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Corrosion poses a significant threat to the longevity of steel bridges, impacting overall structural integrity. To effectively assess the structural condition of corroded steel bridges, conventional methods rely on visual inspections or single point measurements. To enhance and modernize this approach, this study introduces a novel framework integrating laser scanning data, computational models, and convolutional neural networks (CNNs). The CNN models are trained on a data set consisting of more than 1400 artificial corrosion scenarios generated by parameterizing real scan data from naturally corroded girders. This innovative method predicts the residual capacity and failure mode of corroded beam ends, achieving a low error rate of up to 3.3%. Unlike established evaluation procedures, the proposed evaluation framework directly utilizes post‐processed laser scanner output, eliminating the need for feature extraction and calculations. [ABSTRACT FROM AUTHOR]

This study introduces a state-of-the-art methodology for addressing crack segmentation challenges in structure health monitoring, a crucial concern in infrastructure maintenance. The main objective is to enhance real-time crack monitoring through a cutting-edge multimodal fusion approach that intricately combines a modified U-Net with transfer learning-based MobileNetV2. This integration strategically amalgamates spatial awareness and long-range dependency capture, resulting in an advanced model for crack segmentation. Thorough evaluations of a specialized crack detection data set underscore the efficacy of this integrated approach, positioning it as a reliable solution for real-time crack monitoring. Notably, the choice of MobileNetV2, recognized for its efficiency with the least parameters, contributes to the fusion's effectiveness. This study reveals superior performance, particularly when MobileNetV2 is integrated with U-Net, demonstrating enhanced accuracy and Intersection over Union (IOU) scores. [ABSTRACT FROM AUTHOR]

The pulse-coupled neural network (PCNN), due to its effectiveness in simulating the mammalian visual system to perceive and understand visual information, has been widely applied in the fields of image segmentation and image fusion. To address the issues of low contrast and the loss of detail information in infrared and visible light image fusion, this paper proposes a novel image fusion method based on an improved adaptive dual-channel PCNN model in the non-subsampled shearlet transform (NSST) domain. Firstly, NSST is used to decompose the infrared and visible light images into a series of high-pass sub-bands and a low-pass sub-band, respectively. Next, the PCNN models are stimulated using the weighted sum of the eight-neighborhood Laplacian of the high-pass sub-bands and the energy activity of the low-pass sub-band. The high-pass sub-bands are fused using local structural information as the basis for the linking strength for the PCNN, while the low-pass sub-band is fused using a linking strength based on multiscale morphological gradients. Finally, the fused high-pass and low-pass sub-bands are reconstructed to obtain the fused image. Comparative experiments demonstrate that, subjectively, this method effectively enhances the contrast of scenes and targets while preserving the detail information of the source images. Compared to the best mean values of the objective evaluation metrics of the compared methods, the proposed method shows improvements of 2.35%, 3.49%, and 11.60% in information entropy, mutual information, and standard deviation, respectively. [ABSTRACT FROM AUTHOR]

Pavement markings are a key transportation asset and traffic control device that facilitate safe and predictable driving. The effectiveness of pavement markings is dependent on their condition, particularly during nighttime and adverse weather. The Federal Highway Administration (FHWA) has developed regulations to guide minimum pavement marking retroreflectivity levels, which poses a potential challenge to public agencies because the current practice of visual inspection is labor intensive and the results can be subjective. To address the identified challenges and needs of public agencies, the objective of this research is twofold: (1) to serve as a proof of concept for the use of mobile light detection and ranging (LiDAR) to locate and assess the pavement markings for selected testing sections by developing and evaluating new automated LiDAR processing algorithms, and (2) to investigate the feasibility of identifying the deterioration trend of the retroreflectivity condition. This study developed a complete pavement marking inventory with retroreflectivity conditions for the 14 selected testing sections and also compared historical and current data to inform the deterioration trends of three types of marking materials, including polyurea, epoxy, and thermoplastic. The findings of this study will guide future phases with a larger selection of testing sections, material types, and roadway characteristics. The outcomes of the series of studies will help better define the benefit-to-cost ratio for different marking materials and eventually lead to the development of public agencies' pavement marking standards. [ABSTRACT FROM AUTHOR]

