Maximizing the likelihood of a successful structural health monitoring application: A probabilistic approach to optimum sensor placement in bridges

Integrated ultrasonic testing and numerical simulation for damage detection in steel bridge deck pavements

(i) To evaluate longitudinal changes in urine nephrin to creatinine ratio (NCR) throughout gestation in women with pre-existing diabetes; (ii) To evaluate the association of NCR with diabetic nephropathy and pregnancy outcomes including preeclampsia. A prospective cohort study of 158 pregnant women with pre-existing diabetes was conducted. Changes in urinary nephrin, protein, albumin and creatinine were assessed serially during pregnancy at four time points. The association with diabetic nephropathy and pregnancy outcomes was investigated by linear mixed effects models. Spearman's rank correlation was used to assess the correlation of NCR with protein/albumin to creatinine ratio (PCR/ACR). NCR increased from early to late pregnancy (p < 0.05); however, there was no significant difference in NCR between those with and without preeclampsia irrespective of gestational age. Women with diabetic nephropathy had 3.48 times greater [1.38-8.77] NCR at 14 weeks compared to those without (p < 0.01), although this was not sustained in late pregnancy. NCR was positively associated with PCR through pregnancy (p < 0.01) and with ACR at 14 and 30 weeks (p < 0.05); however, the associations were weak. While all women with diabetes show a gradual increase in NCR during pregnancy, it was not a useful marker in predicting preeclampsia. However, higher NCR was indicative of kidney damage in the first trimester and could potentially be useful for monitoring diabetic nephropathy.

Advancements in ex vivo bone biomechanics: multimodal technologies and their integration with artificial intelligence.

An improved YOLOv8 model for detecting damage types of maize kernels using proximal hyperspectral imaging

Using implicit correlations among synchronized vibration signals from pixels to improve the accuracy of structural damage detection

Fatigue damage detection and assessment of standard plate specimens via metal magnetic memory testing

Development of an automatic damage detection methodology using ultrasonic piezoelectric sensors under varying temperature conditions.

Simulation-based method for probabilistic damage detection and quantification using modal measurements

A digital twin framework for damage detection and localization of self-sensed cured-in-place underground pipelines

Robust road damage detection via multi-expert collaboration and cross-scale attention

Deep transfer learning for codling moth damage detection in ‘Conference’ pear using X-ray radiography

Building facade debonding damage detection method based on explainable artificial intelligence and UAV-thermography in cross scale field of view

MFCF: A multimodal cascade fusion system for efficient pipe damage detection

Assessment of the Number of Mode Shapes Required in the Modal Strain Energy Methodology for Structural Damage Detection

Pests represent a formidable challenge to agricultural production, significantly affecting both the quantity and quality of agricultural outputs. The timely detection, prevention, and mitigation of pest damage are paramount to ensuring the quality and safety of agricultural products. However, traditional manual methods for pest identification are not only cumbersome and resource-intensive but also prone to subjectivity, leading to sub-optimal identification efficiency and precision. To address the aforementioned issues, this study introduces an intelligent pest recognition system grounded in a convolutional neural network (CNN). This system integrates a multi-scale hybrid attention module with a residual network, specifically tailored for pest classification. Initially, a comprehensive experimental dataset was compiled by acquiring pest samples through three distinct methods. Subsequently, this dataset was utilized to train the improved CNN. By incorporating a multi-scale hybrid attention module and reconstructing the classifier module within the original ResNet18 model, a specialized CNN named PestNet was developed. Experimental results demonstrate that PestNet attains an average recognition accuracy of 93.61%, marking a significant improvement of 2.04% compared to the baseline ResNet18 network. This study comprehensively validates the effectiveness of the PestNet network model in pest recognition tasks through a series of rigorous evaluation metrics. Further ablation experiments confirm the necessity of each modification in the model and their positive impacts on overall performance. This research not only demonstrates the immense potential and broad application prospects of deep learning in pest recognition but also presents a potential intelligent and convenient technological solution for agricultural pest management.

