Search Results (143)

The prestressed anchor cable is widely used in foundation pit engineering, but its universal bending shape in the anchorage section will significantly affect the load transfer and stress distribution. Based on Cox’s shear-lag model, this paper presents a theoretical analysis of the load transfer behavior at the anchor cable–grout interface and establishes an elastic distribution model of axial force and shear stress that accounts for the anchor cable shape. Furthermore, the influence of cable shape on the elastic stress distribution in the anchorage section under different load conditions, different anchorage lengths, and different bending radii is compared and analyzed. Finally, through a comparative analysis between the model calculation results and experimental data, the proposed distribution model shows good agreement with the experimental results. The findings reveal the evolution of the elastic stress distribution in the anchorage section under different cable shapes and provide a theoretical reference for the axial force loss of prestressed anchor cables in service. Full article

Flexural stiffness identification of prestressed concrete beams plays an important role in evaluating the mechanical performance and damage condition of bridge structures and has become a critical research direction in bridge health monitoring. Accordingly, this paper presented a Physics-Informed Neural Network (PINN)-based method for flexural stiffness identification. In the physical modeling framework, point source forces in the beam-column equation (BCE) were represented by approximating the Dirac delta function with Gaussian functions. This strategy alleviated the convergence issue of the loss function caused by singular behavior and enabled the formulation of a unified governing equation for multi-point loading scenarios. To eliminate the long-term deflection caused by non-load-related factors and self-weight, the BCE was expressed in incremental form. The resulting nondimensional equation was adopted as the target constraint for PINN training to alleviate multi-scale challenges. Furthermore, the residual-based adaptive refinement (RAR) strategy was incorporated during network training to improve computational efficiency and identification accuracy. The proposed method was validated through nine numerical cases without linear relationships and three experimental cases. The results indicate that, even with limited measurement data and under the tested noise levels, the proposed framework can achieve satisfactory flexural stiffness identification under the tested loading conditions. This suggests that the proposed method has promising potential for flexural stiffness identification and may be useful in bridge structural health monitoring under sparse-data conditions. Full article

Camber in high-speed railway prestressed concrete (PC) girders increases with service time and affects profile control, ride comfort, and durability; reliable long-term midspan camber prediction is therefore required. Building on established hybrid physics–data modeling and discrepancy-correction ideas, we present a monitoring-oriented two-layer strategy for long-term camber prediction. In the physics layer, a physics-informed neural network (PINN) is formulated in a quasi-static, stage-aware manner to capture the physics-consistent low-frequency trend governed by creep, shrinkage, prestress loss, and staged loading. In the data layer, an XGBoost model learns a bounded, measurement-level residual correction from monitoring features to account for additional effects not explicitly represented in the physics layer, without altering the underlying physics-driven trend. The approach is evaluated using monitoring data from five 1:4 scaled specimens of a 24 m post-tensioned simply supported box girder and is compared against a theoretical calculation and a standalone PINN. Across prediction stages and specimens, the proposed strategy reproduces the measured camber evolution more closely than the benchmarks while preserving physically plausible trend behavior and yielding more consistent errors among girders. These results indicate that, under the present scaled-specimen and independently calibrated setting, a stage-aware physics baseline combined with bounded residual correction can provide closer agreement with the observed camber evolution than the benchmark models under sparse-monitoring conditions. Its engineering applicability can be repeatedly demonstrated across girders with different construction-condition combinations after girder-wise calibration. Full article

