Search Results (2,278)

Pervious concrete is challenged by the inherent trade-off between permeability and mechanical strength. This study presents a systematic optimization of its mix design to achieve a balance between these properties. Single-factor experiments and an L₉(3³) orthogonal array test were employed to evaluate the effects of target porosity (14–26%), water–cement ratio (0.26–0.34), sand rate (0–10%), and VMA dosage (0–0.02%). Additionally, Spearman rank correlation analysis and nonlinear regression fitting were utilized to develop quantitative relationships correlating the measured porosity to material performance. The results revealed that increasing target porosity enhances permeability but reduces compressive and splitting tensile strengths. The optimal water-to-cement ratio (w/c) was found to be 0.32, balancing both permeability and strength. An appropriate sand content of 6% improved mechanical properties, while a VMA dosage of 0.01% effectively enhanced bonding strength and workability. The orthogonal experiment identified the optimal mix ratio as a w/c ratio of 0.3, VMA dosage of 0.12%, target porosity of 14%, and sand content of 7%, achieving a compressive strength at 28-days of 43.5 MPa and a permeability coefficient of 2.57 mm·s⁻¹. Empirical relationships for the permeability coefficient and mechanical properties as functions of the measured porosity were derived, demonstrating a positive exponential correlation between the measured porosity and the permeability coefficient, and a negative correlation with compressive and splitting tensile strengths. This research provides a systematic framework for designing high-performance pervious concrete with balanced permeability and mechanical properties, offering valuable insights for its development and application in green infrastructure projects. Full article

Tunnel linings play a vital role in underground infrastructure, yet their performance can be severely affected by pre-existing cracks. This study investigates the mechanical behavior and failure mechanisms of C30 concrete with artificial cracks under uniaxial compression, simulating various crack conditions observed in tunnel linings. Specimens were designed with varying crack lengths and orientations. Acoustic emission (AE) monitoring was employed to capture the evolution of internal damage and micro-cracking activity during loading. Fractal dimension analysis was performed on post-test crack patterns to quantitatively evaluate the complexity and branching characteristics of crack propagation. The AE results showed clear correlations between amplitude characteristics and macroscopic crack growth, while fractal analysis provided an effective metric for assessing the extent of damage. To complement the experiments, discrete element modeling (DEM) using PFC3D was applied to simulate crack initiation and propagation, with results compared against experimental data for validation. The study demonstrates the effectiveness of DEM in modeling cracked concrete and highlights the critical role of crack orientation and size in strength degradation. These findings provide a theoretical and numerical foundation for assessing tunnel lining defects and support the development of preventive and reinforcement strategies in tunnel engineering. Full article

Ultra-high-performance concrete (UHPC) is increasingly employed in long-span and heavily loaded structural applications; however, the accurate prediction of its flexural capacity remains a significant challenge because of the complex interactions among geometric parameters, reinforcement details, and advanced material properties. Existing design codes and single-architecture machine learning models often struggle to capture these nonlinear relationships, particularly when experimental datasets are limited in size and diversity. This study proposes a compact hybrid CNN–Transformer model that combines convolutional layers for local feature extraction with self-attention mechanisms for modeling long-range dependencies, enabling robust learning from a database of 120 UHPC beam tests drawn from 13 laboratories worldwide. The model’s predictive performance is benchmarked against conventional design codes, analytical and semi-empirical formulations, and alternative machine learning approaches including Convolutional Neural Networks (CNN), eXtreme Gradient Boosting (XGBoost), and K-Nearest Neighbors (KNN). Results show that the proposed architecture achieves the highest accuracy with an R² of 0.943, an RMSE of 41.310, and a 25% reduction in RMSE compared with the best-performing baseline, while maintaining strong generalization across varying fiber dosages, reinforcement ratios, and shear-span ratios. Model interpretation via SHapley Additive exPlanations (SHAP) analysis identifies key parameters influencing capacity, providing actionable design insights. The findings demonstrate the potential of hybrid deep-learning frameworks to improve structural performance prediction for UHPC beams and lay the groundwork for future integration into reliability-based design codes. Full article

