Search Results (287)

Tailings are solid waste generated during mining and mineral processing. Their tremendous accumulation not only encroaches on arable land but also pollutes the environment. Currently, tailings are considered a viable alternative to natural fine aggregates in concrete because of their suitable physicochemical properties. However, existing studies remain highly fragmented and often report inconsistent conclusions owing to the considerable variability in tailings mineralogy, particle morphology, and physicochemical characteristics. To date, a comprehensive synthesis linking these intrinsic properties to the fresh, mechanical, durable, microstructural, environmental, and economic performance of tailings concrete remains lacking. Therefore, this review provides a systematic and critical assessment of tailings as aggregate in concrete and proposes an integrated framework connecting tailings characteristics, microstructural evolution, engineering performance, and sustainability outcomes. It systematically examines the physico-mechanical properties, durability, microstructure, hydration characteristics, environmental impact, and economic benefits of the resulting tailings concrete. The results showed that although tailings varied considerably in particle size, chemical composition, and mineralogy, they typically exhibited a rough surface texture and high water absorption. Furthermore, partial substitution of fine aggregates with tailings was found to improve the physical–mechanical properties and durability. However, to prevent performance decline, the substitution ratio should not exceed 50%. These benefits originated primarily from the filling effect and optimized particle packing, which increased matrix density. Microstructural analyses indicated that moderate tailings contents refined the pore structure, strengthened the interfacial transition zone (ITZ), and promoted hydration. In contrast, excessive substitution ratios weakened bonding and increased porosity. From an environmental perspective, the use of tailings generally reduced carbon emissions (by up to ~28%) and production costs (by up to ~50%) by lowering natural resource consumption and enabling large-scale waste valorization. Overall, tailings represent a sustainable aggregate alternative, provided that substitution levels are carefully controlled to balance workability, performance, and durability. Full article

Poly(9,9-di-n-octylfluorene) (PFO) applications are limited by green emission defects and imbalanced charge transport. To overcome this, novel branched polystyrene-graft-polyfluorene (PSt-g-PFO) copolymers with varying grafting densities were synthesized. The highly branched architecture induces intense steric hindrance, acting as a physical shield to isolate PFO emissive cores. This successfully suppresses detrimental interchain π–π stacking, mitigating the ~530 nm green emission. Furthermore, the moderately grafted PSt-g-PFO2 promotes locally ordered crystalline packing, achieving a maximum electron mobility of 6.16 × 10⁻⁶ cm²/(V·s), an order of magnitude higher than linear PFO. This structural design effectively decouples deleterious aggregation from charge transport. Full article

The transition towards a circular economy in architecture requires new methods for reusing construction and demolition waste as a material resource. Recycled aggregates are a promising alternative to natural aggregates, although their variable porosity and particle grading often limit practical application. This study evaluates the suitability of recycled concrete aggregate (RCA) and recycled ceramic aggregate for small-scale architectural elements such as street furniture. Three comparative mixtures were analysed using particle size distribution data, the Modified Andreasen model, and the EMMA (Elkem Materials Mix Analyzer) tool. Two mixtures contained recycled aggregates, while one reference mixture was based on natural aggregates. The assessment focused on particle packing, water demand, and binder content. The recycled concrete aggregate mixture showed results closest to the reference mix, with water content of 180 kg/m³ and a water-to-cement ratio of 0.50, compared with 170 kg/m³ and 0.50 for the natural aggregate mixture. The ceramic aggregate mixture required the highest water content (200 kg/m³) and cement dosage (380 kg/m³) due to its higher porosity (15–18%) and finer particle fraction. By adjusting aggregate proportions within the packing model, satisfactory particle structuring was still achieved in all mixtures (q = 0.31–0.35). The study shows that particle packing methods, commonly used in concrete technology, can also support early-stage architectural material selection. Recycled aggregates, particularly RCA, may therefore be considered a viable substitute for natural materials in benches, seating panels, and other small-scale circular design applications. Full article

