Search Results (111)

Objectives: Shen-Ling-Bai-Zhu-San (SLBZS) is a classical traditional Chinese herbal formula with spleen-invigorating and dampness-resolving properties. Recent pharmacological studies suggest its potential to regulate immune and metabolic disorders. Type 2 diabetes mellitus (T2D) and ulcerative colitis (UC) often coexist as comorbidities characterized by chronic inflammation, microbial imbalance, and insulin dysregulation, yet effective therapies remain limited. This study aimed to investigate the molecular mechanisms through which SLBZS may benefit T2D–UC comorbidity. Methods: An integrative multi-omics strategy was applied, combining network pharmacology, structural bioinformatics, and ensemble molecular docking–dynamics simulations. These complementary approaches were used to identify SLBZS bioactive compounds, predict their putative targets, and examine their interactions with disease-related biological networks. Results: The analyses revealed that flavonoids in SLBZS act on the SLC6A14/PI3K–AKT signaling axis, thereby modulating immune responses and improving insulin sensitivity. In addition, SLBZS was predicted to regulate the NF-$\kappa$B/MAPK signaling pathways, key hubs linking inflammation and metabolic dysfunction in T2D–UC. These dual actions suggest that SLBZS can intervene in both inflammatory and metabolic processes. Conclusions: SLBZS demonstrates promising therapeutic potential for T2D–UC by targeting interconnected immune–metabolic networks. These findings not only provide mechanistic insights bridging traditional therapeutic concepts with modern pharmacology but also establish a theoretical basis for future experimental validation and clinical application. Full article

Non-contact measurement technology based on computer vision has been recognized as a critical approach in bridge lightweight monitoring due to its low cost and strong environmental adaptability. To address the sub-millimeter accuracy and real-time requirements of bridge displacement monitoring, this study proposes a visual monitoring method that integrates a connected-domain segmentation matching algorithm with an automatic binarization threshold adjustment mechanism. This combination significantly improves adaptability and robustness under complex lighting conditions. Moreover, the method introduces the SRCNN (Super-Resolution Convolutional Neural Network) as a lightweight super-resolution module, the method achieves a better balance between computational efficiency and measurement precision. The proposed method was validated through model testing and successfully applied to real-bridge displacement monitoring and structural damping ratio identification. These findings demonstrate the practical potential of the method as a reliable reference for static and dynamic performance evaluation and condition assessment of bridges. Full article

This study examines research trends and identifies key gaps relevant to the field of structural safety and resilience; additionally, a systematic literature review (SLR) guided by the PRISMA methodology was conducted, analyzing 4188 documents ranging from 1975 to 2025. The research revealed key trends, including a focus on various aspects of the structural stability and resilience of buildings affected by earthquakes through analysis of various innovative methods and materials. The present study encompasses work describing the use of steel–wood composite columns to improve building stability, assessment of the impact of wood accumulation on bridges during floods, and the effect of debris flow on the stability of check dams. In addition, this study also evaluates the seismic performance of school buildings in Mexico, a method of diagnosing cracks in concrete dams, and the application of recycled materials from old tires for seismic disaster mitigation. Acoustic emission monitoring methods in medieval towers and the design of seismic isolation systems with variable damping are also discussed. Bibliometric analysis highlighted increased collaboration and a thematic shift towards green and data-driven approaches. However, significant gaps were identified. The findings explain that the use of innovative materials and methods can improve the stability and resistance of building structures with respect to dynamic loads, such as those associated with earthquakes and floods. The findings provide guidance for the design and maintenance of safer and more sustainable infrastructure in the future. Full article

The application of tuned mass dampers (TMDs) to high-rise buildings or slender bridges can significantly decrease the dynamical vibrations due to external excitation, such as wind or earthquake loads. However, the individual properties of a TMD such as mass, stiffness and damping have to be designed carefully with respect to the dynamical properties of the investigated structure. In real-world structures, the influence of uncertain system properties might be critical for the performance of a TMD and thus the whole structure. Therefore, the design under uncertainty of such systems is an important issue, which is addressed in the current paper. For our investigations, we consider linear single-degree-of-freedom (SDOF) systems, where analytical formulas for the deterministic design already exist, and linear multi-degree-of-freedom (MDOF) systems, where a time integration and numerical optimization algorithms are usually applied to obtain the optimal TMD parameters. If the numerical optimization should be coupled with a sampling-based uncertainty quantification method, such as Monte Carlo sampling, the design procedure would require the evaluation of a coupled double-loop approach, which is very demanding from the computation point of view. Therefore, we focus the following paper on an efficient analytical uncertainty quantification approach, which estimates the mean and scatter from a Taylor series expansion. Additionally, we introduce an efficient mode decomposition approach for MDOF systems with multiple TMDs, which estimates the maximum displacements using a modal analysis instead of a demanding time integration. Different optimal design problems are formulated as single- or multi-objective optimization tasks, where the statistical properties of the maximum displacements are considered as safety margins in the optimization goal functions. The application of numerical optimization algorithms is straightforward and not limited to specific algorithms. As numerical examples, we investigate an SDOF system with single TMD and a multi-story frame with multiple TMDs. The presented procedure might be interesting for the design process of structures, where the dynamical vibrations reach a critical threshold. Full article

