Structural Control & Health Monitoring最新文献

With increasing performance demand in modern cable-stayed bridges towards long-span, light-weight, heavy-load, and extreme-condition, the associated vibrations have become such problematic that may significantly confine the performance of the cable-stayed bridge systems and even may lead to the failure of the systems. Specifically, the growing span of the cable-stayed bridges may increase the risk of larger and potentially more destructive nonlinear parametric vibrations of super-long cables coupled with bridge decks. For mitigating parametric vibrations, research studies have shown that active control can not only achieve superior effective vibration mitigation but also provide guidance and methods for semiactive control device design such as magnetorheological (MR) dampers and other intelligent equipment. This paper proposes a novel vibration active controller for the coupled super-long stay cable–bridge deck and investigates the nonlinear dynamic behaviors of the active controlled parametric vibrations of super-long stay cable coupled with bridge vibration. Here, a stay cable’s gravity sag curve equation is employed to establish the parametric vibrations model. This sag curve equation includes the chordwise force of gravity. Based on this vibration model, we have provided more comprehensive insight into the nonlinear behaviors of super-long stay cables and the influence of the active controller on the nonlinear behaviors. The nonlinear dynamic characteristics, bifurcations, and chaotic motions were investigated in the case of 1:2:1, 1:1:1, and 2:1:2 resonance. This study firstly provides richer theoretical insight on the complex nonlinear parametric vibrations of super-long stay cable coupled with bridge vibration employed with active controller, secondly gives guidance for semiactive control devices design based on the provided active control strategy, and thirdly highlights potential benefits of using active control strategy to mitigate strongly nonlinear parametric vibrations systems.

This study investigates the effectiveness of combining lead rubber bearings (LRBs) and outrigger systems to enhance structural stability against concurrent earthquake and landslide hazards in clayey sand soil (CSS) conditions. Through advanced numerical modeling incorporating the William–Warnke failure criterion and multiperiod response spectrum analysis, we demonstrate significant performance improvements: 50% reduction in interstory drift, 30%–50% decrease in structural accelerations, and up to 40% mitigation of structural damage. The proposed system effectively addresses soil-structure interaction challenges unique to CSS environments, validated through case studies and parametric analyses. These findings provide practical solutions for multihazard resilient design in vulnerable regions.

Effective identification and quantification of bridge damage are critical for ensuring infrastructure safety and longevity. This study introduces a damage identification approach for steel truss bridges based on the stiffness separation method. This method simplifies large-scale problems by partitioning structures into substructures through separation interfaces. To enhance interface adaptability, the method conducts distinct analyses of nodes and members and a combined analysis involving both. A case study of the New Yellow River Bridge validated the effectiveness of the proposed method. Furthermore, a comparative analysis of the Nelder–Mead (NM) simplex and Interior Point (IP) methods was performed across various damage and separation scenarios. The findings confirm the accuracy and efficiency of the proposed method for damage detection, highlighting its importance for maintaining the safety of large bridge structures.

The safety and durability of infrastructure depend greatly on structural health monitoring (SHM). However, traditional SHM methods are labor-intensive, time-consuming, and prone to human errors. These issues can be solved with the help of machine learning (ML) and deep learning (DL). This paper presents the creation and application of a comprehensive, generalized dataset that addresses a significant gap in research on structural defect detection and classification. The dataset, developed using an unmanned aerial vehicle (UAV), contains over 7000 labeled images for detection purposes, and more than 50,000 images across five categories, including cracks, pockmarks, spalling, exposed rebar, and rust, for classification. Utilizing this dataset, we trained various models, including CNN-based, transformer-based, and hybrid approaches. Our study extensively compares these models in terms of performance and computational efficiency. Additionally, we propose a novel hybrid model, DefectNet, which achieved peak parameter efficiency. This model significantly reduces computational demand while maintaining high accuracy, demonstrating its potential for practical applications in SHM. The proposed network is further validated through real-world photos, suggesting potential in real-world monitoring. The results indicate that the proposed methods surpass traditional inspection techniques and offer a scalable solution for SHM.

Domain adaptation (DA) techniques have recently been developed as a promising approach to enhance the performance of structural damage classification algorithms. Unlike traditional methods, DA imposes fewer constraints on the nature and completeness of datasets, although its effectiveness largely depends on the similarity between the datasets used for knowledge transfer. This paper proposes a novel approach for assessing structural similarity to improve DA in structural health monitoring (SHM). The identification of suitable source data for knowledge transfer in damage detection is an open issue in SHM, especially when dealing with important geometric, mechanical, and topological differences between the structures. To address this issue, damage detection accuracy is increased by investigating similarity in the modal features of different framed structures, with the aim of understanding their dynamic behavior through a similarity index based on divergence measures. In detail, this work proposes a novel modal sensitivity-based similarity index which relies on the Kullback–Leibler divergence computed from vibration-based dynamic features. This similarity index effectively reveals how structures differing in highly sensitive parameters exhibit greater divergence. When DA is applied, source datasets with higher similarity lead to improved multiclass damage classification accuracy on the target framed structure. The proposed index can be used to systematically rank candidate source structures before applying DA, allowing a more efficient selection process. Its applicability extends to large-scale structures, where managing heterogeneous structural datasets is essential, supporting data-driven SHM strategies with enhanced transferability and reliability in real-world monitoring scenarios.