Accurate prediction of shear strength at the interface between new and old concrete is vital for the structural performance of repaired and composite systems. However, the underlying shear transfer mechanism is highly nonlinear and influenced by multiple interdependent factors, which limit the applicability of conventional empirical models. To address this challenge, an interpretable machine-learning (ML) framework is proposed. The latest database of 247 push-off specimens was compiled from the recent literature, incorporating diverse interface types and design parameters. The hyperparameters of the adopted ML models were optimized via a grid search to ensure the predictive performance on the updated database. Among the evaluated algorithms, eXtreme Gradient Boosting (XGBoost) demonstrated the best predictive performance, with R² = 0.933, RMSE = 0.663, MAE = 0.486, and MAPE = 12.937% on the testing set, outperforming Support Vector Regression (SVR), Random Forest (RF), and adaptive boosting (AdaBoost). Compared with the best empirical model (AASHTO, R² = 0.939), XGBoost achieved significantly lower prediction errors (e.g., RMSE was reduced by 67.8%), enhanced robustness (COV = 0.176 vs. 0.384), and a more balanced mean ratio (1.054 vs. 1.514). The SHapley Additive exPlanations (SHAP) method was employed to interpret the model predictions, identifying the shear reinforcement ratio as the most influential factor, followed by interface type, interface width, and concrete strength. These results confirm the superior accuracy, generalizability, and explainability of XGBoost in modeling the shear behaviors of new–old concrete interfaces. Full article

Ultra-High-Performance Concrete (UHPC) is a game-changing, innovative material with the merits of exceptional tensile strength, making it suitable for stirrup-free UHPC beams. In this study, two 4.0 m-long large-scale stirrup-free I-shaped UHPC beams were experimentally explored in bending tests and shear tests. Cracking patterns, failure modes, and ultimate load-bearing capacity were obtained. Experimental findings revealed that the shear capacity of the stirrup-free I-shaped UHPC beams with a web thickness of merely 50.0 mm reached more than 20.0 MPa and demonstrated excellent post-cracking shear behavior. Finite element models were established and verified with experimental results to investigate the shear behaviors of stirrup-free I-shaped UHPC beams, considering the parameters of shear span-depth ratio and longitudinal reinforcement strength. The results demonstrated that as the shear span-depth ratio increases, the shear capacity of UHPC beams exhibits a declining trend, accompanied by increased mid-span deflection and a degradation in stiffness. French code and PCI report were suggested for design purposes, due to rationally conservative prediction and explicit physical indication. Full article

To improve the design theory of prestressed carbon fiber sheet reinforcement and enrich its practical application, a corresponding theoretical analysis and experimental study were carried out. According to the ductile failure condition of reinforced concrete (RC) beams and the plane cross-section assumption, the initial tensile strain control range of carbon fiber sheets with different reinforcement layers was analyzed. Based on the requirement of improving the flexural capacity of beams, a calculation method for reinforcement layers and the initial tensile strain of carbon fiber sheets was proposed. According to the requirements of the practice of prestressed carbon fiber sheet reinforcement, a design process for strengthening RC beams with prestressed carbon fiber sheets was proposed. Through the proposed design method and design process, the design and practice of prestressed carbon fiber sheet reinforcement of RC beams were carried out, and a four-point bending test was carried out on a reinforced beam. The results showed that the failure mode of RC beams after reinforcement was plastic failure, which met the designed bearing capacity requirement. Full article

