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Most of vision‐based methods for structural damage detection rely on supervised learning, requiring a substantial number of labeled images for model training, which is labor‐intensive and time‐consuming. To address these challenges, this study introduces a vision‐based structural anomaly detection and localization approach using unsupervised learning and reverse knowledge distillation. The proposed model incorporates a teacher model, a student model, and a trainable one‐class bottleneck embedding module. The asymmetrical architecture of the teacher and student models forms an encoder‐decoder structure for parameter transfer and feature extraction. The student network receives a specific embedding from the teacher network as input and target, facilitating the recovery of multiscale information from the teacher. Training images only contain the undamaged structures, and the teacher model, a pretrained model, instructs the student model to remember their undamaged features to detect and localize damages in unseen testing images. Through experiments, including a comparison among five candidate backbones for pretrained teacher models based on the residual network and testing across various structural damage types, the optimal model is identified, demonstrating good performance in both anomaly detection and localization. Furthermore, the model's generalization performance is thoroughly validated, confirming its efficacy across diverse scenarios. [ABSTRACT FROM AUTHOR]

The project case of Qiwu Bridge using link slab action between simply supported composite spans is introduced. The 60 m-span girder of Qiwu Bridge is the longest and heaviest among the composite girders erected by bridge-erecting machine in China. Qiwu Bridge consists of a series of 3 simply supported spans built with composite girders and connected at the pier supports by a link slab. This solution has both advantages of (1) smooth driving condition and enhancement of durability by removing expansion joints and (2) clear structural behavior as simply supported spans. A finite element model was established to simulate and predict the mechanical response of this semi-continuous solution. Numerical results showed that the removal of the shear connection around the link slab decreased the stress level but also caused a stress mutation. Qiwu Bridge was also monitored by vibrating wire strain gages to follow its actual structural response. From preliminary monitoring data and numerical results, it can be concluded that the proposed construction method and the static scheme is safe for the 60-span composite girder with a large safety margin. Further experimental results will be obtained during the service stage of the bridge to validate the in-service performance of the proposed steel-concrete composite bridge with link slab. [ABSTRACT FROM AUTHOR]

Debris poses multifaceted risks and jeopardizes various aspects of the environment, human health, safety, and infrastructure. The debris trajectory in turbulent wind flow is more dispersed due to the inherent randomness of the turbulent winds. This paper investigates the three-dimensional trajectories of plate-type wind-borne debris in turbulent wind fields via the method of numerical simulation. A 3D probabilistic trajectory model of plate-type wind-borne debris is developed. The debris trajectories are numerically calculated by solving the governing equation of debris motion and by introducing turbulent wind flows based on the near-ground wind field measured in the wind tunnel to account for the probability characteristics of the debris trajectory. The dimensionless velocities and displacements of the debris trajectory show good agreement with the experimental data in wind tunnel tests, confirming the rationality of the probabilistic trajectory model. Based on the validated trajectory model, the probability characteristics of the debris impact position, impact velocity, and kinetic energy, debris angular displacement, and angular velocity are analyzed in detail under five different wind attack angles. The proposed probabilistic model of plate-type debris in turbulent wind flow provides an accurate and effective method for predicting debris trajectory in three-dimensional space. [ABSTRACT FROM AUTHOR]

Background We assess the effects of standard decompressive craniectomy with stepwise decompression of the intracranial compartment on the postoperative neurologic function, hemodynamics, and Glasgow Outcome Scale (GOS) score of patients with severe traumatic brain injury (sTBI). Methods One hundred sTBI patients admitted from July 2017 to February 2019 were enrolled and randomly divided into step and standard groups (n = 50) using a random number table. The standard group received traditional standard decompression during surgery, while the step group underwent multistep decompression during surgery. Heart rate (HR), systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP) were measured immediately after surgery (T0), 3 hours after surgery (T1), 6 hours after surgery (T2), and 12 hours after surgery (T3). The postoperative Glasgow Coma Scale (GCS) score, neurologic function deficit score, and GOS score were evaluated. Results After treatment, the excellent/good rate of neurologic function improvement and GCS and GOS scores of the step group significantly exceeded those of the standard group (p < 0.05). Compared with the standard group, the HR, SBP, DBP, and MAP decreased significantly in the step group at T1, T2, and T3 (p < 0.05). Conclusion Standard decompressive craniectomy under multistep decompression can markedly improve the neurologic function, hemodynamics, and prognosis of patients. [ABSTRACT FROM AUTHOR]

Readily available environmental covariates in current digital soil mapping usually do not indicate the spatial differences between deep soil attributes. This, to a large extent, leads to a decrease in the accuracy of 3D soil mapping with depth, which seriously affects the quality of soil information generated. This study tested the hypothesis that spatialized laboratory soil spectral information can be used as environmental covariates to improve the accuracy of 3D soil attribute mapping and proposed a new type of environmental covariable. In the first step, with soil-forming environmental covariates and independent soil profiles, laboratory vis-NIR spectral data of soil samples resampled into six bands in Anhui province, China, were spatially interpolated to generate spatial distributions of soil spectral measurements at multiple depths. In the second step, we constructed three sets of covariates using the laboratory soil spectral distribution maps at multiple depths: conventional soil-forming variables (C), conventional soil-forming variables plus satellite remote sensing wavebands (C+SRS) and conventional soil-forming variables plus spatialized laboratory soil spectral information (C+LSS). In the third step, we used the three sets of environmental covariates to develop random forest models for predicting soil attributes (pH; CEC, cation exchange capacity; Silt; SOC, soil organic carbon; TP, total phosphorus) at multiple depths. We compared the 3D soil mapping accuracies between these three sets of covariates based on another dataset of 132 soil profiles (collected in the 1980s). The results show that the use of spatialized laboratory soil spectral information as additional environmental covariates has a 50% improvement in prediction accuracy compared with that of only conventional covariates, and a 30% improvement in prediction accuracy compared with that of the satellite remote sensing wavebands as additional covariates. This indicates that spatialized laboratory soil spectral information can improve the accuracy of 3D digital soil mapping. [ABSTRACT FROM AUTHOR]

Aftershocks (ASs) following strong mainshocks (MSs) can exacerbate structural damage or lead to collapse. However, the scarcity of recorded data necessitates reliance on artificial sequences, which have difficulty in characterizing the time‐frequency correlation between MSs and ASs. This study innovatively converts the AS time history prediction into an image translation task, exploiting the invertible transformation between accelerograms and time‐frequency representations. An encoder–decoder neural network is developed to encode the MS information into the latent space of a pre‐trained generative adversarial network, enabling accurate AS predictions through the decoder. The integration of seismic parameters further improves the AS prediction performance. Comparative analyses demonstrate that the proposed method outperforms the traditional ones on accuracy and robustness and reproduces the non‐stationarity of ASs. [ABSTRACT FROM AUTHOR]