High-performance and ultra-high-performance fiber-reinforced concretes (HPFRC/UHPFRC) have emerged as advanced cementitious composites capable of achieving superior mechanical performance, durability, and structural efficiency compared with conventional concrete. However, their widespread adoption remains challenged by relatively high material costs and significant embodied environmental impacts associated with elevated binder and fiber contents. This study presents a comprehensive life-cycle review of advanced high-performance cementitious composites, evaluating their sustainability from raw material extraction and mix design to structural application, service life, and end-of-life considerations. The review synthesizes current knowledge on material composition, production processes, structural performance, durability characteristics, and environmental impacts through the framework of life-cycle assessment (LCA). Particular attention is given to the influence of mix-design parameters, including binder composition, supplementary cementitious materials (SCMs), aggregate systems, and fiber type, on embodied carbon, energy demand, and mechanical performance. A dataset compiled from published experimental studies covering high-performance and ultra-high-performance concrete mixtures is analyzed to examine relationships between compressive strength, embodied energy, and carbon footprint, highlighting the dominant role of cementitious binders and fiber production in environmental impacts. Although advanced fiber-reinforced concretes generally exhibit higher cradle-to-gate emissions than conventional concrete, their superior mechanical properties, improved durability, reduced material demand, and extended service life can substantially reduce life-cycle environmental impacts at the structural level. The review further discusses emerging strategies for developing low-carbon high-performance cementitious composites, including clinker reduction, recycled and alternative fibers, optimized particle packing, and AI-assisted mix design. Finally, key research gaps are identified, particularly regarding standardized LCA methodologies, long-term durability data, harmonized performance-based functional units, and circular-economy strategies for material recycling and reuse. The findings highlight that performance-based life-cycle evaluation is essential for accurately assessing the sustainability potential of advanced high-performance cementitious composites in resilient and low-carbon infrastructure systems. Full article

Agricultural waste materials can serve as functional constituents in cement-based composites through three pathways: (i) organic bio-aggregates that lower density and alter thermal behavior, (ii) lignocellulosic fibers that control cracking and improve post-cracking resistance, and (iii) agro-ash supplementary cementitious materials (SCMs) that densify pore structure and reduce permeability when ash quality and curing are controlled. This review draws on 98 papers, with coconut shell aggregate and coir/coconut fibers as the core focus; agro-ash SCMs (notably palm oil fuel ash, POFA, and rice husk ash, RHA) enter where they clarify mechanisms or inform hybrid design. Rather than cataloging compressive-strength data, the synthesis is organized around controllable process inputs (feedstock conditioning, mix design, curing) and the interface-governed mechanisms that determine performance: interfacial transition zone (ITZ) character and pore connectivity. In coconut shell systems, density reductions come at a cost: elastic modulus drops and moisture sensitivity rises unless shell conditioning, particle packing, and matrix refinement are managed. In fiber systems, gains in toughness and residual capacity are bounded by mixing workability and by the long-term stability of the fiber–matrix bond under alkaline and wet–dry exposure. A mix must first meet strength, serviceability, and transport requirements before its embodied impact is compared with conventional alternatives. The contribution is to reframe these systems around controllable processing and interface mechanisms instead of tabulated strength values; preparation, treatment, and characterization data are consolidated into bounded design windows, an explicit core versus supporting evidence convention is applied, and sustainability is judged under functional equivalency rather than per-volume carbon. Full article

Lightweight aggregates have a porous structure and high water absorption, which may lead to underestimation of the powder content in conventional mix design methods for lightweight ultra-high-performance concrete (LUHPC). To address this issue, this study used ceramsite sand as the lightweight aggregate and combined the excess paste theory with the particle packing method to design and evaluate ceramsite-sand-based LUHPC mixtures based on the modified Andreasen packing model (APM) and the compressible packing model (CPM). By optimizing the particle size distribution of ceramsite sand and the binder composition, a mix design method suitable for ceramsite-sand-based LUHPC was developed. The workability, apparent density, mechanical properties, elastic modulus, and shrinkage behavior of the material with different steel fiber contents were systematically investigated. The results showed that the total binder content, water-to-binder ratio, and paste volume of the mixtures designed using the two models differed only slightly. However, the aggregate skeleton formed by CPM was denser, and its skeleton packing volume was approximately 3.5% lower than that obtained using APM. At the same steel fiber content, the main mechanical properties of the CPM-designed LUHPC were generally superior to those of the APM-designed mixtures. Specifically, the 28-day cube compressive strength increased by 5.0–7.6%, the axial compressive strength by 8.8–12.2%, the axial tensile strength by 6.4–25.8%, the flexural strength by 14.1–17.2%, and the shear strength by 3.1–6.5%. The elastic modulus was also slightly higher, while the shrinkage remained consistently lower. The CPM-2.0 LUHPC mixture achieved a 28-day cube compressive strength of 124.6 MPa and an apparent density of approximately 1982 kg/m³, realizing a compressive strength above 120 MPa at a density below 2000 kg/m³. The 28-day cube compressive strength of the CPM-3.0 mixture further increased to 131.7 MPa. As the steel fiber content increased from 1.5% to 3.0%, the workability of LUHPC decreased, whereas its compressive, tensile, flexural, and shear properties generally improved, and the elastic modulus increased slightly. Steel fibers effectively restrained shrinkage deformation, but the improvement showed diminishing marginal benefits with increasing fiber content. Considering the mechanical performance, shrinkage control, and material economy, a steel fiber content of approximately 2.0% is recommended as a reference range for ceramsite-sand-based LUHPC. Overall, CPM is more suitable than APM for the mix design of ceramsite-sand-based LUHPC and can provide guidance for mix optimization and performance regulation of lightweight ultra-high-performance concrete. Full article

