Three-dimensional (3D) digital technology is essential to the maintenance and monitoring of cultural heritage sites. In the field of bridge engineering, 3D models generated from point clouds of existing bridges is drawing increasing attention. Currently, the widespread use of the unmanned aerial vehicle (UAV) provides a practical solution for generating 3D point clouds as well as models, which can drastically reduce the manual effort and cost involved. In this study, we present a semi-automated framework for generating structural surface models of heritage bridges. To be specific, we propose to tackle this challenge via a novel top-down method for segmenting main bridge components, combined with rule-based classification, to produce labeled 3D models from UAV photogrammetric point clouds. The point clouds of the heritage bridge are generated from the captured UAV images through the structure-from-motion workflow. A segmentation method is developed based on the supervoxel structure and global graph optimization, which can effectively separate bridge components based on geometric features. Then, recognition by the use of a classification tree and bridge geometry is utilized to recognize different structural elements from the obtained segments. Finally, surface modeling is conducted to generate surface models of the recognized elements. Experiments using two bridges in China demonstrate the potential of the presented structural model reconstruction method using UAV photogrammetry and point cloud processing in 3D digital documentation of heritage bridges. By using given markers, the reconstruction error of point clouds can be as small as 0.4%. Moreover, the precision and recall of segmentation results using testing date are better than 0.8, and a recognition accuracy better than 0.8 is achieved.

Vehicle detection and classification have become important tasks for traffic monitoring, transportation management and pavement evaluation. Nowadays there are sensors to detect and classify the vehicles on road. However, on one hand, most sensors rely on direct contact measurement to detect the vehicles, which have to interrupt the traffic. On the other hand, complex road scenes produce much noise to consider when to process the signals. In this paper, a data-driven methodology for the detection and classification of vehicles using strain data is proposed. The sensors are well arranged under the bridge deck without traffic interruption. Next, a cascade pre-processing method is applied for vehicle detection to eliminate in-situ noise. Then, a neural network model is trained to identify the close-range following vehicles and separate them by Non-Maximum Suppression. Finally, a deep convolutional neural network is designed and trained to identify the vehicle types based on the axle group. The methodology was applied in a long-span bridge. Three strain sensors were installed beneath the bridge deck for a week. High robustness and accuracy were obtained by these algorithms. The methodology proposed in this paper is an adaptive and promising method for vehicle detection and classification under complex noise. It would serve as a supplement to current transportation systems and provide reliable data for management and decision-making.

Bolts are widely applied to the connections of structural components. Under the actions of alternative forces induced by the external loads, the bolts are commonly inevitable to be loosening in engineering, which will be a threat to structural safety. To avoid the reduction of the pretightening force, it is vital to examine the status of bolts periodically. So far, manual inspection with a torque wrench is the most frequently used approach, even though it is time-consuming, labor-intensive, and low frequency of data acquisitions for structural maintenance. Due to the looseness of bolts which commonly follows with relative rotation between bolts and nuts, in this study, a vision-based bolt monitoring system with an Internet of Things (IoT) device is proposed. Specifically, to observe the relative rotation between bolts and nuts, a novel barcode marker (termed PAC-code) is introduced at first. As followed, a corresponding smart device embedded with PAC-code decoding algorithm is also described for the further long-term monitoring of bolt looseness. Finally, a laboratory-based experiment was carried out to validate the reliability and precision of our system. The results indicate that the proposed system is sensitive in the identification of angular changes and can be used to monitor the looseness of bolts as precise as 0.1°. This technique is not only easy to be deployed with high economic efficiency in engineering but also meaningful for the data acquisition in bolts looseness-proof research.