In the field of structural engineering, accurate identification of modal damping ratio is the key to structural dynamic response analysis. In order to accurately identify the modal damping ratio of the structure, this study proposes a method to identify the first-order modal damping ratio of the structure by analyzing the free attenuation response of the acceleration signal. By intercepting the free attenuation section from the structural dynamic response output, the amplitude is extracted, and the logarithmic estimation slope of the amplitude is fitted by the least square method to establish a theoretical model for identifying the first-order modal damping ratio. The results show that the method has high accuracy and good stability when the modal damping ratio is in the range of 0.00500~0.06400, and different nodes have little effect on the accuracy of identification. When the modal damping ratio is in the range of 0.06400~0.07000, the accuracy of the method is relatively low and the stability is relatively poor, but it is still within the acceptable range. When the damping ratio is greater than 0.07000 or less than 0.00500, the accuracy may be reduced. In order to further verify the effectiveness of the method, it is applied to the damping identification of a steel arch bridge project. The dynamic response of the bridge under random excitation and El Centro seismic wave excitation is analyzed by using the recommended value and identification value of the first-order damping ratio. The results show that the method can accurately and reliably identify the first-order modal damping ratio, which is significantly different from the empirical modal damping ratio. The identified modal damping ratio can more accurately describe the dynamic response of the structure after long-term use, while the recommended value is not applicable. This method can be applied to the modal damping ratio identification of other structural types, which reflects that the modal damping ratio identification method proposed in this study has certain engineering significance. It is worth noting that the accuracy of identification will be reduced when the modal damping ratio is less than 0.00500 or more than 0.07000, and it may not even be applicable if the modal damping ratio is too small or too large. This method has higher requirements for acceleration signals. In engineering, it may be affected by noise and other factors, resulting in reduced identification accuracy. In practical engineering, it is necessary to improve the identification accuracy of first-order modal damping ratio by changing the interception point of the free attenuation section of the acceleration signal and the screening of the amplitude. Full article

To address the adverse effects of Tuned Inertia Dampers (TIDs) on track slab vibrations while controlling high-frequency rail vibrations, a hybrid Finite Element-Statistical Energy Analysis (FE-SEA) method is developed for modeling the vehicle-track-bridge coupled system. Short-wavelength track irregularities are introduced as high-frequency excitation, and the accuracy and efficiency of this method are validated by comparison with the traditional finite element method (FEM). A vibration control model for track-bridge structures incorporating TIDs is designed, and the effects of the TID’s inertance, stiffness, and damping coefficients on the vertical acceleration responses of the rail and track slab are investigated in detail. The study reveals that although TIDs effectively reduce rail vibrations, they may induce adverse effects on track slab vibrations. Using the vibration acceleration amplitudes of both the rail and track slab as dual control objectives, a multi-objective optimization model is established, and the TID’s optimal parameters are determined using a multi-objective genetic algorithm. The results show that the optimized TID parameters reduce rail acceleration amplitudes by 16.43% and improve the control efficiency by 12.45%, while also addressing the negative effects on track slab vibration. The track slab’s vibration acceleration is reduced by 5.47%, and the vertical displacement and acceleration of the vehicle body are reduced by 14.22% and 47.5%, respectively, thereby enhancing passenger comfort. This study provides new insights and theoretical guidance for vibration control analysis in vehicle-track-bridge coupled systems. Full article