Object detection in optical remote sensing imagery faces persistent challenges from severe instance overlap, extreme spatial density, and motion or atmospheric blur. These degradations cause conventional detectors to over-mix neighboring instance features and fail to separate closely packed objects. To address these limitations, we propose SARC-DETR, a detection framework that augments the RT-DETR architecture with two complementary plug-in modules: Similarity Adaptive Convolution (SAC) and Receptive Field Cross Convolution (RCC). SAC introduces a reproducing-kernel-Hilbert-space (RKHS) motivated similarity gate that selectively suppresses responses inconsistent with local feature prototypes, thereby reducing cross-instance interference in overlapped and blurred regions. RCC constructs a large directional receptive field through orthogonal strip-based aggregation and content-adaptive fusion, enabling efficient long-range context capture without quadratic complexity overhead. Both modules can be integrated into existing DETR-style detectors without modifying the detection head or training protocol. On VisDrone2019-DET, SARC-DETR improves ${\mathbf{AP}}^{\mathbf{val}}$ from 29.7 to 34.8, ${\mathbf{AP}}_{50}^{\mathbf{val}}$ from 49.5 to 56.2, and ${\mathbf{AP}}_S^{\mathbf{val}}$ from 19.2 to 24.8. On DIOR, AP rises from 57.9 to 68.4, and on NWPU VHR-10, from 44.4 to 66.5, demonstrating robust cross-dataset generalization. After structural reparameterization, the additional overhead is less than 0.75 M parameters and 0.36 G FLOPs, confirming deployment suitability for UAV and satellite-based remote sensing applications. Full article

The sustainable application of ultra-high-performance concrete (UHPC) is often constrained by high material costs and environmental footprints. While the individual effects of various industrial wastes have been extensively studied, the synergistic mechanism of multi-source waste in UHPC remains poorly understood. To fill the research gap, an eco-UHPC was developed wherein river sand (RS) was partially replaced by spontaneous combustion gangue sand (SCGS), and Portland cement (PC) was partially replaced by spontaneous combustion gangue (SCG) powder and phosphorous slag (PS). A systematic investigation was conducted to assess the packing density, flowability, mechanical properties, chloride ion penetration resistance, and micromorphology. The results indicate that 40% SCGS substitution (by mass) optimizes particle packing density and aggregate gradation, while PS incorporation significantly improves flowability by up to 16.83%. Notably, persistent pozzolanic reactions and the consumption of Ca(OH)₂ facilitate the generation of dense C-S-H gel, which creates a uniform microstructure and enhances late-stage compressive strength. Furthermore, superior chloride penetration resistance is achieved when the PS content is maintained below 20%. These findings support the synergistic utilization of SCGS, SCG, and PS in UHPC production, while facilitating broader application of UHPC through reduced costs and lower carbon emissions. Full article

