Search Results (333)

In reinforced concrete structures, the primary and secondary beam joints are capable of transferring concentrated loads through additional transverse reinforcement, such as additional stirrups or additional hanging reinforcement. Ensuring the structural safety of these joints under situations such as small height differences between the primary and secondary beams, large secondary beam loads, or marine environments is crucial. Current research mainly focuses on the structural safety of individual structural members or conventional frame joints. Studies on primary and secondary beam joints are limited, and the performance of additional hanging reinforcement under special situations has been neglected. This study identifies two special situations at primary and secondary beam joints—the “small height difference” and the “large load”—and verifies their rationality through numerical simulations. Furthermore, an ultimate bearing capacity degradation ratio is introduced to quantify the effect of reinforcement corrosion on the mechanical performance of these joints. The results demonstrate that, without considering reinforcement corrosion, beams with additional stirrups fail to satisfy the ultimate bearing capacity requirements under the “small height difference” and “large load” situations. In such cases, additional hanging reinforcement should be adopted as the reinforcement mode. When reinforcement corrosion is considered, the ultimate bearing capacity degradation ratio of the beam with additional stirrups drops to approximately 0.65 after 30 years of service, whereas that of the beam with additional hanging reinforcement remains above 0.95. Under these situations, additional hanging reinforcement is even more preferable. This study clarifies the selection principle for primary and secondary beam joint additional transverse reinforcement under the two special situations and corrosion environments. The research findings provide guidance and reference for the structural design of reinforced concrete primary and secondary beam joints. Full article

To explore the shear behavior and evolutionary pattern of desert sand concrete beams (DSCBs) subjected to freeze–thaw cycles, 16 DSCBs were subjected to rapid freeze–thaw cycling and shear tests, with desert sand replacement ratios (0%, 20%, 40%, and 60%) and numbers of freeze–thaw cycles (0, 25, 50, and 75) considered as the main variables. The failure mode, diagonal crack development, diagonal cracking load, shear capacity, and load–stirrup strain curves of DSCBs were tested and analyzed. The results indicate that all specimens exhibited typical shear-compression failure. The diagonal crack development pattern of DSCBs was similar to that of ordinary concrete beams, whereas freeze–thaw cycles accelerated the initiation and propagation of cracks. Freeze–thaw cycling significantly reduced both the diagonal cracking load and shear capacity. After being exposed to 75 cycles of freezing and thawing, the ultimate shear capacity of test pieces with desert sand replacement proportions of 0%, 20%, 40%, and 60% decreased by 15.6%, 12.9%, 13.9%, and 13.8%, respectively, while the corresponding stirrup strains increased by 47.2%, 34.1%, 37.1%, and 53.7%, respectively. An appropriate desert sand replacement ratio can improve the shear performance of concrete beams. Among all specimens, the beam with a 20% replacement ratio exhibited the best overall mechanical performance, achieving a maximum increase of 6.0% in shear capacity and a maximum reduction of 26.8% in stirrup strain compared with conventional concrete beams. Finally, by introducing modification coefficients related to the desert sand replacement ratio as well as the freeze–thaw cycling times, predictive equations for the diagonal cracking load and shear capacity of DSCBs under freeze–thaw conditions were established. The numerical predictions achieve a high consistency with measured data. Full article

This study investigates the shear damage mechanisms in reinforced concrete (RC) beams through non-linear numerical modeling. Using the Finite Element Method (FEM) in ABAQUS, a Concrete Damaged Plasticity (CDP) framework was validated against experimental results and subsequently utilized for a 36-model parametric investigation. The study isolated the influence of stirrup spacing, diameter, and yield strength to evaluate their roles in ultimate shear capacity. The results indicated that while increasing stirrup diameter yielded modest capacity enhancements of approximately 7%, the impact of increasing yield strength was negligible, as the failure modes were primarily governed by concrete web crushing before reinforcement yielding could occur. These physical limit states were compared against the linear Truss Analogy adopted by major design standards—including ACI 318-19, Eurocode 2, and TS 500—to quantify discrepancies in heavily reinforced sections. The findings reveal that, strictly within the investigated parameter space (a/d = 2.67, f’_c = 28.5 MPa), current linear equations can significantly overestimate the physical capacity gains provided by reinforcement modifications. These observations are configuration-specific and highlight the need for cautious application of linear models in heavily reinforced scenarios. Furthermore, the study suggests that utilizing 3D beam elements for transverse reinforcement provides a more nuanced representation of shear transfer mechanisms, such as dowel action, compared to standard truss models. Full article

