Search Results (1,698)

Offshore reinforced concrete (RC) structures, such as bridges and high-piled wharves, are frequently subjected to the coupled action of steel corrosion and ship collision loads. However, existing studies lack systematic quantification and in-depth revelation of the synergistic degradation mechanism under this coupling effect, resulting in an insufficient scientific basis for engineering design and reinforcement. To address this gap, this study established a refined three-dimensional numerical model of drop hammer-reinforced concrete beams based on ABAQUS, comprehensively considering the strain rate effects of steel and concrete, steel–concrete bond–slip behavior, and the trilinear constitutive model of corroded steel. After validating the model’s reliability against experimental data from the existing literature, parametric simulations were conducted to investigate the coupled effects of different corrosion rates and drop heights (0.25–1.5 m). Key findings include: (1) corrosion reduces the peak impact force by 9.7–58.9% and increases the maximum mid-span displacement by 6.6–35.7%, with this effect amplified by higher drop heights; (2) shear performance degradation (16.14–35.19%) is significantly more severe than flexural performance degradation (13.28–28.93%), confirming that shear performance is more sensitive to corrosion; (3) corrosion causes cracks to propagate from a localized distribution to a global distribution, while higher drop heights accelerate structural evolution toward brittle failure; (4) the synergistic degradation law of “corrosion exacerbates impact damage, and impact amplifies corrosion defects” is revealed. By quantifying the corrosion–impact coupling effect, this study advances research in the field and provides critical technical support for damage assessment and service life prediction for offshore RC structures. In engineering practice, it is recommended that offshore structures in high-corrosion environments prioritize shear resistance enhancement and adopt targeted protective measures for high-impact-risk areas to mitigate the risk of brittle failure. Full article

Concrete-encased steel (CES) bridge piers can be considered as a robust alternative to traditional reinforced concrete sections, especially in regions prone to seismic activity. CES piers combine the ductility of steel with the compressive strength of concrete, offering improved energy dissipation and resilience during earthquakes. Given the lack of CES design specifications in the Canadian design code, it is crucial to compile a body of knowledge describing the behavior of the CES bridge pier in order to facilitate the codification of the design guide. This study assesses the seismic performance of CES rectangular bridge piers with a focus on how variations in the steel section configuration affect the pier’s overall behavior under seismic loads. To conduct this assessment, a fiber element model was employed to model CES bridge piers subjected to seismic loading. The thickness and height of the web and the width and the thickness of the flanges of the I-shape steel section were varied to understand their impact on the bridge’s seismic performance. In addition to the I-shape sections, a crossed two-I-shape section was also studied. Spectral analysis, nonlinear pushover analysis and nonlinear time-history analysis was performed on the bridge models in order to better understand the seismic performance of the studied bridge piers. Simulation results indicate that larger flanges increase the pier’s bending moment capacity, allowing it to absorb greater seismic energy and undergo larger deformations without failing. This increases the overall ductility of the pier and enhances its ability to dissipate seismic energy. However, excessively large flanges or web can reduce the concrete cover and reduce the durability of the pier in the context of Canadian extreme-winter conditions. The study concludes that a balance between web thickness and flange width must be achieved to ensure the bridge can resist seismic forces while maintaining sufficient ductility and energy dissipation. Therefore, an optimized design, according to seismic demands, enhances the overall resistance of CES bridge piers. Full article

To address issues such as web and bottom plate cracking and insufficient bending capacity in in-service prestressed concrete box girder bridges, this study proposes internal prestressing and section enlargement composite reinforcement. Firstly, taking a bridge of Shenhai Expressway as the background project, the combined reinforcement method is designed and the reinforcement effect is analyzed by MIDAS/Civil. Secondly, through numerical analysis, the influence of the bond shrinkage of self-compacting concrete with different mix ratios on the stress of the web of the original box girder is analyzed, and the interface between the new and old concrete is carried out. The analysis of the loss of the new prestress on the bonding surface of the new and old concrete is carried out by parameters such as the interface planting rate, the interface shear stiffness and the reinforcement structure. Furthermore, the theoretical calculation method of prestress loss rate of new and old concrete bonding interface is obtained. The results show that the flexural capacity of the normal section of the main beam is significantly improved after reinforcement, and the surplus coefficient is 1.18, which meets the requirements of the secondary safety level, and the mid-span deflection is improved by 34.28%, which verifies the effectiveness and feasibility of the combined reinforcement method. When the content of fly ash is 54%, the bond shrinkage strain and shrinkage stress of self-compacting concrete are reduced to the lowest level, which has the least influence on the existing box girder structure. It is suggested that the reinforcement ratio between the new and old concrete interface is 0.6%, and the interface roughness is 0.9 mm, which can increase the shear resistance of the new and old concrete interface and effectively reduce the transfer loss of prestress at the interface. Error analysis shows that the proposed semi-empirical calculation method has high accuracy with a deviation of less than 10%. Full article