Fly ash, as the main solid waste of coal-fired power plants, is an environmental problem that needs to be solved due to its massive accumulation. The mechanical properties and optimization mechanism of lightweight aggregate concrete prepared by using new single-cylinder rotary kiln fly ash ceramic granules as aggregate were systematically investigated. Through orthogonal experimental design, combined with macro-mechanical testing and microscopic characterization techniques, the effects of cement admixture and ceramic granule admixture on the properties of concrete, such as compressive strength, split tensile strength, and modulus of elasticity, were analyzed, and the optimization scheme of key parameters was proposed. The results show that the new single rotary kiln fly ash ceramic particles significantly improve the mechanical properties of concrete by optimizing the porosity (water absorption ≤ 5%), and its 28-day compressive strength reaches 46~50.9 MPa, which is 53.3~69.7% higher than that of the ordinary ceramic concrete, and the apparent density is ≤1900 kg/m³, showing lightweight and high-strength characteristics. X-ray diffraction (XRD) analysis shows that the new ceramic grains form a more uniform, dense structure through the synergistic effect of internal mullite crystals and dense glass phase; computed tomography (CT) scanning shows that the total volume rate of cracks of the new ceramic concrete was reduced by up to 63.8% compared with that of ordinary ceramic concrete. This study provides technical support for the utilization of fly ash resources, and the prepared vitrified concrete meets the demand of green building while reducing structural deadweight (20~30%), which has significant environmental and economic benefits. Full article

As an essential medium for the cultural narrative of architectural space, studying the cognitive transformation mechanisms of traditional ethnic decorative patterns is critical for their effective preservation and innovative application. This research focuses on typical decorative motifs found in She ethnic architectural heritage, systematically classifying them into five categories—animal, plant, human figure, totem, and geometric—based on symbolic themes, formal structure, and cultural function. Correspondingly, 20 sets of standardized black-and-white line drawing stimuli were developed for experimental use. Methodologically, this study utilized the EyeLink 1000 eye-tracking system to acquire real-time gaze metrics, including fixation duration and saccadic amplitude, as well as pupil dilation responses from participants engaged in a controlled pattern observation task. Immediately after observation, participants completed a semantic differential assessment using a five-point Likert scale. Data analysis employed descriptive statistics, analysis of variance (ANOVA), Kruskal–Wallis tests, and Bonferroni-adjusted post hoc comparisons (α = 0.05). Attention allocation was further examined through heatmaps and gaze trajectory visualizations to provide comprehensive insight into visual engagement. Two principal findings were identified: first, male participants showed a predominant focus on holistic structural composition and cultural symbol representation, whereas female participants exhibited a processing bias towards fine details; second, concrete symbols imbued with historical significance elicited more pronounced emotional responses, while abstract geometric patterns necessitated formal reconstruction to enhance cognitive accessibility. These findings offer empirical support for gender-inclusive architectural design strategies and inform practical approaches for safeguarding cultural heritage within contemporary architectural environments. Consequently, modern reinterpretation of traditional decorative patterns should balance cultural narrative fidelity with functional adaptation, achieving inclusive expression through contextual reconstruction and interactive design strategies. Future research directions include expanding participant demographics to encompass cross-cultural cohorts and incorporating multimodal neuroimaging techniques to elucidate the underlying cognitive and affective mechanisms, thereby advancing the sustainable transmission and innovation of ethnic cultural heritage. Full article

This study investigates the influence of time (ageing) on the uplift capacity of bored piles in cohesionless silty sand through a full-scale field testing programme. Four reinforced concrete piles, two shorter (16 m) and two longer (21 m), were installed and tested under axial tension at two different ageing intervals: 35 days and 165 days post-construction. The load-displacement behaviour, load transfer characteristics, and shaft friction mobilisation were monitored using load cells and embedded strain gauges. Results showed that while all piles exhibited similar ultimate capacities, the aged piles consistently demonstrated stiffer responses and earlier mobilisation of shaft resistance. Extrapolated estimates showed modest increases in estimated ultimate uplift capacity, ranging from 2% to 7%, with ageing. Strain gauge data also indicated more uniform load transfer in the aged piles, suggesting time-dependent improvements in pile-soil interface behaviour. The findings confirm that even in cohesionless silty sand, moderate ageing effects can enhance uplift performance, but the extent of improvement is small and variable. These findings provide a valuable reference for evaluating uplift design assumptions and interpreting field test behaviour in similar soil environments. Full article

