease of processing and good interfacial compatibility with natural fibers. However, the inherent brittleness of UP limits its performance under impact or tensile loading, as it exhibits minimal plastic deformation and is prone to crack initiation and propagation. In this study, bamboo fiber was incorporated into the UP matrix at various mixing ratios to enhance its crack resistance. After achieving uniform dispersion, the composites were subjected to a splitting tensile test to evaluate their crack resistance behavior. The results indicate that the composite containing 80% polyester exhibits the highest fracture toughness, with a crack resistance value of K_1C = 1.396 MPa·m^0.5. This value represents a 192.03% improvement compared with neat polyester (K_1C = 0.713 MPa·m^0.5). The enhanced crack resistance is attributed to the fiber bridging and energy-absorption mechanisms introduced by the bamboo fibers. These findings demonstrate the effectiveness of bamboo fiber reinforcement in improving the fracture performance of UP-based composites, highlighting their potential for use in lightweight structural applications. Full article

Accurate service performance evaluation of reinforced concrete (RC) beam structures is crucial for ensuring structural safety and guiding maintenance decisions. However, current practice primarily relies on qualitative visual inspections that fail to quantitatively link apparent defects to internal mechanical behavior. To address this, a novel evaluation framework fusing apparent crack features with static and dynamic responses is proposed. A context-aware grid-based deep learning model (CGDL-Crack) is developed that combines transfer learning with skeleton extraction, achieving crack localization with a maximum validation AP of 96.4% under complex backgrounds. Based on large-scale parametric finite element simulations and Sobol global sensitivity analysis, key state indicators—including static reaction forces, modal frequencies, and crack widths—are identified, and an artificial neural network (ANN) surrogate model is constructed to map multi-source monitoring data to material constitutive parameters. Full-process failure tests on 17 RC beams demonstrate that crack width follows bilinear growth and remains sensitive after stiffness indices saturate. The updated FE model accurately predicts ultimate bearing capacity, demonstrating the effectiveness of the proposed framework and its application potential for RC beam-type components in bridge and building engineering. Full article

To enhance crack detection accuracy during UAV-based inspections and address key challenges such as false positives from complex backgrounds, missed narrow cracks, and insufficient structural continuity modeling, this study proposes CFM-Net, a task-oriented segmentation network integrating Channel-Spatial Attention and Multi-Scale Structural Enhancement. Constructed on an optimized U-Net backbone, it employs three dedicated modules: the Channel and Spatial Attention Module (CBAM) to amplify crack-related features and suppress background interference; the Gated Fusion Module (GFF) to dynamically fuse multi-level features, improving detection of fine, narrow cracks; and the Morphology-Guided Multi-Scale Structural Perception Module (MGMSIB), designed to model the structural continuity and multi-scale characteristics of cracks. Comprehensive evaluations on the Mix Bridge Crack dataset demonstrate CFM-Net achieves competitive performance among the evaluated methods, with an mIoU of 80.05% and an F1-score of 87.06%. This represents a significant improvement over strong baselines, outperforming DeepCrack and CrackFormer by 2.3% and 2.42% in mIoU, and 1.21% and 1.03% in F1-score, respectively. Furthermore, the model demonstrates robust performance on heterogeneous crack datasets composed of multiple public sources, particularly in reducing false alarms, recovering narrow cracks, and maintaining crack topology. These results conclusively validate the effectiveness and practical utility of the proposed method for automated bridge crack inspection. Full article