Quantum computing offers potential computational advantages, yet its integration into autonomous decision-making systems remains largely unexplored. This paper addresses the need for a unified framework that systematically combines quantum computation with agent-based artificial intelligence. We examine how quantum technologies can enhance the capabilities of autonomous agents and, conversely, how agentic AI can support the advancement of quantum systems. We analyze both directions of this synergy and present conceptual and technical foundations for future quantum–agentic platforms. Our work introduces a formal definition of quantum agents and outlines architectures that integrate quantum computing with agent-based systems. As concrete proof-of-concept implementations, we develop and evaluate three quantum agent prototypes: (i) a Grover-based decision agent for quantum search-driven action selection, (ii) a variational quantum reinforcement learning agent for adaptive policy learning in a multi-armed bandit setting, and (iii) an adaptive quantum image encryption agent that autonomously selects encryption strategies based on entropy-driven feedback. These prototypes demonstrate practical realizations of quantum agency in decision-making, learning, and security contexts under NISQ-era constraints. Furthermore, we discuss application domains including quantum-enhanced optimization, hybrid quantum–classical orchestration, autonomous quantum workflow management, and secure quantum information processing. By bridging these fields, we introduce a structured theoretical and architectural framework for quantum–agentic systems, providing formal definitions, system models, and early operational prototypes that illustrate the feasibility of quantum-enhanced agency under NISQ constraints. Full article

To upgrade the mechanical properties and reduce the high brittleness of coral aggregate concrete (CAC), calcium sulfate whisker (CSW) has been innovatively used as a reinforcing material in this study. Five incorporation levels (0–4%) were designed to systematically investigate the evolution mechanism of CAC mechanical, microstructure, and pore characteristics at different curing ages. The results showed that CSW incorporation can significantly improve the mechanical properties of CAC; 1% CSW can bring 36.7% enhancement to 14-day compressive strength, and 11.9% improvement to 28-day splitting tensile strength with 2% CSW. Mechanism analysis revealed that appropriate CSW content effectively suppressed microcrack propagation through whisker bridging effects and remarkably enhanced the cement paste–coral aggregate interfacial bond strength by 71%, promoting a transition in failure mode from interfacial failure to aggregate fracture. At the same time, CSW improved the pore structure by reducing the proportion of macropores and increasing the micropore proportion to 76% with 1% CSW content. However, the performance deterioration of CAC caused by CSW excess (4%) was mainly due to the defect formation resulting from whisker agglomeration. The proposed strength prediction models (R² > 0.93) based on experimental data can reliably describe the enhancement effect of CSW. Full article

Reactive powder concrete (RPC) delivers outstanding mechanical performance and durability; however, it is commonly hindered by high cement consumption, elevated embodied carbon emissions, and high material costs. To mitigate these drawbacks, this study develops a low-carbon, cost-effective RPC incorporating high-volume class-F fly ash, a reduced silica fume dosage, conventional river sand, and an optimized steel fiber system. A systematic mix design framework, combining particle packing density with paste rheology optimization, was employed to balance workability, strength, and durability. The optimized mixtures were evaluated for compressive, splitting tensile, and flexural strength, as well as durability-related metrics, including water absorption rate and resistance to chloride penetration. Environmental impact and cost-effectiveness were further quantified via embodied carbon accounting and strength-normalized performance indices. The results show that well-designed high-volume fly ash RPC can achieve compressive strengths above 130 MPa while maintaining excellent impermeability, alongside substantial reductions in both material cost and carbon footprint relative to conventional RPC. In addition, mixed-size steel fibers further enhance mechanical performance through multi-scale crack bridging. Overall, this work provides a practical route to decouple ultra-high performance from high environmental burden, supporting the sustainable deployment of RPC in infrastructure engineering. Full article