Crack detection at the pixel level across complex scenarios (structural interference and adverse working conditions) is a critical consideration in the maintenance of ballastless track slab (BTS). Although existing deep learning models achieve acceptable accuracy on cracks with a monotonous background, the ground truth with high labor cost is inevitable and their performance in complex scenarios may fall far below their theoretical bounds or even cause "all-black images." A hybrid algorithm based on synthetic data from digital twin model and weakly supervised style transfer is proposed in this paper for addressing the above challenges. The algorithm uses a region-attention strategy to enable the uncontrolled generative adversarial network (GAN) focusing its attention on weak labels containing crack regions, directly obtaining segmentation results with the same style as the ground truth of the crack forest dataset. In addition, a digital twin model that can simulate the real inspection working conditions is established to generate a synthetic crack dataset, enabling the hybrid algorithm to extract the most discriminative features. The results show that the performance of the hybrid algorithm on inspection images across complex scenarios is nearly 25% higher than that of the DeepLabv3+ network, while the time cost consumed is only 0.5% of the latter. The deployment of the region attention strategy also enables the hybrid algorithm to achieve a mean intersection of union (MIoU) of 79.38%, which is nearly twice as much as that of GAN. It not only eliminates the oversegmentation caused by structures such as rails and fastener systems but also overcomes "all-black images." In addition, synthetic data can greatly enlarge the range, type, and number of discriminative crack features compared with data augmentation based on limited real data, thus enhancing the performance of the hybrid algorithm for uncertain inspection data. Particularly, the fully trained hybrid algorithm based on the synthetic dataset shows good adaptability and generalization to adverse working conditions such as uneven lighting, noise, and blur. [ABSTRACT FROM AUTHOR]

Background/Purpose: Congenital biliary dilatation (CBD) is a bile duct malformation often associated with pancreaticobiliary maljunction. Different subtypes of CBD have been noted for clinical differences, but their pathogenic mechanisms are unclear. Methods: To elucidate the genetic basis of CBD, we performed lncRNA and mRNA sequencing and bioinformatic analysis on 18 cystic and 18 fusiform CBD samples. Results: We identified differentially expressed mRNAs and lncRNAs between the two types of CBD, and constructed coexpression modules that correlated with clinical characteristics of CBD using weighted gene coexpression network analysis. We found that the brown module was the highest positive correlation with fusiform CBD (R = 0.67, p = 7.9e–6) and contained the most genes. We then built a lncRNA–mRNA coexpression network to identify potential target genes of lncRNAs in CBD, and a protein–protein interaction network to investigate the hub genes from the target genes and the brown module. Finally, we performed enrichment analyses and found differences between cystic and fusiform CBD in hepatobiliary system development, liver and pancreas development involving hub genes ONECUT1 and HNF1B that could be regulated by corresponding lncRNAs. Conclusion: Our study suggests that lncRNAs may modulate pancreaticobiliary duct development differently in cystic and fusiform CBD, providing new insights for etiology studies and clinical treatment. [ABSTRACT FROM AUTHOR]

Transportation agencies make substantial efforts to implement safety improvement countermeasures to mitigate safety hazards. However, region-specific safety considerations, rather than accepting more general, widespread methods, and the most ideal investment decisions to improve safety, are often unclear. This is of increased concern for horizontal curve segments, because they are locations of elevated safety risk. Yet, there exists a gap in literature on the development and use of a geospatial tool to investigate horizontal curve safety. To fill this gap, a methodological approach to create a region-specific geospatial horizontal curve safety tool was developed in this research. The tool was created using two regions as application areas to ensure the methodological approach was reproducible and transferable. Geolocated crash data, roadway infrastructure data, and curve data were spatially integrated using a GIS. Bayesian hierarchical models were estimated with crash severity data to gain region-specific safety results. A GIS tool was then derived from the applied model coefficients. The tool was applied to prioritize the most safety-critical horizontal curves and to select optimal countermeasures, especially in cases with specific infrastructure investment identification needs. The results of this research benefit regional agencies in their aim to efficiently distribute investments and identify the most appropriate countermeasures to improve roadway safety in their region. [ABSTRACT FROM AUTHOR]

Monitoring traffic at locations where bicyclists and pedestrians are present and studying their interactions with motorized vehicles has the potential to reveal underlying crash mechanisms and, in turn, guide the implementation of suitable countermeasures. Previous research has demonstrated the capabilities of vision-based systems in traffic monitoring; however, as these systems rely on video camera data, they remain constrained in adverse lighting and weather conditions. Although radar-based sensors can be used in traffic monitoring, they have not been tested in multimodal environments. To bridge this gap a novel framework to classify trajectories recorded by radar-based sensors in multimodal traffic environments is developed. The Support Vector Machine is used as the classifier and the following aspects allow to develop a robust, flexible, and transferable classification framework that can be applied in various traffic scenes. The SVM employs (1) a speed and length or speed and acceleration feature vector, (2) trajectory normalization scheme (i.e., select multiple measurements per trajectory), (3) training sample balancing strategy, and (4) cross-validation strategy. The framework is tested and validated using data from two different multimodal intersections. The results suggest that the mode type for every trajectory can be predicted with 95% accuracy using ten speed measurements and the vehicle's mean length while accounting for the unequal number of observations per class. While these results are subject to change, i.e., both SVM's input (feature vector and/or balancing approach) and performance may vary across different traffic scenes, the proposed framework is transferable and flexible. [ABSTRACT FROM AUTHOR]