The seismic fragility of reinforced concrete (RC) bridge piers is critical for urban rail transport systems, as severe pier damage may interrupt post-earthquake operation and threaten network safety. Compared with conventional highway bridge piers, urban rail transport RC solid piers usually have lower axial load ratios, larger cross-sections, and stricter serviceability requirements. However, the combined effects of geometric parameters, reinforcement detailing, and material strength on their cyclic behavior, dynamic response, and seismic fragility remain insufficiently understood. To address this issue, seven 1/4-scale RC solid pier specimens were tested under quasi-static cyclic loading to examine the effects of pier height, transverse reinforcement ratio, and longitudinal reinforcement ratio on damage evolution, hysteretic response, skeleton curves, and energy dissipation. A fiber-based OpenSees model considering bond-slip effects was then established, validated against the tests, and extended to a full-scale prototype pier for parametric analysis. The effects of aspect ratio, axial load ratio, longitudinal reinforcement ratio, stirrup ratio, steel yield strength, and concrete strength were evaluated under cyclic loading and nonlinear dynamic time-history excitations. An incremental dynamic analysis-based probabilistic seismic demand model was further developed using 30 near-fault ground motions, with peak ground acceleration as the intensity measure and displacement ductility as the engineering demand parameter. The results showed that increasing the aspect ratio changed the failure mode from flexure-shear-dominated to flexure-dominated behavior, increasing the ultimate displacement from 122 mm to 155 mm while reducing the peak lateral strength from 263 kN to 248 kN. Increasing the longitudinal reinforcement ratio improved both peak strength and ultimate displacement, from 226 kN to 262 kN and from 120 mm to 160 mm, respectively. The numerical results indicated that aspect ratio, axial load ratio, and longitudinal reinforcement ratio had more pronounced effects on seismic demand and fragility than stirrup ratio. Increasing steel yield strength generally reduced seismic fragility, whereas increasing concrete strength enhanced lateral resistance but did not necessarily improve fragility performance. These findings suggest that the seismic performance of urban rail transport RC solid piers should be evaluated by combining cyclic response, dynamic demand, and fragility-based performance, rather than by maximizing any single design parameter. Full article

To investigate the structural dynamic response of pile-column bridge piers in mountainous regions under rockfall impact, this study takes the No. 4 double-column pier of Changba Bridge in Nanchuan, Chongqing, as a prototype. Based on similarity theory and differential equation analysis, a scaled model test was designed and conducted. By considering different rockfall impact angles (30°, 45°, 60°) and different impact positions on the pier (top, middle, bottom), the strain response characteristics of the pier concrete and reinforcing steel were systematically analyzed. The results indicate that the peak strain at the impacted location increases significantly with the increase in the rockfall impact angle; when the impact angle increases from 30° to 60°, the peak strain increases by approximately 22.8%. The peak strain decreases as the impact position approaches the bottom of the pier, with the most pronounced strain response observed at the middle position. The strain response of the reinforcing steel follows the same pattern as that of the concrete, albeit with a brief delay. Furthermore, the stirrups exhibit predominantly transverse orthogonal strain, while the longitudinal reinforcing bars exhibit predominantly longitudinal orthogonal strain. It is concluded that the impact angle is a key parameter affecting local damage to the pier, and the middle section of the pier should be regarded as a priority protection zone. This study provides a theoretical basis for the design of bridge piers in mountainous regions against rockfall impact. Full article