Variations in damper configuration strategies have a direct impact on the seismic damage patterns of long-span deck-type concrete-filled steel tube (CFST) arch bridges. This study developed an analysis and evaluation framework to identify the damage category, state, and progression sequence of structural components. The framework aims to investigate the influence of viscous dampers on the seismic response and damage patterns of long-span deck-type CFST arch bridges under near-fault pulse-like ground motions. The effects of different viscous damper configuration strategies and design parameters on seismic responses of long-span deck-type CFST arch bridges were systematically investigated, and the preferred configuration and parameter set were identified. The influence of preferred viscous damper configurations on seismic damage patterns of long-span deck-type CFST arch bridges was systematically analyzed through the established analysis and evaluation frameworks. The results indicate that a relatively optimal reduction in bridge response can be achieved when viscous dampers are simultaneously installed at both the abutments and the approach piers. Minimum seismic responses were attained at a damping exponent α = 0.2 and damping coefficient C = 6000 kN/(m/s), demonstrating stability in mitigating vibration effects on arch rings and bearings. In the absence of damper implementation, the lower chord arch foot section is most likely to experience in-plane bending failure. The piers, influenced by the coupling effect between the spandrel construction and the main arch ring, are more susceptible to damage as their height decreases. Additionally, the end bearings are more prone to failure compared to the central-span bearings. Implementation of the preferred damper configuration strategy maintains essentially consistent sequences in seismic-induced damage patterns of the bridge, but the peak ground motion intensity causing damage to the main arch and spandrel structure is significantly increased. This strategy enhances the damage-initiation peak ground acceleration (PGA) for critical sections of the main arch, while concurrently reducing transverse and longitudinal bending moments in pier column sections. The proposed integrated analysis and evaluation framework has been validated for its applicability in capturing the seismic damage patterns of long-span deck-type CFST arch bridges. Full article

This study aims to develop an integrated wireless monitoring system named MANTiS-32, which leverages an open-source platform to enable autonomous modular operation, high-speed large-volume data transmission via Wi-Fi, and the integration of multiple complex sensors. The MANTiS-32 system is composed of ESP32-based MANTiS-32 hubs connected to eight MPU-6050 sensors each via RS485. Four MANTiS-32 hubs transmit data to a main PC through an access point (AP), making the system suitable for real-time monitoring of modal information necessary for structural performance evaluation. The fundamental performance of the developed MANTiS-32 system was validated to demonstrate its effectiveness. The evaluation included assessments of acceleration and frequency response measurement performance, wireless communication capabilities, and real-time data acquisition between the MANTiS-32 hub and the eight connected MPU-6050 sensors. To assess the feasibility of using MANTiS-32 for performance monitoring, a flexible model cable-stayed bridge, representing a mid- to long-span bridge, was designed. The system’s ability to perform real-time monitoring of the dynamic characteristics of the bridge model was confirmed. A total of 26 MPU-6050 sensors were distributed across four MANTiS-32 hubs, and real-time data acquisition was successfully achieved through an AP (ipTIME A3004T) without any bottleneck or synchronization issues between the hubs. Vibration data collected from the model bridge were analyzed in real time to extract dynamic characteristics, such as natural frequencies, mode shapes, and damping ratios. The extracted dynamic characteristics showed a measurement error of less than approximately 1.6%, validating the high-precision performance of the MANTiS-32 wireless monitoring system for real-time structural performance evaluation. Full article

Magnetorheological (MR) foams represent a class of smart materials with unique tunable viscoelastic properties when subjected to external magnetic fields. Combining porous structures with embedded magnetic particles, these materials address challenges such as leakage and sedimentation, typically encountered in conventional MR fluids while offering advantages like lightweight design, acoustic absorption, high energy harvesting capability, and tailored mechanical responses. Despite their potential, challenges such as non-uniform particle dispersion, limited durability under cyclic loads, and suboptimal magneto-mechanical coupling continue to hinder their broader adoption. This review systematically addresses these issues by evaluating the synthesis methods (ex situ vs. in situ), microstructural design strategies, and the role of magnetic particle alignment under varying curing conditions. Special attention is given to the influence of material composition—including matrix types, magnetic fillers, and additives—on the mechanical and magnetorheological behaviors. While the primary focus of this review is on MR foams, relevant studies on MR elastomers, which share fundamental principles, are also considered to provide a broader context. Recent advancements are also discussed, including the growing use of artificial intelligence (AI) to predict the rheological and magneto-mechanical behavior of MR materials, model complex device responses, and optimize material composition and processing conditions. AI applications in MR systems range from estimating shear stress, viscosity, and storage/loss moduli to analyzing nonlinear hysteresis, magnetostriction, and mixed-mode loading behavior. These data-driven approaches offer powerful new capabilities for material design and performance optimization, helping overcome long-standing limitations in conventional modeling techniques. Despite significant progress in MR foams, several challenges remain to be addressed, including achieving uniform particle dispersion, enhancing viscoelastic performance (storage modulus and MR effect), and improving durability under cyclic loading. Addressing these issues is essential for unlocking the full potential of MR foams in demanding applications where consistent performance, mechanical reliability, and long-term stability are crucial for safety, effectiveness, and operational longevity. By bridging experimental methods, theoretical modeling, and AI-driven design, this work identifies pathways toward enhancing the functionality and reliability of MR foams for applications in vibration damping, energy harvesting, biomedical devices, and soft robotics. Full article