The long-term behavior of reinforced concrete (RC) structures under sustained loading is strongly affected by creep and cracking, particularly under service conditions where tension stiffening and curvature changes are significant. This study investigates the flexural response of cracked RC beams through combined numerical and experimental analyses. A new 1D finite element model is proposed, integrating nonlinear material behavior, damage mechanics, and time-dependent effects, including creep in both compression and tension. The model relies on a layered fiber section approach and uses a Newton–Raphson iterative procedure to solve equilibrium, allowing accurate prediction of strain, curvature, and internal force evolution over time. The model shows excellent agreement with experimental observations and ABAQUS simulations, accurately capturing deflection trends and crack development. Its performance is further validated using a database of 55 RC beams, including specimens with recycled aggregates and fiber reinforcement. Across this dataset, 84.5% of predicted deflections fall within ±1 mm of measured values, with an R² of 0.960, demonstrating strong reliability. A Sobol-based sensitivity analysis identifies load ratio as the most influential parameter on long-term deflection, followed by concrete strength and humidity. Overall, the model offers an efficient and robust tool for long-term deflection prediction, bridging simplified design rules and complex 3D simulations. Full article

Prestressed concrete (PSC) beams are widely used in bridges and large structures due to their high load-bearing capacity and crack resistance. However, owing to their complex construction process, they are highly sensitive to temperature variations. Implementing temperature monitoring at this stage helps assess the actual mechanical behavior and effective prestress of the beam, providing a reliable basis for construction control and prestress adjustment. This study aims to investigate the mechanical performance of a bidirectionally stiffened composite tensioning and anchoring system developed earlier by the research team during the construction phase and to elucidate the effect of temperature on the mechanical behavior of pretensioned prestressed concrete beams. By deploying a monitoring system integrated with high-precision sensors, synchronized temperature and displacement data during tensioning, pouring, and curing stages were obtained. Field-measured data were used to validate finite element models under different thermal load conditions. The results indicate that the heat of hydration of concrete causes a temperature difference of 12.0 °C at the end form, leading to a maximum displacement of 0.2 mm at the top of the anchor plate. Notably, a temperature change of 22 °C induces a prestress fluctuation of 0.12% to 0.3%. The numerical model demonstrates strong accuracy, with the highest agreement with experimental data and an error of less than 7.5%. These findings provide a scientific basis for compensating prestress losses and controlling the deformation of prestressed concrete beam structures, playing a critical role in ensuring the long-term safety and performance of structures under complex working conditions. Full article

Surface defects such as depressions, heaving, and irregular undulations frequently develop on aging concrete bridge decks under repeated traffic loading and environmental effects. Accurate and objective identification of such defects is essential for structural serviceability and safety, yet manual inspection remains labor-intensive and subjective. This study develops a systematic framework for surface defect identification through geometric feature augmentation with a streamlined point cloud learning strategy. In practical engineering scenarios, point cloud data of concrete bridge decks can be periodically acquired via vehicle-mounted mobile laser scanning (MLS) systems and subsequently streamlined for analysis. The proposed method heightens defect sensitivity by extracting interpretable geometric descriptors, further integrating multi-scale representations to capture surface defects across varying spatial extents. Evaluated on a public point-level annotated benchmark, the proposed method clearly outperforms the same network trained with geometric coordinates only. To improve result reliability, all experiments were repeated four times with different random seeds, and the performance is reported as mean ± standard deviation. Results show that the proposed method achieves a precision of 0.597 ± 0.021 and an accuracy of 0.933 ± 0.009 under the benchmark protocol. Overall, these results demonstrate a reproducible proof of concept under controlled benchmark conditions for bridge deck surface defect segmentation, while broader cross-site and cross-sensor validation will be pursued in future work. Full article

