Search Results (152)

The purpose of this parametric study was to develop a numerical simulation model calibrated with experimental data to predict the flexural behavior of prestressed concrete (PSC) girders subjected to long-term prestress losses. The model is capable of accurately simulating the flexural behavior of PSC girders using commercial finite-element (FE) software in the ABAQUS/Explicit program. The accuracy of the model was validated by comparing its results with flexural response test data from three post-tensioned girders, with the tendons ultimately having tensile strength capacities of 1860 MPa, 2160 MPa, and 2400 MPa. The comparison demonstrated generally excellent agreement between numerical and experimental results in terms of the load–deflection response and crack propagation behavior, from the onset of first cracking through the maximum load and into the ductile response range. Subsequently, a parametric study was conducted to evaluate the effects of tendon ultimate strength, amount of long-term prestress loss, grouting defects, degradation-induced reductions in concrete strength, and reductions in tendon cross-sectional area on girder flexural behavior. Through this parametric investigation, the study identified key factors with respect to long-term prestress loss that may influence the flexural behavior of aging PSC structures. Full article

In winter, stay cable sheaths are prone to icing, which increases cable loads and poses a falling-ice hazard upon thawing. While manual and chemical de-icing are common methods, their safety and cost drawbacks make robotic de-icing a promising alternative. Robotic de-icing offers a promising alternative. However, to protect the sheath from damage, the de-icing blade is designed to minimize contact with its surface. Consequently, a thin layer of residual ice is often left behind, which reduces the surface friction coefficient and complicates the climbing process. This study evaluates the climbing performance of a self-manufactured cable-climbing mechanism through laboratory tests and dynamic simulations (ADAMS). A physical prototype was built, and dynamic simulations of the cable-climbing mechanism were conducted using Automated Dynamic Analysis of Mechanical Systems (ADAMS) software. The preliminary validation results demonstrate that the mechanism is capable of maintaining stable climbing under extreme conditions, including a friction coefficient of 0.12 to reflect thin-ice variability and indicated stable climbing even at μ = 0.12), a vertical inclination of 90°, and a load of 12 kg, confirming the design’s validity. Furthermore, we analyzed key parameters. A lower friction coefficient requires a higher clamping force and adversely affects the climbing speed due to increased slip. Similarly, an increased payload elevates the mechanism’s deflection angle, spring force, and wheel torque, which in turn reduces the climbing speed. Cable inclination has a complex effect: deflection decreases with slope, yet clamping force peaks near 70°, showing a bell-shaped trend. This peak requirement dictated the damping spring selection, which was given a safety margin. This ensures safe operation and acceleration at all other angles. Limitations: The present results constitute a feasibility validation under controlled laboratory conditions and rigid-support simulations. The long-term effects of residual ice and field performance remain to be confirmed in planned field trials. Full article

Owing to the light-transmitting, energy-saving, and load-bearing properties of glass, laminated glass has gradually been adopted as vehicle lane surfaces in scenarios such as multi-storey commercial complexes, glass walkways roads, and underground parking lots. However, currently, a mature design system for vehicle-borne glass bridge decks is still lacking, and the existing design system for pedestrian glass bridge decks cannot be directly applied to vehicle-borne scenarios. Combining domestic and international specifications and research, this study focused on material selection, structural configuration, and structural calculation of vehicle-borne glass bridge decks, proposed a targeted design method, and verified it with engineering examples. The key conclusions are as follows: (1) Laminated glass for bridge decks should preferably use homogenized tempered glass with SGP as the interlayer material; the number of glass layers should be controlled between 3 and 5, the aspect ratio of glass panels should be maintained between 1 and 2, the thickness of a single glass panel should not be less than 8 mm, and the interlayer thickness should be between 0.76 mm and 2.28 mm. (2) This study proposes design loads, load combination methods, calculation models, design criteria, and the equivalent thickness calculation method for vehicle-borne glass bridge decks; meanwhile, it incorporates the adverse working condition of single-layer glass breakage into design considerations. (3) The design method shows good agreement with numerical simulation results: both PVB and SGP-laminated glass can meet the load-bearing capacity requirements, but SGP-laminated glass has a larger safety redundancy under the same thickness; after single-layer glass breakage, the bridge deck still has sufficient load-bearing capacity; the calculation results of the design method are slightly more conservative than the finite element calculation results, but the calculation of stress and deflection for SGP-laminated glass is relatively accurate. (4) Future research will further deepen the study on the impact of the long-term performance of laminated glass on the full-life-cycle of vehicle-borne glass bridge decks and improve this design method. Full article