This study investigates noise detection and damping-based noise mitigation strategies for cavity structures, with a specific focus on addressing noise issues in the conveyor trough of combine harvesters. Despite its practical significance, research on the noise generation mechanisms, transmission paths, and control measures for conveyor troughs remains limited, particularly under varying operational conditions. To bridge this gap, this work integrates experimental measurements with numerical simulations to systematically analyze and optimize the noise reduction performance of the conveyor trough. Noise measurements were conducted using the sound intensity method, revealing sound pressure levels in the range of 93–95 dB. Frequency spectrum analysis identified key noise sources and dominant frequency components. Finite element analysis (FEA) and vibration modal testing were performed to uncover critical noise-inducing factors, including chain meshing impacts and structural resonances. Based on these findings, a damping optimization strategy was proposed by incorporating constrained damping layers to attenuate vibration and reduce noise in targeted frequency bands. The effectiveness of this approach was validated through multiple coherence analysis, which confirmed significant suppression of structural vibration noise in the 0–500 Hz range, while experimental results showed that the optimized conveyor trough structure achieved a maximum reduction of 0.4071 dB in continuous equivalent A-weighted sound pressure under load conditions. This research provides a comprehensive methodology for noise control and structural optimization of conveyor trough systems, offering valuable theoretical and practical insights for enhancing the operational comfort and environmental performance of combine harvesters. Full article

Titanium alloy thin-walled components are extensively used in aerospace engineering, yet their milling stability remains a persistent challenge due to vibration-induced surface anomalies. This study develops an advanced dynamic model for the face milling of titanium alloy thin-walled structures, systematically integrating axial cutting dynamics with regenerative chatter mechanisms and nonlinear process damping phenomena. The proposed framework crucially accounts for time-varying tool–workpiece interactions and damping characteristics, enabling precise characterization of stability transitions under dynamically varying axial immersion conditions. A novel extension of the semi-discretization method is implemented to resolve multi-parameter stability solutions, establishing a computational paradigm for generating three-dimensional stability lobe diagrams (3D SLDs) that concurrently evaluate spindle speed, cutting position, and the axial depth of a cut. Comprehensive experimental validation through time-domain chatter tests demonstrates remarkable consistency between theoretical predictions and empirical chatter thresholds. The results reveal that process damping significantly suppresses chatter at low spindle speeds, while regenerative effects dominate instability at higher speeds. This work provides a systematic framework for optimizing machining parameters in thin-walled component manufacturing, offering improved accuracy in stability prediction compared to traditional two-dimensional SLD methods. The proposed methodology bridges the gap between theoretical dynamics and industrial applications, facilitating efficient high-precision machining of titanium alloys. Full article

Double-column self-centering segmentally assembled bridges (SC-SABs) present greater design complexity compared to single-column systems, primarily due to vertical stiffness discontinuities at segmental spandrel abutments, which critically affect the refinement of their seismic design methods. To address these challenges, this study conducts a systematic investigation into the mechanical behavior and seismic performance of double-column SC-SAB. First, leveraging fundamental mechanical principles and stress-strain relationships, the coupling mechanism between the two columns is analytically established. An analytical expression for the elastic stiffness of a double-column SC-SAB, when simplified to an equivalent single-column system, is derived. This establishes the equivalent stiffness conditions for reducing a double-column system to a single-column model, and the overall equivalent stiffness of the double-column system is formulated. To validate the theoretical framework, a finite element model of the double-column SC-SAB is developed using OpenSees (1.0.0.1 version). An equivalent single-column model is constructed based on the derived stiffness equivalence conditions. By comparing the peak displacement and bearing capacity between the double-column and equivalent single-column models, the accuracy and feasibility of the simplification approach are confirmed. The numerical results further validate the derived overall equivalent stiffness, providing a robust theoretical foundation for simplified engineering applications. Additionally, pushover analysis and hysteretic response analysis are performed to systematically evaluate the influence of key design parameters on the seismic performance of double-column SC-SAB. The results demonstrate that the prestressed twin-column system exhibits excellent self-centering capability, effectively controlling residual displacements, aligning with seismic resilience goals. This research advances the seismic design methodology for SC-SAB by resolving critical challenges in stiffness equivalence and joint behavior quantification. The findings of this study can be utilized to derive equivalent damping ratios and equivalent periods. Based on the displacement response spectrum, the pier-top displacement and maximum force can be determined, thereby enabling a displacement-based seismic design approach. This research holds significant theoretical and practical value for advancing seismic design methodologies for self-centering segmental bridge piers and enhancing the seismic safety of bridge structures. Full article

