Hongwei Ma

A critical challenge in structural health monitoring (SHM) is the extreme scarcity of labeled damage data from real-world bridges. Consequently, domain adaptation approaches that require target domain data with damage situation become nearly infeasible for real-world engineering applications. To address this limitation, this paper proposes a structural damage detection (SDD) method based on domain generalization, which constructs a highly generalizable damage recognition model by integrating style transfer and adversarial training. A latent domain with diverse styles is generated by mixing style features, such as mean and variance from different samples, followed by label generation through clustering. Adversarial training is then introduced to encourage the feature extractor to learn domain-invariant damage-sensitive features, thereby enabling the model to focus on essential features that are indicative of damage states. To validate the effectiveness of the proposed method, a simply supported beam model was established as the benchmark structure. Simulation results demonstrate that, compared to conventional damage identification algorithms, the proposed method exhibits superior damage localization performance on unseen target domain data. Furthermore, laboratory experiments confirm that the approach achieves excellent results in practical scenarios.

This study introduces a new bridge damage detection method using adversarial discriminative domain adaptation transfer learning. The method aims to transfer damage information between numerical models and real-world bridges across domains. The method uses an adversarial approach to teach the target domain’s feature extractor to mimic the feature mapping of a pretrained source domain extractor, aligning target domain features with the source’s feature space. The innovation is asymmetric mapping for adversarial learning, which represents real-world bridge data features within the finite element model data space and promotes cross-domain damage information transfer. The method’s efficacy was tested through numerical simulations and steel beam-cutting experiments. Moreover, the simulation tested accuracy and stability as domain shifts increased. The lab experiment cut steel beams to induce damage, treating the damaged structure as the target model. Both confirmed the method’s strong performance amidst errors and uncertainties.

A nonlinear ultrasonic time-domain identification method based on chaos sensitivity was proposed in this study. The Duffing chaotic system was introduced into the weak second harmonic identification to realize early detection and quantitative evaluation of fatigue damage in U71Mn steel. First, to ensure the reliability of nonlinear ultrasonic testing, a probe-pressure monitoring device was designed. Through pressure-stability experiments, 16 N was determined as the optimal pressure, which effectively suppresses contact nonlinearity interference and ensures coupling stability. Subsequently, the Duffing chaos detection system was established. The signal-system frequency-matching problem was resolved through time-scale transformation. Simultaneously, the issue of unknown initial phases was resolved using phase traversal compensation. Based on the chaotic system’s sensitivity to specific frequency signals and immunity to noise, the amplitudes of the fundamental wave and second harmonics in the target signals were quantified to calculate the nonlinear coefficient. Experimental results demonstrate that the proposed method can extract these amplitudes directly in the time domain, thereby effectively overcoming the spectral leakage inherent in traditional frequency-domain methods. The nonlinear coefficient of U71Mn steel exhibits a “double-peak” characteristic as fatigue damage increases. Specifically, the first peak appears at approximately 50% of fatigue life, while the second occurs at approximately 80%. This phenomenon is closely correlated with the distinct stages of internal fatigue crack propagation, reflecting a complex damage-evolution mechanism. This study not only provides a novel method for the precise extraction of weak nonlinear signals but also establishes a critical theoretical and experimental foundation for accurate fatigue life prediction for U71Mn rail steel.

The long‐term degradation of damper performance under adverse conditions compromises energy dissipation, posing a potential risk to structural safety. Online degradation identification approaches provide benefits, including in situ evaluation, real‐time processing, and high computational efficiency, among which Kalman filter–based methods offer unbiased minimum variance estimates from partial measurements. However, parameter identification in multidamper systems remains challenging, primarily due to the ill‐posed nature of estimating numerous unknowns from limited measurements. To overcome this limitation, this paper proposes a novel sparse extended Kalman filter under unknown input (SEKF‐UI) algorithm. In this method, damper damage factors are incorporated into an extended state vector while explicitly leveraging their sparsity as prior information. The sparsity constraint is seamlessly embedded within the recursive steps via the pseudomeasurement (PM) technique, thereby avoiding additional offline optimization and preserving the real‐time advantage. Thus, the proposed approach effectively addresses the ill‐posed inverse problem, enabling the simultaneous identification of multidamper damage factors and unknown inputs. Moreover, it preserves the recursive framework of the classical Kalman filter while significantly enhancing its robustness and accuracy. Numerical validation employs an 8‐story shear frame, a single‐span truss, and a 3‐story benchmark model, each equipped with a different damper mechanical model. Results confirm that the proposed algorithm outperforms conventional methods in identifying parameter degradation across multidamper systems.