Corrosion concerns motivate the use of alternatives to conventional steel reinforcement in RC beams. This review evaluates fiber-reinforced polymer (FRP) bars and hybrid steel–FRP composite bars (SFCBs) used for durability-critical applications. We conducted a structured literature search focused on 2010–2025 and included seminal pre-2010 studies for context. Experimental studies and code provisions were screened to synthesize evidence on load–deflection response, cracking, and failure, with brief notes on UHPC systems. FRP-RC offers corrosion resistance but limited ductility and an abrupt post-peak response. Steel is ductile and provides warning before failure. SFCB combines durability with steel-core ductility and yields gradual softening and higher energy absorption. Practice should select reinforcement based on stiffness–ductility–durability trade-offs. Current codes only partially cover hybrids. Key gaps include standardized bond–slip and tension-stiffening models for SFCB and robust data on long-term performance under aggressive exposure. Full article

The abnormal tension change in one cable in a cable-stayed bridge indicates cable damage, so it is necessary to obtain the benchmark of the cable tension. After establishing the regression model of the mapping between the temperature-induced cable tension and the bridge temperature field or other data, the regression value can be used as the benchmark. To improve the regression model, the geometric compatibility and mechanical equilibrium must be jointly considered. Therefore, two data groups, which contain the bridge temperature field and the regression values of the temperature-induced deflection of the main girder, are input into the deep learning neural networks. Time lags exist between the temperature features and the temperature-induced cable tension, but are not significant between the temperature-induced deflection and tension. So one neural network module, which receiving the regression values of the temperature-induced deflection, is composed of Convolutional Neural Networks (CNNs). The other neural network module, which receives the temperature features, is composed of stacked CNN and Long Short-Term Memory (LSTM). Finally, several convolution kernels will integrate the array output from the two modules into one regression value of the temperature-induced cable tension. By combining the input data and the composite neural networks, the R² of the regression models of the temperature-induced cable tension is more than 0.95, and the error of the regression values is less than 0.3 kN. In the future, if the nonlinearity at the curve inflection point and the complexity in data distribution could be solved, the stability of the model may be improved. Full article

This study investigates the long-term flexural performance of prestressed concrete double tees under sustained loading. Six full-scale specimens were subjected to a comprehensive experimental program, including a 320-day storage period following prestress release, a short-term flexural test, and a 990-day sustained loading phase. Mid-span deflections were measured using a string-line method, while the effective prestress in tendons was continuously monitored with fiber Bragg grating (FBG) sensors. Results showed a pronounced increase in camber during the storage phase, with long-term camber reaching approximately three times the initial value. Under short-term loading, the slabs exhibited a clear bilinear moment–deflection behavior. During sustained loading, most of the long-term deflection developed in the early stages, and an inverse relationship between load level and deflection growth was observed. Additionally, data from 20 short-term tests were compiled, and a bilinear stiffness model was proposed to estimate flexural stiffness in the cracked state. These findings contribute to a deeper understanding of long-term deformation in prestressed concrete double tees and provide reference data for serviceability evaluation and design refinement. Full article