Enhancing the damping capacity of concrete structures is crucial for improving their resilience under dynamic loading conditions such as earthquakes, vehicular impacts, and industrial vibrations. This study presents a comprehensive review of how material properties—specifically fiber reinforcement, ductility, and toughness—affect the damping behavior of concrete. Various types of fiber reinforcements, including steel, polypropylene, and glass fibers, are analyzed in terms of their contribution to energy dissipation mechanisms such as crack bridging, fiber pullout, and frictional sliding. The role of the ductility index and toughness in augmenting the damping ratio is also discussed, demonstrating that higher ductility and toughness directly correlate with enhanced energy dissipation. Furthermore, the interrelationships between material properties and structural performance under cyclic loading are critically evaluated. The findings highlight that optimizing fiber content and improving the mechanical properties of concrete can significantly increase its damping capacity, thereby offering strategic pathways for designing safer and more durable infrastructure, especially in seismic-prone regions. This review aims to consolidate the current understanding and provide recommendations for future research focused on developing high-damping concrete composites. Full article

This study presents an innovative approach to processing vibration signals in bridge structures, with a focus on enhancing the accuracy of dynamic response measurements and structural health assessments. It addresses key challenges in signal processing, particularly the uncertainties in selecting filtering parameters for isolating dynamic components from static displacements. A novel method for adaptive filter parameter selection is proposed, which considers variations in resonant frequencies and the non-linearity of quasi-static displacements caused by moving loads. This approach significantly reduces errors in determining forced and natural vibration parameters, leading to more accurate assessments of the bridge’s mechanical characteristics. The study introduces an optimized algorithm for processing acceleration and velocity signals, improving the resolution of natural frequency identification. This method combines traditional Fast Fourier Transform (FFT) techniques with an innovative spectral analysis approach, enabling precise identification of resonant frequencies and damping coefficients. A comprehensive evaluation framework is developed, integrating vibration amplitude, frequency, and damping ratio analyses. This framework enhances structural health assessments, improving the detection and characterization of potential defects and changes in load-bearing capacity. The practical significance of this research lies in its real-world application to bridge diagnostics. The study provides guidelines for sensor selection and configuration, adapted for various bridge types and sizes. The proposed methods demonstrate notable improvements in dynamic coefficient determination and overall structural assessments, offering the potential to reduce maintenance costs and enhance bridge safety. Full article

Ground-based interferometric radar (GBIR) is a powerful remote sensing technique used for infrastructure monitoring, particularly in the field of bridge structural health monitoring (SHM). Despite its high resolution and rapid data acquisition and the availability of various commercial systems, GBIR has not yet been fully recognised or routinely adopted in standard bridge monitoring practices. This study presents a comprehensive review of GBIR technologies and methods historically applied in bridge SHM. A total of 104 peer-reviewed papers were selected through a systematic review process, encompassing 128 monitored bridges assessed using a wide range of GBIR systems. The applications of GBIR across different bridge materials and operational conditions are discussed in detail. The review shows that 76% of GBIR applications focus on roadway and railway bridges. In terms of materials, steel and concrete bridges dominate the dataset, accounting for 95% of the total, while masonry bridges represent only 5%. The GBIR system types examined in this study are categorised into six main groups based on their technical specifications, with their key characteristics and capabilities analysed. The review also investigates bridge feature extraction techniques, revealing a predominant focus on identifying natural frequencies, while fewer studies explore the extraction of damping ratios and structural mode shapes. Furthermore, the integration of GBIR with other sensing technologies—particularly accelerometers—is explored, highlighting opportunities for complementary sensor fusion. Overall, this study provides a comprehensive overview of the current state of practice and identifies key areas for future research and technological development. Full article