Vision-based structural defect detection methods based on YOLOv11 have achieved promising performance in recent years; however, their robustness in real engineering environments remains limited due to illumination variation, shadow occlusion, surface contamination, and complex background textures. Existing data-driven approaches primarily rely on visual appearance features while neglecting the intrinsic geometric continuity and morphological characteristics associated with structural failures such as cracks and spalling. To address these challenges, this study proposes an enhanced defect detection framework termed GCA-YOLO for intelligent structural inspection. The proposed method integrates a Geometric Constraint Attention (GCA) module and a Residual Efficient Channel Attention (RECA) module to improve feature representation. Instead of explicit physical simulation, the GCA module embeds morphology-guided geometric priors into the attention mechanism using differentiable gradient and Laplacian operators. This enforces structural continuity perception and suppresses geometrically inconsistent responses caused by background noise. Furthermore, a geometry confidence gating mechanism adaptively modulates the contribution of morphological features, while the RECA module recalibrates channel-wise responses to enhance the representation of weak and low-contrast defects. To comprehensively evaluate the proposed method, experiments were conducted on three representative datasets, including a public crack dataset and two self-built datasets (one for peeling/detachment and one for crack defects). These datasets were collected from diverse civil infrastructure scenarios such as bridges, tunnels, and pavements under challenging conditions including low illumination, shadow occlusion, complex textures, and heterogeneous backgrounds. Compared with the baseline YOLOv11 model, the proposed GCA-YOLO framework improves mAP@0.5 by 2.2%, 2.5%, and 15.9% on the public crack dataset, the self-built peeling/detaching dataset, and the self-built crack dataset, respectively. Meanwhile, Recall is improved by 4.6%, 3.8%, and 33.1%, respectively, demonstrating the effectiveness of the proposed dual-attention framework in enhancing the completeness of defect localization and reducing missed detections. Despite these performance gains, the proposed framework maintains a lightweight architecture and does not introduce significant computational overhead. Experimental results demonstrate that the proposed framework achieves strong robustness, stable generalization capability, and favorable detection efficiency across different defect categories and engineering scenarios, demonstrating promising potential for intelligent infrastructure inspection, urban safety monitoring, and practical engineering deployment. Full article

Cracking in reinforced concrete beam bridges severely compromises their durability and structural integrity. Although external prestressed CFRP plate reinforcement technology has emerged as an effective repair solution, current design codes primarily rely on idealized crack-free or simplified single-crack assumptions, leading to inadequate precision in prestressing application for real-world structures with complex crack networks. This study investigated the reinforcement effectiveness of externally prestressed CFRP plates on three pre-cracked reinforced concrete T-beams with varying reinforcement ratios (1.20%, 2.41%, and 3.61%). A comprehensive experimental program was conducted to monitor crack closure behavior, strain distributions, and deflection changes during tensioning and loading phases. A three-dimensional finite element model was developed using Midas FEA NX 2022, and theoretical formulas for crack closure prestressing were derived under the plane-section assumption, supplemented by engineering correction factors. Results demonstrated that calculation errors for both crack closure prestressing and secondary cracking loads were below 5%, while correlation coefficients between finite element simulations and experimental data ranged from 0.93 to 0.99. External prestressing significantly enhanced the stiffness of cracked beams, with stiffness recovery rates reaching up to 156.2%, and exhibited excellent synergistic performance among CFRP plates, steel reinforcement, and concrete. These findings provide a theoretical foundation and technical support for the precision design of external prestressing reinforcement in cracked reinforced concrete beams. Full article

Ultra-high-performance concrete incorporating coarse aggregates (UHPC-CA) exhibits pronounced multi-scale heterogeneity and staged damage evolution. However, existing single-scale reinforcement strategies often fail to address the complete micro-to-macro fracture process, leaving a critical research gap in achieving full-stage crack control. To address this, this study introduces a novel cross-scale toughening strategy using hybrid steel fibers (SF) and calcium carbonate whiskers (CCW), and decouples the coupled influences of water-to-binder (W/B) ratio, coarse aggregate (CA), and multi-scale fibers via an orthogonal design. Mechanical properties, fiber dispersion, and pore structure are jointly characterized to establish structure–property relationships. An optimal composition (W/B = 0.32, CA = 18%, SF = 2%, CCW = 1%) is identified, achieving a balanced enhancement of strength and ductility. Results indicate that matrix densification is primarily controlled by W/B via pore refinement, while mechanical performance is governed by the interplay between fiber spatial uniformity and interfacial integrity; the roles of CA and CCW are clearly stress-state dependent. Furthermore, a novel cross-scale synergistic mechanism is revealed, in which micro-scale CCW regulates microcrack initiation and stabilizes the pre-peak response, whereas macro-scale SF dominates post-peak behavior through crack bridging and pull-out energy dissipation. This sequential activation enables a full-stage enhancement of tensile performance, shifting failure from brittle localization to pseudo-ductile multiple cracking. The findings provide a correlative framework for tailoring UHPC-CA through multi-scale hybrid reinforcement. Full article