Predicting the mechanical performance of Engineered Cementitious Composite (ECC)-strengthened reinforced concrete (RC) beams is both meaningful and challenging. Although existing methods each have their advantages, traditional numerical simulations struggle to capture the complex micro-mechanical behavior of ECC, experimental approaches are costly, and data-driven methods heavily depend on large, high-quality datasets. This study proposes a novel physics-informed machine learning framework that integrates domain-specific empirical knowledge and physical laws into a neural network architecture to enhance predictive accuracy and interpretability. The approach leverages outputs from physics-based simulations and experimental insights as weak supervision and incorporates physically consistent loss terms into the training process to guide the model toward scientifically valid solutions, even for unlabeled or sparse data regimes. While the proposed physics-informed model yields slightly lower accuracy than purely data-driven models (mean squared errors of 0.101 VS. 0.091 on the test set), it demonstrates superior physical consistency and significantly better generalization. This trade-off ensures more robust and scientifically reliable predictions, especially under limited data conditions. The results indicate that the empirical-guided framework is a practical and reliable tool for evaluating the structural performance of ECC-strengthened RC beams, supporting their design, retrofitting, and safety assessment. Full article

This study addresses the challenge of minimizing carbon emissions in designing prestressed road flyovers by comparing advanced predictive modeling techniques for surrogate-based optimization. The research develops a two-stage optimization approach. First, a response surface is generated using Latin-hypercube sampling. Second, that response surface is optimized to identify design configurations with the lowest CO₂ emissions. The optimal configuration (deck #37)—base width 3.40 m, deck depth 1.10 m, and concrete grade C-35 MPa—achieved a carbon footprint of 386,515 kg CO₂, representing a reduction of 12% compared to the reference bridge. Among the models tested, the artificial neural network (ANN) achieved the highest predictive accuracy (RMSE = 8372 kg, MAE = 7356 kg), closely followed by the Kriging 1 model (RMSE = 9235 kg, MAE = 7236 kg). Results indicate that emissions remain minimal for deck depths between 1.10 and 1.30 m, base widths between 3.20 and 3.80 m, and concrete grades of C-35 to C-40 MPa. This study provides practical guidelines for reducing the carbon footprint of prestressed bridges and highlights the value of robust surrogate models in sustainable structural optimization. Full article

This study investigates rapid-setting, phosphate-based, chemically bonded phosphate ceramic (CBPC) composites for emergency pothole repair through a two-phase experimental approach. Phase I involved fundamental mix design experiments that systematically examined the effects of water-to-binder ratio (20–40%), filler content (10–50%), and phosphate powder fineness (570–3640 cm²/g) on setting and mechanical performance. Based on Phase I results, Phase II evaluated field-applicable mixes optimized for concrete and asphalt pavement conditions in terms of rapid strength development: compressive strength exceeding 24 MPa within 30 min, flexural strength surpassing 3.4 MPa within 1 h, and adhesive strength reaching up to 1.62 MPa (concrete) and 0.68 MPa (asphalt) within 4 h. Additional performance evaluations included Marshall stability (49,848 N), water-immersion residual stability (100% under the test protocol), length change (small magnitude over 28 days), and self-filling behavior (complete filling in 17 s in the specified setup). These rapid early-age results met or surpassed relevant domestic specifications used for emergency repair materials. Based on these data, mix designs for field application are proposed, and practical implications and limitations for early-age performance are discussed. Full article

The purpose of this paper is to study the shear performance of reinforced concrete corbels under a synergistic change in the main stirrup inclination angle to explore the synergistic mechanism of the main reinforcement and the stirrup inclination angle, and to evaluate the applicability of existing design specifications. The shear performance test was carried out by designing RC corbel specimens with an inclination angle of the main reinforcement and stirrup. The test results show that a 15° inclination scheme significantly improves the shear performance: the yield load is increased by 28.3%, the ultimate load is increased by 23.6%, the strain of the main reinforcement of the 15° specimen is reduced by 51.3%, the stirrup shows a delayed yield (the yield load is increased by 11.6%) and lower strain level (250 kN is reduced by 23.7%), and the oblique reinforcement optimizes the internal force transfer path and delays the reinforcement yield. A CDP finite element model was established for verification, and the failure mode and crack propagation process of the corbel were accurately reproduced. The prediction error of ultimate load was less than 2.27%. Based on the test data, the existing standard method is tested and a modified formula of the triangular truss model based on the horizontal inclination angle of the tie rod is proposed. The prediction ratio of the bearing capacity is highly consistent with the test value. A function correlation model between the inclination angle of the steel bar and the bearing capacity is constructed, which provides a quantitative theoretical tool for the optimal design of RC corbel inclination parameters. Full article