Given the intricate operating environment of steel truss bridges, data anomalies are frequently initiated by faults in the sensor monitoring system itself during the monitoring process. This paper utilizes a steel truss bridge as a case study in engineering, with a primary focus on the deflection of the main girder. The paper establishes an Attention Mechanism-based Bidirectional Long Short-Term Memory Neural Network (ATT-BiLSTM) model, with the objective of accurately repairing abnormal monitoring data. Firstly, correlation heat maps and Gray correlation are employed to detect anomalies in key measurement point data. Subsequently, the ATT-BiLSTM and Support Vector Machine (SVR) models are established to repair the anomalous monitoring data. Finally, various evaluation indexes, including Pearson’s correlation coefficient, mean squared error, and coefficient of determination, are utilized to validate the repairing accuracy of the ATT-BiLSTM model. The findings indicate that the repair efficacy of ATT-BiLSTM on anomalous data surpasses that of SVR. The repaired data exhibited a tendency to decrease in amplitude at the anomalous position, while maintaining the prominence of the data at abrupt deflection change points, thereby preserving the characteristics of the data. The repair rate of anomalous data attained 93.88%, and the mean square error of the actual complete data was only 0.0226, leading to substantial enhancement in the integrity and reliability of the data. Full article

Trajectory planning is crucial for solar aircraft endurance. The multi-wing morphing solar aircraft can enhance solar energy acquisition through wing deflection, which simultaneously incurs aerodynamic losses, complicating energy coupling and challenging existing planning methods in efficiency and long-term optimization. This study presents an energy-optimal trajectory planning method based on Hierarchical Reinforcement Learning for morphing solar-powered Unmanned Aerial Vehicles (UAVs), exemplified by a Λ-shaped aircraft. This method aims to train a hierarchical policy to autonomously track energy peaks. It features a top-level decision policy selecting appropriate bottom-level policies based on energy factors, which generate control commands such as thrust, attitude angles, and wing deflection angles. Shaped properly by reward functions and training conditions, the hierarchical policy can enable the UAV to adapt to changing flight conditions and achieve autonomous flight with energy maximization. Evaluated through 24 h simulation flights on the summer solstice, the results demonstrate that the hierarchical policy can appropriately switch its bottom-level policies during daytime and generate real-time control commands that satisfy optimal energy power requirements. Compared with the minimum energy consumption benchmark case, the proposed hierarchical policy achieved 0.98 h more of full-charge high-altitude cruise duration and 1.92% more remaining battery energy after 24 h, demonstrating superior energy optimization capabilities. In addition, the strong adaptability of the hierarchical policy to different quarterly dates was demonstrated through generalization ability testing. Full article

Nowadays, Fused Filament Fabrication (FFF) 3D printing offers promising opportunities for the customized manufacturing of ankle–foot orthoses (AFOs) targeted towards rehabilitation purposes. Polypropylene (PP) represents an ideal candidate in orthotic applications due to its light weight and superior mechanical properties, offering an excellent balance between flexibility, chemical resistance, biocompatibility, and long-term durability. However, Additive Manufacturing (AM) of AFOs based on PP remains a major challenge due to its limited bed adhesion and high shrinkage, especially for making large parts such as AFOs. The primary innovation of the present study lies in the optimization of FFF 3D printing parameters for the fabrication of functional, patient-specific orthoses using PP, a material still underutilized in the AM of medical devices. Firstly, a thorough thermomechanical characterization was conducted, allowing the implementation of a (thermo-)elastic material model for the used PP filament. Thereafter, a Taguchi design of experiments (DOE) was established to study the influence of several printing parameters (extrusion temperature, printing speed, layer thickness, infill density, infill pattern, and part orientation) on the mechanical properties of 3D-printed specimens. Three-point bending tests were conducted to evaluate the strength and stiffness of the samples, while additional tensile tests were performed on the 3D-printed orthoses using a home-made innovative device to validate the optimal configurations. The results showed that the maximum flexural modulus of 3D-printed specimens was achieved when the printing speed was around 50 mm/s. The most significant parameter for mechanical performance and reduction in printing time was shown to be infill density, contributing 73.2% to maximum stress and 75.2% to Interlaminar Shear Strength (ILSS). Finally, the applicability of the finite element method (FEM) to simulate the FFF process-induced deflections, part distortion (warpage), and residual stresses in 3D-printed orthoses was investigated using a numerical simulation tool (Digimat-AM^®). The combination of Taguchi DOE with Digimat-AM for polypropylene AFOs highlighted that the 90° orientation appeared to be the most suitable configuration, as it minimizes deformation and von Mises stress, ensuring improved quality and robustness of the printed orthoses. The findings from this study contribute by providing a reliable method for printing PP parts with improved mechanical performance, thereby opening new opportunities for its use in medical-grade additive manufacturing. Full article