Concrete crack segmentation in bridge health monitoring is crucial for ensuring the safety and longevity of infrastructure. However, this task is complicated by challenges such as weak contrast, background interference, and multi-scale crack structures, which hinder traditional methods’ accuracy. This study introduces a novel Boundary-Sensitive Hybrid Attention Network (BSA-Net) designed to address these issues by combining a hierarchical Transformer encoder (Hiera-A), a multi-scale context module (Light-ASPP), and a boundary-aware decoder (BAD). The hierarchical encoder effectively captures multi-scale features, while Light-ASPP enhances the network’s ability to aggregate contextual information with minimal computational cost, making it suitable for large-scale applications. The dual-branch decoder explicitly decouples the learning of semantic segmentation and boundary prediction, ensuring more accurate boundary detection and crack continuity. The extensive experiments on multiple benchmark datasets demonstrate that BSA-Net consistently outperforms existing crack detection models, particularly in complex, noisy environments. The model achieves competitive performance in terms of segmentation accuracy, boundary clarity, and recall rates, particularly for fine-scale and weak contrast cracks. The results indicate that BSA-Net not only enhances the performance of crack segmentation in real-world conditions but also provides a scalable and reliable solution for automated infrastructure monitoring and defect detection. Full article

This study investigates the mechanical behavior and durability performance of kaolin clay stabilized using a hybrid system composed of Xanthan Gum biopolymer, polypropylene fibers, and Trivoltherm waste fibers. Experimental studies were designed according to the Taguchi L16 orthogonal array to evaluate the effects of different additive combinations. Unconfined compressive strength tests were performed after curing periods of 7, 28, and 90 days, while durability behavior was assessed through 5 and 10 freeze–thaw cycles. In addition, scanning electron microscopy analyses were conducted to investigate the microstructural characteristics of the stabilized soils. The results indicated that strength increased significantly with curing time, reaching a maximum value of 1186 kPa after 90 days. Statistical analyses showed that Xanthan Gum was the dominant parameter affecting strength development, contributing approximately 57–63% to the unconfined compressive strength behavior. Fiber additives also improved ductility, crack resistance, and freeze–thaw durability through reinforcement and crack-bridging mechanisms. The best-performing mixtures exhibited markedly lower strength losses under freeze–thaw conditions compared with untreated soil specimens. Analysis of variance results confirmed that the investigated parameters were statistically significant (p < 0.05), and the developed models showed high prediction accuracy (R² > 85%). Overall, the findings demonstrate that the synergistic interaction between the biopolymer matrix and fiber reinforcement system provides an effective and sustainable hybrid stabilization approach for improving the engineering performance of clay soils. Full article

The use of bio-based and locally available materials in construction is an effective approach to reducing embodied energy and supporting circular economy principles. In the earthen construction, the cereal straw fibres have traditionally been used as a natural reinforcement. However, their tensile properties and interspecies variability remain insufficiently documented. This study investigates the tensile behaviour of the straw fibres from four cereal species, wheat, rye, oat, and barley, to evaluate their suitability for rammed earth construction. The straw samples were collected during the 2020/2021 growing season and prepared under controlled laboratory conditions. Single-fibre tensile tests were performed using a Zwick/Roell Z600 universal testing machine under displacement-controlled loading at 0.5 mm/min. Tensile strength and modulus of elasticity were derived from the load–displacement data and specimen geometry. The results indicate systematic interspecies variations in the tensile behaviour. Wheat fibres exhibited the highest average tensile strength (39.61 MPa) and stiffness, indicating a favourable crack-bridging capacity. Rye and oat fibres showed comparable tensile strengths of 33.50 MPa and 33.72 MPa, respectively, accompanied by a greater variability. Barley fibres recorded the lowest average tensile strength (25.32 MPa), suggesting a limited structural suitability. These findings confirm the mechanical potential of cereal straw fibres, particularly wheat, as natural micro-reinforcement for the rammed earth. The study supports the valorisation of the agricultural by-products in sustainable, low-carbon construction. Full article