Existing vibration‐based damage identification methods face significant challenges in practical engineering applications due to the reliance on a large number of sensors. To overcome the limitation, this paper proposes a novel structural damage identification method based on moving phase space reconstruction, which can use only a single sensor for effective damage identification. The proposed method introduces the concept of a subphase space and adopts an improved damage indicator to analyze the degree of topological change of phase trajectories at different time points. The parameters required for this method are completely determined by the vibration signal, without the need for detailed geometric or material data of the structure. To verify the effectiveness and reliability of the proposed method, the numerical simulation of a simply supported beam is carried out, and then the experimental verification is conducted on a hollow box girder. The results show that the proposed method can successfully identify and locate structural damage using only the vibration data of a single sensor and can effectively evaluate the degree of topological change in phase trajectories over time.

Structural health monitoring (SHM) is vital for ensuring structural integrity by continuously evaluating conditions through sensor data. However, sensor anomalies caused by external disturbances can severely compromise the effectiveness of SHM systems. Traditional anomaly detection methods face significant challenges due to reliance on large labeled datasets, difficulties in handling long-term dependencies, and issues stemming from class imbalance. To address these limitations, this study introduces a hierarchical attention Transformer (HAT)-based method specifically designed for sensor anomaly detection in SHM applications. HAT leverages hierarchical temporal modeling with local and global Transformer encoders to effectively capture complex, multi-scale anomaly patterns. Evaluated on a real-world dataset from a large cable-stayed bridge, HAT achieves superior accuracy (96.3%) and robustness even with limited labeled data (20%), significantly outperforming traditional models like CNN, LSTM, and RNN. Additionally, this study visualizes the convergence process of the model, demonstrating its fast convergence and strong generalization capabilities. Thus, the proposed HAT method provides a practical and effective solution for anomaly detection in complex SHM scenarios.

In recent years, effective methods for concrete damage identification and localization have become crucial in the field of structural health monitoring (SHM). This study proposes an approach utilizing piezoelectric sensors to detect and localize damage in concrete structures. The method involves using a network of piezoelectric ceramic sensors to actively excite and receive stress waves within the concrete. By analyzing the differences in wave propagation between healthy and damaged states, internal damage can be identified and localized. The collected data are processed using advanced signal processing techniques, including wavelet analysis and pattern recognition algorithms, to accurately identify the damage’s location and severity. Experimental results demonstrate the high precision of this method. Compared to traditional techniques, this approach offers significant advantages, including faster detection, non-destructive testing, and real-time monitoring. In conclusion, the use of piezoelectric sensors for damage detection and localization provides a promising solution for enhancing the safety and longevity of concrete structures, offering a reliable tool for structural health monitoring in civil engineering applications.

Background: Preeclampsia, one of the most lethal pregnancy-related diseases, is associated with the disruption of uterine spiral artery remodeling during placentation. However, the early molecular events leading to preeclampsia remain unknown.Results: By analyzing placentas from preeclampsia, non-preeclampsia, and twin pregnancies with selective intrauterine growth restriction, we show that the pathogenesis of preeclampsia is attributed to immature trophoblast and maldeveloped endothelial cells. Delayed epigenetic reprogramming during early extraembryonic tissue development leads to generation of excessive immature trophoblast cells. We find reduction of de novo DNA methylation in these trophoblast cells results in selective overexpression of maternally imprinted genes, including the endoretrovirus-derived gene PEG10 (paternally expressed gene 10). PEG10 forms virus-like particles, which are transferred from the trophoblast to the closely proximate endothelial cells. In normal pregnancy, only a low amount of PEG10 is transferred to maternal cells; however, in preeclampsia, excessive PEG10 disrupts maternal vascular development by inhibiting TGF-beta signaling.Conclusions: Our study reveals the intricate epigenetic mechanisms that regulate trans-generational genetic conflict and ultimately ensure proper maternal–fetal interface formation.