Asynchronous-pouring construction (APC) technology employs a suspended hanging basket directly supported by corrugated steel webs (CSWs) with high shear strength, significantly enhancing construction efficiency. To further elucidate the characteristics of APC and promote its application in prestressed concrete (PC) composite box girder bridges with CSWs, this study analyzes the sustainable development of APC from two aspects, including environmental impact and economic performance. Finite element models of APC and traditional balanced cantilever construction (TBCC) were established for the case bridge with a main span of 105 m. The stress distribution and deflection of the main girder in the cantilever construction state are compared with field measurements, and the variations in stress and deflection in typical sections during construction are analyzed. Additionally, a simplified theoretical method is proposed for calculating stress and deflection in PC composite girder bridges during the cantilever construction stage using APC. Results demonstrate that APC demonstrates significant advantages in reducing economic costs and minimizing long-term environmental impacts. Furthermore, this method ensures acceptable stress and deflection throughout construction. The proposed simplified formula for CSW deflection in the maximum segment agrees well with both measured data and finite element results, providing a valuable reference for deflection calculation in APC applications. Full article

As an important part of national transportation infrastructure, the operation status of bridges is directly related to transportation safety and social stability. Structural deflection, which reflects the deformation behavior of bridge systems, serves as a key indicator for identifying stiffness degradation and the progression of localized damage. The accurate modeling and forecasting of deflection are thus essential for effective bridge health monitoring and intelligent maintenance. To address the limitations of traditional methods in handling multi-source data fusion and nonlinear temporal dependencies, this study proposes an enhanced iTransformer-based prediction model, termed LDAiT (LSTM Differential Attention iTransformer), which integrates Long Short-Term Memory (LSTM) networks and a differential attention mechanism for high-fidelity deflection prediction under complex working conditions. Firstly, a multi-source heterogeneous time series dataset is constructed based on wireless sensor network (WSN) technology, enabling the real-time acquisition and fusion of key structural response parameters such as deflection, strain, and temperature across critical bridge sections. Secondly, LDAiT enhances the modeling capability of long-term dependence through the introduction of LSTM and combines with the differential attention mechanism to improve the precision of response to the local dynamic changes in disturbance. Finally, experimental validation is carried out based on the measured data of Xintian Yellow River Bridge, and the results show that LDAiT outperforms the existing mainstream models in the indexes of R², RMSE, MAE, and MAPE and has good accuracy, stability and generalization ability. The proposed approach offers a novel and effective framework for deflection forecasting in complex bridge systems and holds significant potential for practical deployment in structural health monitoring and intelligent decision-making applications. Full article

This study proposes a unified span–depth ratio model aimed at optimizing the minimum thickness of reinforced concrete flat plate slabs, addressing the limitations of the simplified span-to-depth ratio provisions in ACI 318. The existing code does not fully consider critical parameters such as panel aspect ratio, reinforcement ratio, support conditions, concrete strength, and long-term deflections due to creep and shrinkage. To overcome these shortcomings, a generalized analytical model is developed based on fundamental deflection theory, incorporating both immediate and time-dependent behaviors. The model is validated through numerical simulations applied to interior, edge, and corner slab panels subjected to various geometric configurations, loading scenarios, and reinforcement levels. Results from the parametric study indicate that deflection control improves significantly with higher reinforcement ratios and lower aspect ratios, leading to more efficient slab designs. Comparisons with ACI 318 guidelines reveal that the proposed model provides enhanced accuracy, particularly for irregular slab geometries and stringent deflection limits (e.g., L/480). The findings highlight that conventional code-based thickness limits may underestimate slab depth requirements in many practical scenarios. The study advocates for integrating deflection-based considerations into the preliminary design stage, offering structural engineers a more robust and practical tool to ensure serviceability while optimizing material use. Full article