Coupling beams are critical connecting components in coupled shear wall systems and core tube structures. At the same time, they play an important role when the structure is subjected to an earthquake. Plate-reinforced composite (PRC) coupling beams exhibit superior comprehensive performance in terms of bearing capacity, deformation performance, energy dissipation capacity, and construction efficiency. However, research on PRC coupling beams remains limited both domestically and internationally. To better describe the structural response of steel plate–concrete composite coupling beams, this study collected existing experimental data. The beams had a small span-to-depth ratio. The loading was cyclic. The study normalized the skeleton curves of each specimen. The span-to-depth ratio ranged from 0.9 to 2.5. The plate ratio ranged from 3% to 5%. For these beams, preliminary skeleton curve fitting equations are proposed. The equations are based on existing data. The equations apply to two types of composite coupling beams. One type uses a steel plate and ordinary concrete. The other type uses a steel plate and fiber concrete. These equations are derived using a trilinear model and linear fitting tools. Furthermore, restoring force models for steel plate–conventional concrete and steel plate–fiber concrete composite coupling beams with a small span-to-depth ratio are proposed. Comparative analysis shows that each model captures the hysteretic response of PRC coupling beams with acceptable accuracy in the elastic and decline phases, while the elastic–plastic stage is suitable only for trend prediction. It should be noted that the proposed models are preliminary engineering approximations primarily applicable within the following ranges: a span-to-depth ratio of 0.9~2.5, a plate ratio of 3~5%, concrete strength of C30~C50, a longitudinal reinforcement ratio of 0.86~2.23%, a stirrup ratio of 0.56~0.63%, and a steel plate thickness of 6~10 mm. For configurations significantly outside these ranges, additional experimental validation is required. Full article

Reinforced concrete (RC) columns in cold regions are often exposed to combined seismic actions and cryogenic environments, which can significantly alter their structural response. This study examines the seismic performance of RC columns over a temperature range of 20 °C to −90 °C using numerical simulations, with axial load ratios of 0.0–0.6 and stirrup ratios of 1.0–3.0% considered. The results reveal that failure modes remain generally consistent across temperatures, while damage becomes more pronounced at lower temperatures. A decrease in temperature leads to higher peak load and initial stiffness, accompanied by a reduction in ductility. Taking the specimens with ρ_sv = 1.0% as an example, as the temperature decreases from 20 °C to −30 °C, −60 °C, and −90 °C, the peak load increases by 10.9%, 17.1%, and 32.7%, respectively. As the temperature decreased from 20 °C to −90 °C, the ductility coefficient decreased by 33.3%, and the total dissipated energy increased by 6.4%. Increasing the stirrup ratio enhances deformation capacity and partially mitigates ductility loss. Furthermore, the influence of axial load ratio on hysteretic response follows a similar pattern to that at ambient temperature, but with greater sensitivity under cryogenic conditions. Based on the numerical findings, predictive expressions are proposed to estimate the plastic hinge length and flexural strength considering temperature effects. Full article

The deterioration of steel reinforcement through corrosion triggers cracking and loss of concrete cover, ultimately weakening the structure’s strength and ductility. In practical design and assessment, it is vital to precisely quantify the shear capacity of corroded reinforced concrete beams (CRCBs). In this paper, machine learning (ML) models are developed to predict the shear capacity of CRCBs, including kernel ridge regression (KRR), K-nearest neighbors (KNN), decision trees (DT), random forest (RF), gradient-boosted regression trees (GBRT), and extreme gradient boosting (XGBoost). A total of 408 data entries on the shear strength of CRCBs under different corrosion conditions were collected to establish an extensive database. The reliability of the proposed ML models is examined by contrasting their outputs with the experimental data. The XGBoost model demonstrated superior predictive capability, achieving an R² value of 0.994 and outperforming all other tested models, including RF, GBRT, and DT. The Shapley Additive Explanations (SHAP) algorithm is adopted to reveal the contribution of each input feature to the predicted shear capacity of CRCBs. The interpretive SHAP results show that the ultimate shear capacity of CRCBs is most influenced by beam depth (h), with the shear span-to-depth ratio (λ) and concrete compressive strength ($f_{cl,150}$) being the subsequent key contributors. A comparative assessment between the XGBoost model and traditional analytical models was carried out to estimate the shear strength of CRCBs. Results demonstrate that the XGBoost model delivers enhanced predictive accuracy and improved performance. A parametric investigation examined its robustness under variations in geometry and material properties, while a user-friendly interface was created to support its practical use. Full article