To investigate the influence of basalt fiber (BF) on the mechanical properties and crack evolution of coal gangue–slag geopolymer concrete, geopolymer concrete specimens were prepared using coal gangue powder calcined at 700 °C and slag as precursors, with BF contents ranging from 0 to 1.25%. Mechanical testing combined with digital image correlation (DIC), scanning electron microscopy (SEM), and X-ray diffraction (XRD) was conducted to evaluate the effects of BF on macroscopic mechanical behavior, crack evolution, and underlying microstructural mechanisms. The results demonstrate that BF effectively enhances both the mechanical performance and crack-control capacity of coal gangue–slag geopolymer concrete, exhibiting a clear content-dependent trend. Compressive strength initially increases and subsequently decreases with increasing BF content. The 28-day compressive strength reaches a maximum value of 84.05 MPa at a BF content of 0.5%, representing an 11.92% improvement compared with the control group. Splitting tensile strength and flexural strength attain their peak values at a BF content of 1%, increasing by 37.88% and 25.81%, respectively. DIC analysis indicates that BF delays strain localization and effectively restrains the propagation of dominant cracks. Specifically, the compressive strain field becomes more uniformly distributed at 0.5% BF content, while crack propagation during splitting failure is more stable at 1% BF content. SEM observations reveal that the primary strengthening mechanisms include crack bridging, interfacial load transfer, and energy dissipation associated with fiber pull-out. XRD analysis shows that BF incorporation does not significantly alter the phase composition of the coal gangue–slag geopolymer system; thus, performance enhancement mainly arises from fiber bridging and interfacial reinforcement rather than changes in primary reaction products. Full article

Reliable crack detection is essential for ensuring the safety, serviceability, and long-term performance of civil structures. Conventional manual inspections are labor-intensive and subjective, while existing computer vision models often exhibit reduced accuracy under variable field conditions. This study develops a computer vision-based automated crack detection framework utilizing a hybrid Transformer–CNN architecture to support infrastructure inspection and condition assessment. The proposed model leverages the global context modeling capability of Transformers and the local feature sensitivity of convolutional neural networks (CNNs) to enhance detection robustness. The optimized hybrid model achieved an Intersection over Union (IoU) of 91.8% and an accuracy of 98.7%, outperforming baseline CNN, Transformer-only, and LSTM architectures. Field validation on bridge inspection imagery demonstrated strong resilience to variations in illumination and texture. The developed approach contributes to digital inspection and intelligent lifecycle management of infrastructure assets by enabling reliable, automated, and non-intrusive structural condition evaluation under realistic field conditions. Full article

With the strategic shift toward reducing reliance on critical raw materials, Cobalt-free eutectic high-entropy alloys (EHEAs) have emerged as a pivotal frontier for high-performance structural applications. This review systematically elucidates the synergistic relationship between Co-free alloy design and the non-equilibrium solidification mechanisms of Selective Laser Melting (SLM). The ultra-high cooling rates (10⁵–10⁸ K/s) inherent in SLM are shown to refine eutectic lamellae to the sub-micron scale (typically <300 nm), effectively suppressing the macro-segregation common in conventional casting. We evaluate the design principles of Al-Cr-Fe-Ni and related systems, noting that SLM-processed Co-free EHEAs frequently achieve yield strengths exceeding 1000 MPa and ultimate tensile strengths (UTSs) surpassing 1300 MPa, while maintaining tensile elongations above 10%—a significant improvement over the coarse-grained structures produced by traditional methods. Furthermore, the study identifies critical processing windows, such as laser energy densities (60–120 J/mm³), required to mitigate micro-cracking and achieve near-full density (>99.5%). By synthesizing recent experimental breakthroughs and AI-driven modeling, this review provides a quantitative roadmap for the precision manufacturing of cost-effective, high-performance EHEAs, bridging the gap between theoretical alloy design and industrial additive manufacturing. Full article