Early-age cracking is a common issue in the prefabrication of large-scale box girders, and the application of pre-tensioning techniques to introduce pre-compressive stress is an effective measure to mitigate such cracking. To determine an optimal pre-tensioning scheme for the 60 m large-scale box girder used in the Ningbo–Xiangshan intercity railway, friction coefficient tests and field stress monitoring were conducted. A numerical model simulating the pre-tensioning process of the box girder, accounting for the constraint of the steel formwork, was developed using Abaqus 2021. Based on the validated finite element model, a parametric study was performed to investigate the effects of friction coefficient, internal formwork roof, and prestressing tendon arrangement on the pre-compressive stress. The results indicate that the bond force between cast-in-place concrete and steel formwork is approximately 2.1 times the sliding friction force. As the friction coefficient increases, the pre-compressive stress in the box girder exhibits a notable decreasing trend. For the critical midspan section S40, the inclusion of frictional effects results in a more uniform distribution of pre-compressive stress. Compared to the case without the internal formwork roof, its inclusion leads to a 9.2% to 10.4% reduction in pre-compressive stress at section S40. To mitigate prestress losses transmitted from the ends to the midspan section, it is recommended that the internal formwork be completely removed prior to prestressing tensioning. The pre-compressive stress in the box girder varies considerably with different prestressing combinations. The comparative analysis of different prestressing combinations reveals substantial variations in pre-compressive stress distribution. After evaluating multiple schemes, the optimal pre-tensioning sequence for the 60-m railway box girder is determined as follows: sequentially tensioning tendon groups F1-2, F1-4, F1-5, F1-6, and B2-3, with an anchorage stress controlled at 558 MPa. This scheme ensures that all critical sections of the box girder remain in a pre-compressive state. In particular, the pre-compressive stress at the key midspan section S40 ranges from 1.12 to 1.26 MPa, achieving the desired effect and effectively suppressing early-age cracking in the large-scale box girder concrete. Full article

Ultra-High-Performance Concrete (UHPC) is increasingly used in slender and prestressed structural members due to its superior strength and durability. However, inaccurate or incomplete prediction of creep deformation may lead to excessive long-term deflection, prestress loss, cracking, and potential serviceability or safety risks in buildings and infrastructure. Therefore, reliable prediction methods for UHPC creep are essential for both structural design and long-term performance assessment. In this study, a database containing 60 literature-derived UHPC creep records was compiled to investigate the creep coefficient at approximately 100 days. Pearson correlation analysis revealed strong interdependence among predictors and weak single-variable linear relationships, indicating that creep behavior is governed by nonlinear interactions. A feedforward backpropagation neural network (BPNN) trained using the Levenberg–Marquardt algorithm was developed to predict the creep coefficient. To maintain engineering interpretability, the fib Model Code 2020 (MC2020) formulation was adopted as a code-based benchmark and further calibrated using ridge regression. Results show that the calibrated MC2020 model improves prediction consistency, while the BPNN model provides the highest predictive accuracy. The proposed framework integrates machine-learning prediction with interpretable code-based calibration, contributing to the development of creep modeling approaches for UHPC and providing practical support for the safe design of UHPC structures. 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

Given that freeze–thaw damage of prestressed concrete significantly threatens structural service life and that existing conventional simulation techniques fail to capture prestress time series, this paper proposes a deep learning prediction model based on the Transformer model. The model integrates a multi-head self-attention mechanism and positional encoding to effectively capture long-range dependencies in prestressed time series. It enhances temporal modeling capability through a 128-dimensional high-dimensional feature space (chosen to balance representation capacity and computational efficiency for the dataset scale) and a 4-layer encoder stacking structure. A dataset was constructed using time-series data from three prestressed concrete components subjected to 50 freeze–thaw cycles. The F-a component was used as the training set, while F-b and F-c served as the testing sets. During the training phase, a Noam learning rate scheduler, gradient clipping, and an early stopping strategy were employed. The results indicate that the training strategy enables the loss function to converge quickly without overfitting, demonstrating good generalization performance. The prediction model performs well on the F-a and F-c datasets, with determination coefficients (R²) of 0.8404 and 0.8425, and corresponding Mean Absolute Error (MAE) of 61.71 MPa and 57.41 MPa, respectively. It can accurately track the periodic variation trend of prestress, demonstrating the model’s effectiveness in prestress prediction. This model provides a new technical tool for the health monitoring and performance prediction of prestressed concrete structures in freeze–thaw environments. 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