Expanded polystyrene (EPS) foam concrete is attractive for lightweight building applications, yet its practical use is often limited by weak EPS–cement interfacial bonding, which promotes interfacial debonding and crack propagation and thereby compromises mechanical performance. Although nano-SiO₂ (NS) has been reported to improve EPS–cement compatibility, the interfacial strengthening mechanism is still not fully clarified across scales, especially the molecular-level interactions that govern the formation of a robust interfacial transition zone (ITZ). Herein, EPS particles were modified with NS and a multi-scale framework (macro tests, micro-characterization, and molecular dynamics (MD) simulations) was employed to establish a mechanistic linkage between interfacial chemistry/structure and macroscopic performance. The results show that an optimal NS dosage of 9% (by cement mass) increases the 28-day compressive strength and flexural strength of EPS concrete by up to 18.3% and 11.2%, respectively, compared with the unmodified system. SEM, XRD, and FTIR collectively indicate a denser interfacial microstructure, increased hydration-product accumulation near the EPS surface, refined interfacial porosity, and the occurrence of condensation-related reactions involving NS. MD simulations further reveal that NS facilitates the formation of molecular bridges between EPS and C–S–H through hydrogen bonding and ionic interactions, which enhances interfacial adhesion and contributes to improved ITZ thermal stability. This study provides a cross-scale mechanistic understanding for designing high-performance EPS foam concrete via targeted interfacial engineering. MD simulations further suggest that NS enhances interfacial bonding by increasing the occurrence of hydrogen-bond networks and ionic associations at the EPS/C–S–H interface, as evidenced by the intensified interaction-related distributions and peaks in the simulation outputs. Full article

Moving beyond traditional reactive maintenance decisions, this study explores a preventive maintenance strategy for highway bridges by integrating long-term durability forecasting. This need is addressed by analyzing two decades of historical inspection data from China. Visual condition records were sourced from a management system covering 2854 bridges, while durability parameters were obtained through 31 field tests on 23 bridges. This research introduces an instantaneous carbonation coefficient, which quantifies the carbonation rate specific to each discrete condition rating. The analysis reveals a 700% surge in the carbonation rate for poor condition states relative to intact ones, significantly higher than the 300% increase projected by traditional averaged models. Under the premise that maintenance can restore a bridge’s condition by one rating grade, three maintenance strategies are evaluated. Results indicate that initiating preventive interventions at a qualified condition can reduce long-term maintenance frequency by about 20%, offering a practical, condition-informed framework for optimizing maintenance planning and resource allocation. Full article

This study investigates the interaction between polycarboxylate-based water-reducing admixtures (WRAs) and various types of CEM I 42.5R Portland cements, focusing on optimizing input parameters in cementitious systems. Despite the widespread use of WRAs to enhance concrete’s workability, strength, and durability, their compatibility with cement remains a critical challenge, often leading to performance issues such as low initial flow, bleeding, and rapid slump loss. This research addresses two significant gaps in the literature: the unexplored use of input parameter reduction in cementitious systems and the application of novel metaheuristic algorithms in optimizing these systems. In this study, 25 WRA were first synthesized to enrich the inputs of machine learning (ML) models. Then, a dataset of 750 entries was generated, and advanced prediction models were developed. To ensure scientific rigor and eliminate data leakage, a triple-split dataset strategy (Training–Validation–Test) and 5-fold cross-validation were implemented. Among the machine learning techniques analyzed, the Optimized Artificial Neural Networks (opANN) architecture decisively demonstrated the highest prediction performance on the isolated test dataset. In the opANN process, 10 different metaheuristics were tested to evaluate their effectiveness in hyperparameter optimization. As a result, the Kepler Optimization (KOA) algorithm was determined as the algorithm with the highest performance in ANN hyperparameter optimization. Furthermore, Shapley Additive Explanations (SHAP) analysis was utilized to bridge the gap between empirical observations and algorithmic predictions, quantitatively corroborating the rheological roles of phosphate and sulfonate groups. The results offer new insights into WRA–cement compatibility and present advanced, interpretable modeling approaches that enhance predictive accuracy, contributing to more reliable and sustainable concrete practices. Full article