High-strength concrete (HSC) is widely used in structural applications because of its superior mechanical properties and durability. However, its extensive use is often associated with high cement consumption and a greater environmental burden. This study evaluates the influence of coarse aggregate size distribution on the performance of sustainable high-strength concrete (HSC) incorporating a 40% ternary SCM replacement (metakaolin, GGBS, and silica fume). To achieve a target compressive strength of 100 MPa, three mixtures were tested with varying aggregate gradings: 5–10 mm (M1), 10–20 mm (M2), and 5–20 mm (M3). Results at 28 days showed that M3 (continuous grading) achieved the highest compressive strength (114 MPa), followed by M2 (111 MPa) and M1 (104 MPa). While all mixes exceeded the 100 MPa threshold, workability was highly sensitive to aggregate size; M1 exhibited the lowest slump (20 mm), whereas M3 achieved 90 mm, indicating that continuous grading significantly improves particle packing and fluid behaviour in high-binder systems. These findings demonstrate that a 40% reduction in Portland cement is viable for 100 MPa applications when aggregate skeletons are selected to minimize void ratios, though the low workability of smaller fractions (M1) requires increased dosage of high-range water reducers. Full article

Reducing cement consumption in mortar systems is essential for lowering the environmental impact of cement-based materials. Conventional mix design approaches rely mainly on particle size distribution and fineness modulus, which do not fully capture the effects of aggregate packing, morphology, and petrographic composition on paste demand and mechanical performance. Fourteen fine aggregates of distinct geological origins were experimentally characterized in terms of physical and petrographic properties. A dataset of 211 mortar mixtures, yielding 633 transverse-strength observations, was used to train a Random Forest Regressor (RFR) model for strength prediction. The model achieved $R^2=0.762$ (RMSE = 0.223 kN; MAE = 0.165 kN), demonstrating its reliability as a surrogate screening tool. This study presents a hybrid framework that integrates particle packing theory with machine learning to optimize fine aggregate blends. By introducing a Paste Demand Index (PDI)—combining normalized uncompacted void content, surface texture, and shape—the framework enables the identification of mixtures that minimize paste demand while maintaining mechanical performance under strength constraints. Results confirm that the proposed PDI and strength-based filtering are robust, offering a physically grounded decision-support methodology for narrowing the design space. Ultimately, this approach provides an efficient strategy for resource optimization, effectively bridging the gap between computational screening and laboratory validation in cement-reduction initiatives driven by the cement-based tile manufacturing industry. Full article

Copper slag (CS) was considered a major by-product produced from the copper refining industry, which estimates about 2.2 to 3 tons generated during the production of every one ton of copper. At the same time, continuous dumping and improper disposal of this byproduct have led to serious environmental problems, especially due to the leaching of heavy metals into soil and water. This review carefully studies the potential of CS as a sustainable construction material through a clear distinction of its performance, especially when used as a fine aggregate and as a supplementary cementitious material (SCM). Due to the presence of higher content of iron and silica, higher hardness, and very low water absorption, it was found that CS helps in improving the density and durability of concrete. When used as a fine aggregate, CS enhances workability, strength, and durability at an optimum level of about 40%, mainly due to better particle packing and reduced pore connectivity. On the other hand, when used as an SCM, CS contributes to long-term strength through pozzolanic reactions and the formation of C–S–H gel, but its replacement level should be limited to about 20% to avoid loss of early-age strength caused by reduced alkalinity. In terms of durability, the use of CS can reduce water absorption by up to 60%, lower chloride penetration, and improve resistance to sulfate attack. Environmental Life Cycle Assessment studies show that CS can reduce global warming potential by about 12–19% and also decrease overall energy consumption. Statistical validation using multi-criteria decision analysis (MCDA) and separate regression modeling with an R² value of about 0.965, which supports these optimum replacement levels up to 40% for fine aggregate and 20% for cement, providing a good balance between strength, durability, environmental benefits, and cost. Overall, this review shows that CS is a valuable and multi-functional material that supports circular economy practices when used with a proper mix design based on specific applications. Full article

Image demosaicking aims to reconstruct a full-resolution color image from spatially sparse and interleaved color filter array observations. Despite the significant progress achieved by deep learning-based methods, existing approaches have not fully addressed the sampling-structure-constrained nature of demosaicking. In particular, four-channel half-resolution packing may disrupt the CFA spatial phase relationships, while local convolutions and global non-local matching struggle to model reconstruction-relevant cross-position dependencies. To address these issues, this paper proposes an end-to-end image demosaicking network with region-level non-local modeling and residual aggregation (RNRA-Net). Instead of packing Bayer RAW data into a four-channel half-resolution representation, RNRA-Net decomposes the original mosaic image into a three-channel representation at the original resolution, thereby preserving the spatial arrangement of CFA sampling. To capture structurally related information, a region-level non-local module is introduced to compute feature correlations within spatially bounded regions, enabling aggregation of reconstruction-relevant contextual information. In addition, a residual aggregation module is developed to explicitly collect and refine early residual compensation features, facilitating the recovery of edges, textures, and high-frequency details. Extensive experiments on benchmark and high-resolution datasets demonstrate the effectiveness of RNRA-Net. Full article

