The increasing interest in Digital Twins (DT) for road infrastructure stems from their ability to seamlessly integrate data from various sources and facilitate interactions between physical entities and their virtual counterparts. The cornerstone for achieving a road digital twin is a high-quality road model. Despite the widespread application of Building Information Modeling (BIM) technology across various stages of road engineering, the modeling of existing roads within the BIM framework has faced challenges, including a lack of accurate design data and extensive manual work. This paper presents an innovative and cost-effective approach to model existing roads by combining Uncrewed Airborne Vehicles (UAVs) -LiDAR System (ULS) and BIM. It begins with the development of a low-cost UAV-LiDAR platform designed for efficient data collection in road environments, capturing essential geometric information. Subsequently, point cloud mapping techniques, including an intensity-based planar downsampling strategy and a hierarchical refinement method, are employed to create accurate point cloud maps using the cost-effective ULS. Further, an automated modeling pipeline is proposed with point cloud processing and Revit Dynamo. It comprises three main steps: cross-section model design, point cloud parameter extraction, and the design of an automated workflow in Dynamo. The proposed method is validated with both qualitative and quantitative experiments, and the results demonstrate that the generated model accurately represents the structural, alignment, and lateral gradient details of the experimental road. In summary, this study offers a cost-efficient yet highly effective means to construct realistic models of existing roads.

This study employs the Long Short-Term Memory (LSTM) deep learning model to analyze the dual earthquake events that occurred in Turkey on February 6, 2023. The model is trained using Outgoing Longwave Radiation (OLR) data from the 11 years preceding the earthquakes. The research area is centered around the seismic grid, covering a spatial range of 5°×5°. Predictions are made using a 5-day sliding time window, and anomalies are identified within a 95% confidence interval. The results highlight multiple concentrated anomalies in the three months leading up to the earthquakes. These findings contribute to a deeper understanding of seismic precursors and underscore the potential of utilizing OLR data in earthquake prediction models. The research emphasizes the importance of fully considering temporal and spatial factors in earthquake event prediction, providing valuable insights for earthquake monitoring and early warning systems.

The accurate representation of pavement in three-dimensional (3D) space is pivotal for road infrastructure management. However, the sparse measurement from affordable equipment, such as 2D LiDAR, has limited the fully utilization of 3D pavement data. This paper aims to address this challenge by exploring the point cloud upsampling for pavement area. Specifically, Pavement-PU, a novel data-driven model is designed to enhance the quality and efficiency of dense point cloud generation task. Utilizing a transformer-based feature extraction module coupled with an integrated point upsampling strategy, Pavement-PU significantly improves the density, uniformity, and accuracy of point clouds derived from sparse and irregular initial scans. Through rigorous testing on the public dataset and a specially curated Pavement3D dataset, the model demonstrates substantial improvements over existing methods in terms of both quantitative metrics and qualitative assessments. Ablation studies further validate the impact of our architectural choices, confirming the effectiveness of the innovative structures implemented within the network. Our research paves the way for more effective and efficient methods in pavement maintenance and monitoring, leveraging advanced techniques for practical, real-world applications in pavement management.

Daily road maintenance is essential to road safety and serviceability, particularly to highways. In daily road maintenance inspection tasks, the objectives include a variety of targets, such as pavement cracks, foreign objects, guardrail damages etc. There is a lack of rapid detection methods that allow for the uniform identification of multiple targets for road maintenance. This paper proposes an automatic multi-object detection method for road daily maintenance inspection assisted by unmanned aerial vehicles (UAV). A dataset of multiple roadway anomalies (UAVROAD) for daily road maintenance needs was constructed. UM-YOLO, an improved algorithm based on the YOLOv8n algorithm was created to better extract the features of multiple targets, as well as fuse features and reduce computation while maintaining accuracy. The improvements include adding EMA (Efficient multiscale Attention Module) in the C2f module in the backbone, employing Bi-FPN fusion mechanism in the neck and using GSConv, a lightweight convolutional network, for the convolution operation. Compared with the YOLOv8n, the proposed UM-YOLO improved mean average precision(mAP) by 4.6% and reduced the model computation by 14%.