Long-term processes, manifesting themselves in slow geometrical alterations and changes in internal forces, have been known and observed to take place mainly in large bridges made of prestressed concrete, but they also occur, albeit to a smaller degree, in steel bridges. Two sets of data, coming from, respectively, multi-year geodetic surveys and the structural health monitoring of a cable-stayed bridge (forces in its stays), were compared. Using the collocation method, displacements consistent with the results of the geodetic measurements were input into a numerical model of the bridge. Then, changes in the forces in the stays, which should accompany the displacements, were computed. The computed changes were compared with the actual changes in the mean force values in the stays of the bridge recorded over an eight-year period of its structural health monitoring. The two sets of data were found to be not in satisfactory good agreement. The main factors making it difficult to reach full agreement were the very small relative values of the observed geometrical alterations (the deformation, i.e., the increase in deflection, of the 375 m long span amounting merely 10–15 mm after eight years of periodic measurement) and the very small changes (amounting to about 0.5% for 8 years of monitoring) in the mean forces in the stays, as well as the possible mistakes in the survey. Despite these difficulties, the employed collocation method proved to be effective. It was also found that the long-term geometrical alterations and the changes in the forces in the stays do not adversely affect the safety of the bridge and its use. Full article

Background/Objectives: Total hip joint arthroplasty (THA) is a common procedure aimed at alleviating pain and restoring mobility in patients with severe hip joint conditions, particularly osteoarthritis. While the surgery itself is effective, postoperative rehabilitation is crucial for long-term functional recovery. This study investigates the role of muscle deflection as an indicator of rehabilitation quality, focusing on hip movement improvements (abduction, extension, and flexion) over an 8-week recovery period. The objective is to assess whether muscle deflection measurements can serve as reliable metrics for evaluating recovery progress and functional outcomes. Methods: The study involved post-THA patients from Euroclinic Hospital S.A. and Bucharest Emergency Clinical Hospital, who were divided into an experimental group (undergoing a specialized rehabilitation program) and a control group (receiving standard recovery protocols). Measurements of active range of motion (AROM), passive range of motion (PROM), and muscle deflection were taken using a handheld digital dynamometer (ActivForce 2) at different time points (2, 4, 6, and 8 weeks post-surgery). The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) was also used to assess pain, physical difficulties, and joint stiffness. Results: The study found progressive improvements in AROM, PROM, and muscle deflection across all movements over the 8-week period. Conclusions: The findings highlight the importance of structured physiotherapy in post-THA rehabilitation. The use of muscle deflection measurements provides an objective and quantifiable method for tracking patient progress. Future rehabilitation protocols could benefit from incorporating muscle deflection monitoring to tailor recovery programs and optimize patient outcomes. Standardizing rehabilitation exercises, including balance training and resistance exercises, may further enhance recovery and reduce long-term functional impairments. Full article

The monolithic composite action of structures relies on the interface bond strength between concrete and the repair material. This study uses explainable deep learning techniques to evaluate the ultimate strength capacity (Us) of U-shaped normal concrete (NC) strengthened with polyurethane grouting (PUG) materials. Machine learning algorithms (ML) such as Long Short-Term Memory (LSTM), Random Forest (RF), and Wide Neural Network (WNN) models were developed to estimate Us by considering five input parameters: the initial crack strength (Cs), thickness of the grouting materials (T), mid-span deflection (λ), and peak applied load (P). The results indicated that LSTM models, particularly LSTM-M2 and LSTM-M3, demonstrated superior predictive accuracy and consistency in both the calibration and verification phases, as evidenced by high Pearson’s correlation coefficients (PCC = 0.9156 for LSTM-M2) and Willmott indices (WI = 0.7713 for LSTM-M2), and low error metrics (MSE = 0.0017, RMSE = 0.0418). The SHAP (SHapley Additive exPlanations) analysis showed that the thickness of the grouting materials and maximum load were the most significant parameters affecting the ultimate capacity of the composite U-shaped specimen. The RF model showed moderate improvements, with RF-M3 performing better than RF-M1 and RF-M2. The WNN models displayed varied performance, with WNN-M2 performing poorly due to significant scatter and deviation. The findings highlight the potential of LSTM models for the accurate and reliable prediction of the ultimate strength of composite U-shaped specimens. Full article