Ultra-high-performance concrete (UHPC) exhibits significantly superior compressive and tensile properties compared to conventional concrete, demonstrating substantial application potential in bridge engineering. This study conducted full-scale bending tests on a 30 m prestressed UHPC-NC composite box girder within an actual engineering context. The testing flexural capacity $M_t=\mathrm{34,469.2} \mathrm{k}\mathrm{N}·\mathrm{m}$ exceeded the design requirement $M_d=\mathrm{18,138.0} \mathrm{k}\mathrm{N}·\mathrm{m}$, with $M_t/M_d=1.90$. Finite element modeling (FEM) was employed to analyze and predict experimental outcomes, revealing a simulated flexural capacity of approximately 37,597.1 $\mathrm{k}\mathrm{N}·\mathrm{m}$. The finite element models further explored failure mode transitions governed by the loading position while the concentrated load-to-support distance exceeds 9.62 m (shear span to effective depth ratio λ = 6.3), and the box girder fails in flexure; while less than 9.62 m, the box girder fails in shear. The flexural capacity of the test girder was also estimated using Response-2000 software and the recommended formulas from the Chinese code T/CCES 27-2021 (Technical specification for ultra-high-performance concrete girder bridge). The Response-2000 software yielded a flexural capacity estimate of $M_r=\mathrm{30,816.1} \mathrm{k}\mathrm{N}·\mathrm{m}$. The technical specification provided two estimating results: (with safety factors) $M_{u1}=\mathrm{25,414.4} \mathrm{k}\mathrm{N}·\mathrm{m}$ and (without safety factors) $M_{u2}=\mathrm{33,810.9} \mathrm{k}\mathrm{N}·\mathrm{m}$. All estimated values of Response-2000 and Chinese code were rationally conservative ($M_r,{ M}_{u1}, M_{u2}<M_t$). Comparative analysis demonstrates that Abaqus FEM accurately simulates the flexural behavior of the prestressed UHPC-NC composite box girders. Both Response-2000 calculations and the Chinese code T/CCES 27-2021 provide critical references for similar applications of prestressed UHPC-NC composite box girders. Full article

To evaluate the mechanical properties of connectors in timber–concrete composite (TCC) structures under low-cycle reversed loading, eighteen push-out specimens were designed and fabricated following the standard push-out test method. This study presents the first comparative analysis of the seismic performance between notch-bolted and ordinary bolted connections across three bolt diameters (12 mm, 16 mm, and 20 mm), addressing a gap in systematic experimental data for different connection types. Key performance indices under cyclic loading—including stiffness degradation, strength degradation, energy dissipation capacity, and ductility—were investigated. Furthermore, cumulative damage analysis elucidated the damage accumulation process, establishing a damage index (Dw) based on an energy method and proposing Dw = 0.6 as a critical early-warning threshold for failure. Practical recommendations for seismic design and engineering applications are provided. The results demonstrate that compared to ordinary bolted connections, notch-bolted connections achieve a 15–30% increase in ultimate bearing capacity and exhibit superior stiffness. Specimens with 16 mm bolts exhibited optimal ductility (ductility coefficient ξ = 3.6), while notch-bolted connections maintained stable ductility within the range of ξ = 2–3. Finally, a numerical model was developed using ANSYS finite element software. Validation against experimental results confirmed the model’s accuracy in simulating structural behavior. This research elucidates the cumulative damage mechanisms in TCC structures under cyclic loading, providing a theoretical basis for design optimization and valuable insights for promoting the seismic application of these composite systems. Full article