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

The fire performance of existing reinforced concrete (RC) bridge decks strengthened by external prestressing systems is investigated, with particular attention to the vulnerability of externally applied tendons under realistic fire scenarios. Fire exposure represents a critical condition for such retrofitted structures, as the structural response is strongly influenced by load level, prestressing effectiveness, and thermal degradation of the strengthening system. A comprehensive assessment framework is proposed, combining thermal and mechanical analyses applied to representative highway overpass bridges. The thermal input adopted for the analyses is first validated through computational fluid dynamics (CFD) simulations, aimed at evaluating temperature development in typical RC beam–girder grillage decks subjected to fire from below. The CFD study considers variations in clearance height and span length and confirms that, in the case of hydrocarbon tanker accidents with fuel spilled on the roadway, conventional fire curves commonly adopted in the literature provide a reliable and conservative representation of both the temperature levels reached and their rate of increase within structural elements, thus supporting their use for rapid and simplified assessments. The validated thermal input is then employed in an analytical fire safety procedure applied to several realistic bridge case-studies. A parametric investigation is carried out by varying deck geometry, span length, reinforcement layout, and the presence of external prestressing retrofit, allowing the evaluation of the reduction in bending capacity and the time-dependent degradation of mechanical properties under fire exposure. The results highlight the critical role of external prestressing in fire scenarios, showing that significant loss of prestressing effectiveness may occur within the first minutes of fire, potentially leading to critical conditions even at service load levels. Finally, a multi-hazard assessment is performed by combining fire effects with pre-existing aging-related deterioration, such as reinforcement corrosion and long-term prestressing losses, demonstrating a marked increase in failure risk and, in the most severe cases, the possibility of premature collapse under dead loads. Full article

Steel and prestressed concrete traction poles can be fixed to reinforced concrete pile foundations using typical bolted connections. The stainless steel fastening screw is connected to the ordinary steel foundation pile reinforcement by friction welding under specific friction welding process parameters. From the perspective of the structural strength of the connection between the traction pole and the foundation pile, regarding the transfer of tensile and shear forces through a single anchor bolt, the yield strength of stainless steel bolts should be R_e,min ≥ 345 MPa for M30 anchors, R_e,min ≥ 310 MPa for M36 anchors and R_e,min ≥ 300 MPa for M42 anchors. This requirement is reliably met by martensitic stainless steels, while other stainless steels have yield strengths below the required minimum. What truly determines the foundation pile’s load capacity is not the satisfactory mechanical strength of the stainless steel (here, the parameters are met), but the quality of the friction-welded end connection between the reinforcement and the threaded bars. Incorrect selection of the type of prestressing steel in the analyzed connection can have enormous consequences for foundation pile manufacturers. Annual production of foundation piles amounts to thousands of units, and an incorrect decision made by the pile designer at the design stage can result in significant financial losses and a high risk to human life. This article presents the results of studies on friction-welded connections of M30, M36, and M42 threaded bars made of austenitic 1.4301 (AISI 304) and martensitic 1.4021 (AISI 420) stainless steel with B500SP reinforcement bars. The tests yielded negative results for 1.4021 (AISI 420) steel, despite its yield strength exceeding R_e ≥ 360 MPa. Full article