Wind energy is proving to be the backbone of the transition to sustainable electricity generation. However, due to the permanent increase in wind turbine rotor blade length to achieve higher capacities, the already challenging transportation process of rotor blades becomes even more difficult, which can be addressed by segmenting the parts. This work presents several measurement campaigns of bolted connections used to couple the structural parts of wind turbine blades, monitored by both passive low-frequency strain measurements and active high-frequency ultrasonic waves. The comprehensive test concept enables gaining insight into the degradation process of the connection type under different loading conditions. Moreover, the monitoring systems are analyzed separately, and the impact of data fusion by combining the obtained scores is thoroughly investigated. The results reveal the strong need for more reliable analysis of the individual measurement systems. Particularly challenging is the high number of false positives due to the ultrasonic guided wave’s high sensitivity. The additional strain measurements incorporated at the decision level address this endeavor, despite their lower sensitivity, thereby increasing the reliability of the results at the macroscopic level. Overall, the study presents extensive test results for bolted connections under various loading conditions and demonstrates the reliable monitoring of the system employing decision-level data fusion.

Container structural integrity is vital to global trade safety and operational efficiency; however, manual damage inspection is time-consuming and error-prone, particularly for minor defects in complex port environments. Although machine vision has provided promising solutions, existing methods remain limited by difficulties in multi-scale damage detection under cluttered backgrounds, high computational complexity, and frequent missed detections of small targets. To overcome these challenges, a novel two-stage detection framework is proposed in this article. In the first stage, a Lightweight U-Net integrated with a Convolutional Block Attention Module (CBAM) is employed to achieve precise container surface segmentation and effective background stripping. By incorporating depthwise separable convolutions in shallow layers and CBAM-enhanced skip connections, the proposed model reduces parameters by 28.4% (to 20.21M) while maintaining high segmentation accuracy (99.03% mIoU) and real-time inference speed (48 FPS). In the second stage, a Vision Transformer (ViT) equipped with an adaptive dual-threshold mechanism is used to classify the segmented container surface as damaged or undamaged. Leveraging ViT’s global attention capability, classification sensitivity is dynamically adjusted through a confidence threshold ($\tau$_s) in conjunction with an optimized classification threshold ($\tau$_c), effectively reducing false positives. Experimental results demonstrate an F1-score of 94.7%, a false positive rate of 5.0%, and a manual review rate of only 10.0%, significantly outperforming You Only Look Once (YOLO)-based detectors, conventional convolutional neural networks (CNNs), and standalone ViT models. Overall, the proposed framework achieves a favorable balance between accuracy, efficiency, and deployability on edge devices, providing a robust “machine-dominant, human-assisted” solution for automated container inspection in port environments.

ABSTRACT Molybdenum disulfide (MoS 2 ), a prototypical semiconductor of the transition metal dichalcogenide (TMD) family, finds extensive applications in various fields such as optical sensing, electronics, photocatalysis, and flexible substrates. When subjected to long‐term storage or improper preservation, MoS 2 may undergo oxidation, adsorption of contaminants, structural degradation, or interlayer re‐agglomeration, which can significantly compromise its performance. This study employs experimental detection via Raman spectroscopy, supplemented by computational analysis using the quantum chemistry simulation software Gaussian 16W. Firstly, three experimental samples are analyzed using a Raman spectrometer. Secondly, the cluster structures of monolayer, 2H‐phase, and 1T‐phase layered MoS 2 are modelled and computed using Gaussian 16W. Finally, spectral characteristics and vibrational mechanisms are analyzed. Experimental results indicate that when structural damage occurs in MoS 2 , its characteristic peak near 411 cm −1 exhibits an increased FWHM, peak splitting, and a gradual blue shift, accompanied by peak broadening in the low‐wavenumber region. Theoretical calculations reveal that these spectral evolution features originate from changes in the interlayer stacking modes and an increase in lattice disorder within MoS 2 . This work provides a new criterion for the non‐destructive and rapid monitoring of the layer count in MoS 2 , and also offers valuable insights for the Raman spectroscopic detection of structural damage in other layered materials.

Replicating the skin's ability to sense touch, feel pain, and heal itself is key to developing the next generation of durable soft electronics. These capabilities become more critical in underwater environments, where divers and underwater machines face severe challenges such as limited dexterity, device damage, and restricted power availability. Here, we develop a self-healing magnetoelectric sensory system (SMES) that uniquely integrates self-powered tactile and proximity sensing with damage detection and autonomous recovery for amphibious operation. The SMES features a multilayer architecture composed of a damage-sensing layer and an underlying magnetoelectric sensing layer, both utilizing a self-healing elastomer with patterned liquid-metal conductors. The design enables the system to detect and recover from pricking, puncturing, and cutting damage while maintaining stable functionality. The SMES exhibits good sensitivity, rapid response, and robust durability in both air and water. Demonstrations with a smart diving glove and a soft robotic hand highlight its potential for noncontact communication and mechanoreception with damage feedback, paving the way toward next-generation amphibious soft machines that can feel and heal like living skin.