The development of tight heavy-oil reservoirs is severely hampered by the high viscosity and poor mobility of crude oil caused by strong intermolecular stacking interactions among asphaltenes, coupled with the substantial adsorption loss and inadequate deep transport capacity of conventional displacement agents. By targeted penetrant delivery, a novel nanoemulsion system with a well-defined “core–shell” architecture was synthesized to address these critical challenges. The physicochemical properties, stability and oil displacement performance were evaluated. The prepared nanoemulsion exhibited an ultrasmall and uniform particle size distribution between 10 nm and 20 nm. It also demonstrated exceptional dispersibility in aqueous media and remarkable thermal and salinity stability under reservoir conditions. Furthermore, an ultralow critical micelle concentration of approximately 0.01% could be achieved and the oil–water interfacial tension was reduced to 7.3 × 10⁻² mN/m, significantly outperforming the conventional surfactant AES. Core flooding tests revealed that the proposed nanoemulsion enhanced oil recovery by 37.1% and attained a displacement efficiency of 68.9% in oil-wet capillary models. Molecular dynamics simulations further elucidated the underlying synergistic mechanism. The hydrophilic shell minimized adsorption on rock surfaces, facilitating deep migration within nanoporous channels. The hydrophobic core, containing terpinene as a penetrant, effectively disrupted the π-π stacking of asphaltenes due to its nonplanar molecular configuration. This disruption transformed the asphaltene aggregates from a tightly packed state to a dispersed state, resulting in substantial viscosity reduction. This work elucidated the mechanism of asphaltene aggregate disruption by nanoemulsions at the molecular level, offering a promising and theoretically grounded strategy for the efficient exploitation of tight heavy-oil reservoirs. Full article

The global construction sector, a major consumer of virgin raw materials, is under increasing pressure to transition from a linear to a circular economy model. Marble waste, generated in large quantities during quarrying, cutting, and polishing operations, represents a promising secondary resource for sustainable construction applications. This systematic review was conducted in accordance with the PRISMA 2020 reporting guidelines to critically evaluate the utilization of marble waste in concrete and other building materials. A comprehensive literature search was performed using major scientific databases, and relevant studies published between 2000 and 2025 were analyzed. The findings consistently indicate that marble waste performs most effectively as a fine aggregate replacement at 10–20%, resulting in improved compressive strength, pore refinement, and durability. As a cement substitute, the optimum replacement level is generally 5–10%, beyond which dilution effects may adversely affect strength development. The performance is primarily attributed to improved particle packing and microstructural refinement. This review further highlights future pathways for industrial-scale implementation, mix optimization, standardisation, and policy integration to accelerate circular construction practices. These findings support the potential of marble waste as a sustainable material in advancing circular economy principles in the construction industry. Full article

High-resolution remote sensing image (HRRSI) scene classification faces challenges such as significant target scale variations, complex background interference, and the difficult spatial parsing of dense objects (such as tightly packed buildings in dense residential areas or scattered aircraft on aprons), while existing models struggle to balance computational efficiency and classification accuracy. To address these issues, this paper proposes a lightweight Multi-Scale Selective Dilated Attention Residual Network (MS-DARNet). The model utilizes a Multi-branch Dilated Feature Extraction (MDFE) module, employing parallel convolutional branches with varying dilation rates to dynamically expand the receptive field and collaboratively extract multi-scale features without increasing parameter counts. Furthermore, a Context-Position Aware Attention (CPAA) module is introduced, combining a large kernel decomposition strategy to suppress irrelevant background noise with direction-aware feature aggregation to retain precise spatial coordinates for dense objects. Extensive experiments on the AID, NWPU-RESISC45, and RSD-WHU46 datasets show that MS-DARNet achieves superior classification accuracies of 97.78%, 94.53%, and 94.55%, respectively. Concurrently, it maintains a significantly low complexity of just 2.50 M parameters and 0.5940 GMACs. These findings demonstrate that MS-DARNet effectively achieves an optimal balance between lightweight architecture and exceptional classification performance for complex remote sensing scenes. Full article