Although PINNs have demonstrated strong predictive capabilities in forward problems, their performance in inverse problems remains inadequate, largely due to unquantifiable noise encountered during the multi-parameter identification of prestressed concrete beams. Experimental measurements are often noisy, sparse, or asymmetric, while numerical or analytical models, although physically consistent, are typically approximate and lack regularization from well-defined multi-fidelity data. To address this limitation, this paper proposed a multi-fidelity data and prior-enhanced physics-informed neural network (MF-rPINN), which integrates multi-fidelity data with physics prior relational constraints to guide parameter identification using only sparse experimental observations. The MF-rPINN architecture is designed to enforce consistency between each training iteration and a prescribed set of experimental measurements, while embedding the second-order displacement function into the loss function. Experimental results demonstrate that the proposed MF-rPINN achieves accurate parameter identification even under noisy and incomplete observations, owing to the combined regularization effects of governing physical laws and the second-order displacement prior. The minimum relative errors of the elastic modulus are −6.49% and −9.32% under different and identical loading conditions, respectively, while the minimum relative errors of the prestress force are 0.65% and 4.51%. Compared with classical analytical approaches, MF-rPINN exhibits superior robustness and is capable of predicting continuous displacement fields of prestressed concrete beams while simultaneously identifying prestress force and elastic modulus. These advantages highlight the potential of MF-rPINN as a reliable surrogate modeling tool for practical engineering applications. Full article

The study presents a comprehensive analysis of the effects of high temperatures (500 °C and 700 °C) on the microstructure, mechanical properties, and acoustic emission (AE) parameters of cold-drawn prestressing steel. The investigations included mechanical testing, AE signal acquisition, and numerical verification using the finite element method (FEM). It was demonstrated that increasing temperature leads to significant microstructural changes (pearlite spheroidisation, carbide coarsening), resulting in strength degradation and a shift in the failure mechanism from quasi-brittle (initial state) to transitional (500 °C), and finally to ductile (700 °C). For the first time, AE parameters (Counts to Peak and RA-value) were correlated with local axial strains ε₂₂ and von Mises equivalent stress, enabling the identification of the moment of onset load-bearing capacity loss and the determination of critical material damage thresholds. A multi-criteria diagnostic indicator was proposed to assess the condition of prestressing steel after fire exposure. The results confirm the high potential of AE as a non-invasive tool for evaluating the safety of prestressing tendons and cables in reinforced concrete structures subjected to overheating or fire. Full article

Prestressed concrete (PSC) beams are widely used in critical infrastructure but are highly susceptible to corrosion in aggressive environments, which can significantly compromise their structural performance. Accurate prediction of residual flexural capacity is crucial for evaluating the safety and serviceability of corroded PSC elements. This study presents a simplified empirical model for estimating the residual flexural strength of corroded PSC beams, with maximum strand mass loss serving as the key governing parameter. This approach is superior to existing models, which typically rely on average mass loss and fail to capture the localized stress concentrations induced by pitting corrosion. The model was developed from an experimental dataset of 31 corroded beams, covering maximum mass loss up to 90% using exponential regression. An extensive database of 124 test results from 19 independent studies was used for the purpose of verification and comparison with existing models. The proposed model was capable of effectively capturing the degradation trend and providing accurate and conservative predictions, with an average experimental-to-calculated capacity ratio of 1.04 and a coefficient of variation of 8.7%. Its direct reliance on maximum mass loss, which can be indirectly inferred from corrosion-induced crack widths, significantly enhances the practicality and safety of the model for field condition assessments and life-cycle management of PSC structures. Full article

Rising global temperatures pose a significant threat to agricultural productivity, making the early detection of plant stress crucial for minimizing crop losses. While hyperspectral remote sensing is a powerful tool for monitoring plant health, the precise spectral regions and most effective machine learning models for detecting thermal stress remain an open research question. This study presents a robust framework that utilizes eight state-of-the-art machine learning algorithms to classify the reflectance response of thermal-induced stress in two cultivars of bean plants. Our controlled experiment measured hyperspectral data across two growth stages and three stress conditions (pre-stress, during stress, and post-stress) using a spectroradiometer. The results demonstrate the high performance of several algorithms, with the Artificial Neural Network (ANN) achieving an impressive 99.4% overall accuracy. A key contribution of this work is the identification of the most contributory spectral ranges for thermal stress discrimination: the green region (530–570 nm) and the red-edge region (700–710 nm). This framework is a feasible and effective tool for modelling the hyperspectral response of thermal-stressed bean plants and provides critical guidance for future research on stress-specific spectral indices. Full article