The orthotropic steel-box girder (OSG) is widely used in the construction of a large-scale bridges. Since cumulative damages caused by the heavy vehicles and initial flaws of welding, the bridges with OSGs frequently suffer from fatigue cracks, which are commonly distributed around U-ribs. Hence, the management of fatigue cracks is mandatory in practical engineering. Although some techniques have been adopted for the detection of cracks, the workflow is often labor-intensive, time-consuming, and of low-temporal resolution. Considering the optical visibility of a crack and the limitation of the shape of an over-welding-hole around the U-rib, a machine vision-based monitoring methodology for the fatigue cracks in U-rib-to-deck weld seams is proposed in this paper. To be specific, a specific Internet of Things (IoT) based image acquisition device is first developed and introduced to obtain precisely part-view images of a fatigue crack. As followed, a novel image rectification and stitching method based on a specified coded calibration board is innovated and described for generating a measurable panoramic fatigue crack image. Furthermore, a deep learning-based crack detection-segmentation integrated algorithm is developed to detect and segment the crack areas. Afterwards, a feature extraction procedure based on image processing is explored to obtain the morphological features of a crack, involving its area, length and width. Finally, a field experiment was carried out on a real steel suspension bridge. By comparing the measurements both from manual measuring and vision-based monitoring, the results indicate that the proposed methodology is very promising to monitor the fatigue cracks in U-rib-to-deck weld seams, and the root-mean-square errors in length and width measuring could be 3.0195 mm and 0.003 mm, respectively. This work is not only of practical value to the management and maintenance of the OSG bridges in engineering, but also critical for the researches on fatigue cracks propagation.

Since the high randomness feature of traffic flows, it is challenging to be aware of the spatial-temporal characteristics of dynamic vehicle loads on a long-span bridge. Compared with conventional measuring approaches, computer vision-based techniques have competitive advantages in the measurement of factual vehicle loads parameters, including the passing time, velocity, vehicle type, and corresponding locations on the bridge at each timestamp. In this paper, a vision-based framework for the identifications of dynamic vehicle loads on long-span bridges is proposed. To be specific, the framework consists of two major parts. The first part is the extraction of spatial-temporal factors of vehicles, including the detection of vehicles and the estimation of tracklets. Here, Single Shot Multi-Box Detection (SSD) architecture with MobileNets is utilized for detecting vehicles, while the data association technique with Kalman filter is applied on detection results for generating tracklets. Then, in the second part, based on the detection and tracking results, a Multi-layer Monte Carlo (MLMC) model is developed for axle-load intensities inference and estimation. The learned model is utilized to infer the dynamic load of vehicles from the detection results and generated tracklets. The proposed methodology is validated via an experiment of a case study on the Jiangyin bridge, Jiangsu Province, China, and several spatial-temporal distributions of vehicle loads are specifically analyzed and discussed. Experimental results indicate our proposed framework is a feasible and reliable solution to predict and identify dynamic vehicle loads on long-span bridges, without the assistance of the deck-embedded Weigh-in-Motion (WIM) system.

Traffic load simulation (TLS) is critical for the design and assessment of long-span bridges. Traditional methods, such as Monte-Carlo sampling and Cellular Automaton, rely on actual traffic data for load generation and evolution. However, they often overlook in-lane transverse movements, which are vital for precise bridge component assessment. This paper presents a TLS framework that incorporates in-lane transverse movements for long-span bridges. We select eight parameters as input features for a Transformer-based deep learning model, designed to predict both longitudinal and transverse vehicle speeds. The TLS process begins with spatial-temporal traffic load monitoring on the target bridge. Monte-Carlo sampling generates vehicle data, and the trained Transformer model simulates traffic evolution. A case study on a 1490-meter main-span suspension bridge illustrates the proposed method. Traffic trajectories were captured using a multi-vision system and reconstructed to minimize errors. The Transformer model was trained with optimized hyperparameters, enabling the completion of TLS on the entire bridge deck. We also compare the performance of other deep learning models, evaluate the accuracy of transverse distribution in TLS, and discuss its potential applications in future bridge assessments. The proposed TLS method enhances current practices by accurately simulating transverse vehicle positions on bridge decks, thereby improving the fidelity of microscopic traffic simulations and enabling more precise fatigue damage assessments of bridge components.