Glass fiber-reinforced polymer (GFRP) composites offer a compelling solution to the durability degradation of reinforced concrete (RC) structures in harsh marine and de-icing environments. Hybridizing fiber-reinforced polymer (FRP) with conventional steel reinforcement synergizes the superior corrosion resistance of FRP with the high ductility of steel. However, the synergistic mechanisms of GFRP–steel hybrid reinforced columns confined by either GFRP or steel spiral stirrups under axial compression remain insufficiently quantified. This study systematically investigates the axial compressive performance of such structures through material testing, static axial compression tests on seven short column specimens, and advanced finite element (FE) modeling. The investigation focuses on the effects of the steel-to-GFRP area ratio and the spiral stirrup type. Experimental results reveal that spirally confined hybrid columns exhibit failure modes remarkably similar to conventional RC columns. The incorporation of GFRP bars significantly enhanced the ultimate load-bearing capacity, while the steel bars ensured the requisite ductility. Notably, a higher ultimate capacity was achieved at a steel-to-GFRP area ratio of 1:1 under steel spiral confinement, retaining a ductility index equivalent to 83.6% of a pure RC column. Furthermore, an ABAQUS-based FE model was developed and rigorously validated against experimental data, successfully capturing the failure progression and ultimate capacities across diverse parameters. Ultimately, based on the superposition principle, by quantifying the independent load-bearing contributions and synergistic interactions of the spalled concrete cover, confined core, and hybrid bars, this study derives a theoretical formula. The proposed model accurately predicts the axial compressive capacity of spirally confined hybrid columns, providing an analytical tool for resilient structural design. Full article

To investigate the shear performance of steel fiber-reinforced high-strength concrete (SFRHSC) corbels subjected to concentrated loading, an experimental program was executed on six specimens featuring welded anchorage for the upper longitudinal reinforcement. The control variables included shear span-to-depth ratios of 0.2 to 0.5 and steel fiber volume fractions of 0%, 0.75%, and 1.50%. During the testing phase, strain evolution within the steel reinforcement and concrete matrix was monitored to analyze the structural deformation sequence and ultimate failure modes. Anchored in the Mohr–Coulomb failure criterion and the foundational strut-and-tie model (STM) framework, a softened strut-and-tie calculation approach for corbel shear capacity was formulated; this method explicitly accounts for the softening effect of the steel fiber-reinforced concrete (SFRC) and incorporates a size effect correction. The established shear capacity calculation model, alongside STM-based provisions from ACI 318-19, EN 1992-1-1, and CSA A23.3-19, was deployed to forecast the shear capacities of the six fabricated specimens and 18 additional units sourced from existing literature. Ultimately, a rigorous comparative analysis was conducted between the theoretical predictions generated by each method and the empirical test data. The results indicate that the failure process of the SFRHSC corbels primarily involves three distinct stages: initial cracking, through cracking, and ultimate failure. The addition of steel fibers can alleviate stress concentration at cracks and limit crack growth, thereby improving the tensile performance of the cracked concrete. Test results indicate that the strain in the longitudinal tensile reinforcement increased with the shear span-to-depth ratio but decreased as the steel fiber volume fraction increased. At the point of specimen failure, all longitudinal tensile reinforcement had reached the yield strength, while the horizontal stirrups only partially yielded. The concrete strain distribution across the normal section of the corbel did not follow the plane section assumption. Furthermore, incorporating steel fibers increased both the cracking load and the ultimate load of the corbel normal sections. The mean value of the experimental-to-predicted ratios obtained from the STM provisions of various international codes was 1.453, with a variance of 0.029, indicating conservative calculation results. In contrast, the mean value of the experimental-to-predicted ratios for the calculation model developed in this study was 1.048, with a variance of 0.004, demonstrating closer agreement with the experimental results and less dispersion. Simultaneously, by explicitly considering the softening effect in SFRHSC and the size effect, it provides a better prediction for the shear capacity of corbels. Full article