Deploying intelligent fault diagnosis models in real-world industrial settings is severely hampered by a trio of challenges: data scarcity, cross-machine heterogeneity, and time-varying operating conditions. Existing domain adaptation methods, which primarily align statistical distributions, often fail because they are physics-agnostic and implicitly assume data stationarity. To overcome these fundamental limitations, we propose a novel framework that learns representations invariant to both machine and operational variances. Our approach integrates a physical-informed spectral attention (SA) mechanism with a dynamic spectral distribution alignment (DSDA) strategy. The SA mechanism adaptively identifies and focuses on the invariant harmonic structures of fault signals, making it robust to nonstationarity. Concurrently, the A-distance-guided DSDA dynamically balances physical constraints and statistical alignment to handle complex domain shifts. On 12 cross-machine, constant-speed tasks with only 10 labeled samples, our method achieves a state-of-the-art accuracy of 97.22%. More critically, in ultimate stress tests under time-varying speeds, it maintains an exceptional average accuracy of 93.55%, where traditional methods’ performance collapses. This work presents a paradigm shift toward building robust diagnostic systems by effectively decoupling physical and operational variances.

Bridge structural health monitoring (BSHM) has consistently been a research hotspot in civil engineering. The field of BSHM has experienced a significant transition from traditional manual inspections to an advanced integration of artificial intelligence (AI), culminating in the current peak with data-driven AI methodologies. Nevertheless, despite the impressive performance, data-driven AI techniques such as machine learning (ML) and DL exhibit limitations in interpretability, stability, and security. Conversely, the earlier generation of knowledge-driven AI, including expert systems and logical reasoning, while offering greater interpretability and stability, has not achieved widespread adoption due to its limited scope, inefficiency, and subpar predictive accuracy. Against this backdrop, the current paper advocates for the creation of more reliable and intelligible explainable artificial intelligence (XAI). The paper provides a chronological overview of AI’s evolution within BSHM and discusses the fundamental principles of knowledge-driven AI, data-driven AI, and XAI. It examines their respective applications in BSHM and evaluates the advantages and limitations of these approaches. The paper concludes by anticipating future trends and identifying the challenges within the field. The findings underline the necessity for advancement in XAI in BSHM. The envisioned AI is designed to incorporate the advantages of both traditional knowledge-driven AI and data-driven AI while minimizing their respective shortcomings. This symbiosis is projected to set the direction for AI’s progression in BSHM.

Traditional fault diagnosis methods primarily rely on single-parameter measurements, which often result in diagnostic inaccuracies. In addition, the power supply of the sensor in the smart bearing is usually a challenge. To address these two limitations, this study introduces an innovative smart bearing system that integrates two sensors with an energy-harvesting module. First, bearing heat generation was theoretically calculated using the Palmgren friction torque model, and the bearing thermodynamics under 1000–3500 rpm are characterized by finite element thermal field simulations through Ansys. Then, a Hertz contact-based dynamic model was developed, which is numerically solved by MATLAB, to capture the vibration characteristics for 1000–3000 rpm. The energy-harvesting efficiency of the energy-harvesting module in the smart bearing was systematically evaluated using Maxwell equation–driven electromagnetic analysis in Ansoft. Finally, the monitoring performance of the smart bearing was experimentally validated using a bearing life testing rig. The experimental results show that the temperature difference between the smart bearing and the simulation results is less than 3°C, and the vibration amplitude detected by the smart bearing is higher, which demonstrates the superior condition monitoring capabilities of the novel smart bearing. Furthermore, the experiment verified the energy-harvesting effect of the energy-harvesting module at 200–1000 rpm, and the output voltage could reach 2.151 V at 1000 rpm, verifying the rationality of the smart bearing’s energy-harvesting module. This research presents a significant advancement in the integration of multiparameter sensors with self-powered smart bearing technology, offering a new approach for condition monitoring in rotating machinery.

Neural operators have been developed to learn a highly nonlinear mapping between input fields and solution fields of mechanics problems, achieving significant speedup compared to conventional solvers. However, the application of neural operators in solid mechanics problems is still limited. To solve both forward and inverse problems in structural engineering, this study develops a two-dimensional vehicle–bridge Interaction Neural Operator (VINO2D) framework, achieving efficient bridge dynamic simulations and real-time damage detection on numerical datasets. VINO2D learns the mapping between the damage distribution field and the two-dimensional solution field of bridge response as a function of coordinates and time. A finite element (FE) simulation dataset of a bridge structure with an arbitrary damage distribution field is established to train the Fourier neural operator (FNO) model to learn the complicated nonlinear mappings between input damage distribution fields and structural response fields. The results indicate that forward VINO2D models achieve high-fidelity simulation of bridge dynamics responses induced by a traveling vehicle with a speedup of more than 2000 times compared to conventional FE solvers. Furthermore, the inverse VINO2D model demonstrates high accuracy with 5%–7% errors in the real-time inference of the damage distribution field along the bridge span from the structural response fields. The proposed method was validated through rigorous numerical experiments and may be combined with a state-of-the-art computer vision algorithm to achieve real-time model updating of bridge structures and digital twins.