The utilization of cement is one of the primary sources of carbon emissions in concrete, driving the search for sustainable alternative materials. Although extensive research has been conducted on the use of agricultural waste as supplementary cementitious materials (SCMs), the effects of coconut shell ash (CSA) and coir fiber (CF) on concrete properties have not been extensively investigated. This study systematically investigates the influence of CSA as a SCM (0–20%) and CF as a reinforcement material (0–0.32%) on the workability, density, compressive strength, flexural strength, splitting tensile strength, and failure modes of concrete, complemented by microstructural mechanism analysis. The cement and CSA were characterized using XRF, XRD, and SEM. The results indicate that the incorporation of both CSA and CF reduces the workability and density of concrete. For concrete with CSA only, the compressive strength decreases by up to 24.7% when the replacement level reaches 20%. However, concrete with 10% CSA still maintains 87.2% of the strength of ordinary concrete, which satisfies the C40 requirement. In contrast, CF incorporation alone improves the mechanical properties, with compressive strength, flexural strength, and splitting tensile strength reaching peak increases of 6.4%, 13.9%, and 7.5%, respectively, when the CF content is 0.24%. Incorporating 0.16% CF into 10% CSA concrete mitigates the strength reduction caused by CSA, achieving compressive, flexural, and splitting tensile strengths of 47.99 MPa, 5.63 MPa, and 3.99 MPa, respectively (95.7%, 98.3%, and 96.4% of the strengths of ordinary concrete). Microstructural analysis reveals that CSA deteriorates the interfacial transition zone (ITZ), while CF compensates for partial strength loss through the bridging effect, although its reinforcement efficiency is influenced by fiber dispersion and ITZ quality. This study provides a theoretical foundation and technical reference for the utilization of coconut shell waste in sustainable concrete. Full article

Excessive deflection during the service period of long-span prestressed concrete (PC) bridges remains a persistent challenge in bridge engineering. This study proposes a prestressing design strategy for PC bridges that targets a desired structural curvature (DSC) by counteracting self-weight and external loads, thereby controlling both the initial curvature and its time-dependent evolution associated with prestress losses. The proposed framework was verified through a numerical simulation of a long-term simply supported beam test lasting 1350 days, showing that the mid-span deflection was significantly mitigated and the stress distributions were changed under sustained loading. Furthermore, the applicability of the proposed method is demonstrated through evaluations of two in-service long-span PC girder bridges. Compared with the original designs, the proposed method effectively controls excessive mid-span deflection and improves the bending moment (BM) and stress distributions. For the three-span PC rigid frame bridge constructed using the symmetrical cantilever method, the mid-span deflection was reduced by approximately 63% at 3500 days of service and remained stable after retrofitting. For the five-span continuous PC bridge erected by means of symmetrical cantilever construction, the secondary mid-span deflection at 4800 days was reduced by nearly 70%, satisfying serviceability requirements. These results demonstrate that the proposed DSC-based prestressing design method provides an effective and practical solution for mitigating time-dependent deflection of long-span PC bridges and ensuring robust performance throughout the service life. Full article

Reliable structural safety risk diagnosis and control of prestressed concrete bridges is essential for safe operation. Unfortunately, current practice relies heavily on inspectors’ engineering knowledge, field experience, and subjective judgments. In addition, existing general-purpose large language models (LLMs) often underperform in bridge defect diagnosis because of missing domain terminology and hallucinated technical statements. Therefore, there is a need to establish a trustworthy and explainable method for structural safety risk diagnosis and control. This study develops a domain knowledge-graph-enhanced framework, the prestressed concrete bridge defect knowledge-graph-enhanced LLM (PCBDK-LLM), to support defect diagnosis and treatment recommendations for prestressed concrete bridges. First, a prestressed concrete bridge defect knowledge graph is constructed using a hybrid approach that combines direct text-driven extraction from standards, peer-reviewed literature, and inspection reports with an ontology-based schema and consistency axioms. Then, the authors propose a retrieval module (REM) that performs ontology-aware chunking and hybrid similarity search to ground a locally deployed dialogue model (DeepSeek-R1) on verified domain knowledge. Eight real rehabilitation cases (eight bridges) are used to evaluate model recommendations against a reference solution documented in the project materials. Results indicate that the proposed PCBDK-LLM generates treatment suggestions that are more consistent with the reference plan than the baseline LLM and the ablation variants. Full article