The bond strength of corroded reinforced concrete (CRC) structures is critical for structural safety and long-term durability. However, the corrosion-induced bond degradation process is influenced by multiple, coupled factors and exhibits complex, nonlinear behavior, making it difficult for traditional theoretical models to provide accurate predictions. To address this challenge, this study proposes a novel, unified prediction framework based on machine learning techniques. A total of 391 experimental datasets were collected and compiled, covering key parameters including bond strength, reinforcing bar diameter, yield strength, concrete cover thickness, concrete compressive strength, mass loss rate due to corrosion, and the presence of stirrups. Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost) algorithms were employed to develop predictive models for bond strength. Model training and testing were performed using 10-fold cross-validation. Furthermore, the SHapley Additive exPlanations (SHAP) approach was introduced to enhance model interpretability and quantitatively assess the influence of each input feature, revealing that mass loss rate and bar diameter are the dominant factors. This study effectively bridges the research gap between high-precision black-box algorithms and the need for physical interpretability in engineering. The results demonstrate that (1) the proposed XGBoost model significantly outperforms traditional empirical formulations, achieving a high coefficient of determination (R² = 0.893) and a much lower coefficient of variation (25.85%) on the testing set, and (2) the SHAP analysis reveals that the machine learning predictions are highly consistent with established physical mechanisms, successfully capturing the negative impact of splitting tensile stresses caused by rust expansion and the positive confinement effect of stirrups. Overall, the proposed models demonstrate superior accuracy, robustness, and generalization capability, providing an effective tool and theoretical basis for evaluating bond behavior and designing durable CRC structures with broad engineering applicability. Full article

There are many factors that affect the shear capacity of FRP (fiber-reinforced polymer)-strengthened reinforced concrete (RC) beams, and traditional capacity models based on empirical or semi-empirical formulas often suffer from insufficient accuracy. To enhance the predictive accuracy and generalization ability of the shear capacity of FRP-strengthened RC beams, this study proposes an interpretable machine learning model based on the Jaya-CNN-LSTM model. A comprehensive database consisting of 315 test data on shear capacity of FRP-strengthened RC beams, encompassing various FRP reinforcement modes, has been established. Key feature parameters for predicting the shear capacity of FRP-strengthened RC beams are selected through Pearson correlation coefficient analysis. Based on the Jaya algorithm, the hyperparameters of the ensemble CNN-LSTM prediction model are adaptively optimized. A comparative analysis is conducted between the proposed method, other machine learning models, and existing empirical formulas to evaluate the proposed model’s efficacy. The results demonstrate that the proposed model outperforms other machine learning models and empirical formulas in terms of prediction accuracy and stability. Furthermore, the machine learning-based predictions align more closely with experimental values than those derived from empirical formulas. Additionally, the SHAP method is utilized to quantify the critical parameters’ impact on predicting the shear capacity of FRP-strengthened RC beams. The results reveal that there is an explicit mapping relationship between key features such as shear-span ratio, concrete strength, and yield strength of stirrups and the shear capacity of FRP-strengthened RC beams, providing technical support for practical applications. Full article

In this paper, a hidden ring beam (HRB) joint suitable for steel-tube-reinforced concrete (ST-RC) composite columns is proposed. The seismic performance was evaluated experimentally by hysteresis loading tests on reinforcement anchorage construction and reinforced concrete (RC) slabs, which was evaluated by several indices to assess the strength, ductility, stiffness degradation and energy dissipation capacity. The results showed that the HRB joints have reliable seismic safety performance. The ultimate failure of all the specimens occurred in the plastic hinge regions of the RC beams. The specimens with different reinforcement anchorage construction methods exhibited excellent anchorage performance, maintaining effective anchorage between beam longitudinal bars and ring bars under cyclic loading. The RC slab increased the joint strength and the initial stiffness, with only a reduction in the ductility coefficient, and the average equivalent viscous damping coefficient reached 0.155. In addition, a joint numerical model was established, and the accuracy was validated against the test results, with the predicted strength differing from the test results by no more than 6%. A parametric analysis using numerical simulations revealed that the ring–longitudinal ratio, bearing stirrup diameter, RC slab constraints and axial load ratio were critical factors influencing the seismic performance of the joints. On the basis of the results of the parametric analysis, a moment capacity calculation method is proposed for HRB joints, providing a practical reference for seismic design in engineering applications. Full article

The calculation for the local bearing capacity of stirrup-confined concrete is an important issue in structural design. Due to the coupling effects of multiple factors, there is no unified calculation method recognized by scholars. The improved backpropagation neural network model based on the particle swarm optimization algorithm (PSO-BPNN) is used in this research to conduct a systematic analysis. The results of 40 stirrup-confined concrete specimens from the tests conducted by ourselves and an additional 92 similar test data points from references were combined; the calculation efficiency and accuracy of the PSO-BPNN model were verified. Compared with the BPNN model, the training iterations of the PSO-BPNN model were reduced by 74.23% with the condition of same training effect. The mean squared error (MSE) is reduced by 33.9%, and the coefficient of determination (R²) is increased by 5.5% with the condition of the same number training iterations. In addition, compared with the calculation stability and accuracy of Random Forest Regression (RFR), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost) models, the PSO-BPNN model also shows better results. Within the applicable range of the codes, the average ratio of the predicted values to the calculated values for GB50010-2010, MC2020 and ACI318-25 are 1.988, 1.719, and 5.387, respectively. A higher evaluation for the contribution of stirrup is considered in the MC2020 code; the predicted values of some specimens are lower than the calculated values when A_cor/A_l is less than 1.35. The brittleness effect is not adequately considered: the predicted values of some specimens are also lower than the calculated values with the active powder concrete (RPC) is used. The sensitivity ranking of the model with coupling effect for parameters is A_l, A_b, f_c,k, s, d, d_cor, and f_y,k. It is slightly different from the sensitivity ranking obtained by analyzing individual parameters, but the calculation logic is consistent. The research results can provide a theoretical basis for practical engineering. Full article

With the increasing application of the Vertical Shaft Machine (VSM) method in ultra-deep shafts, accurate prediction of construction-induced structural stresses is vital for engineering safety. Currently, VSM is predominantly used in soft soils, where structural response analysis still relies on finite element (FE) simulations that are computationally intensive and complex to model. To improve analysis efficiency and understand the structural behavior of VSM shafts in granite composite strata, this study takes the first VSM shaft project in South China—the Guangzhou–Huadu Intercity Railway Shield Shaft—as a case study. A “monitoring-driven, large-sample data, machine learning substitution” framework is proposed for predicting structural stresses during construction. The framework calibrates an FE model using monitoring data. Through full factorial design, key design parameters—including main reinforcement diameter, stirrup diameter, concrete strength grade, and steel plate thickness—are systematically varied. Parametric FE simulations are then conducted to construct large-sample response databases (540 sets for ring 0 and 864 sets for the cutting edge ring). Genetic algorithm is introduced to optimize the hyperparameters of Random Forest, XGBoost, and Neural Network models, and their predictive performances are systematically compared. Results show that the proposed framework effectively substitutes traditional FE analysis and enables rapid multi-parameter comparison. Among the models, GA-XGBoost achieves the highest prediction accuracy across all stress indicators (R² > 0.999, where R² is the coefficient of determination, with values closer to 1 indicating better predictive performance), demonstrating the superiority of its gradient boosting and regularization mechanisms in handling tabular data with strong physical correlations. Moreover, the method exhibits good extensibility to other engineering response predictions beyond construction stresses. Full article