Yongding Tian

Computer vision technology has emerged as a promising approach for multipoint displacement monitoring of civil infrastructure, owing to its inherent noncontact operation and remote measurement capabilities. However, its measurement accuracy is greatly affected by ambient temperature variations during long-term monitoring applications. Therefore, this study investigates temperature-induced displacement measurement errors in vision-based measurement techniques and proposes an error compensation method based on a time series forecasting model. First, a deep learning-based feature descriptor is employed and enhanced for image feature extraction and displacement extraction, followed by a quantitative analysis of the impact of temperature variations on vision-based displacement measurement errors. Second, the ModernTCN deep learning model is used to establish the nonlinear relationship between temperature differences and temperature-induced measurement errors. Finally, the established model is employed to predict and compensate for the temperature-induced measurement errors. The effectiveness of the proposed method is validated via indoor laboratory experiments, outdoor temperature tests, and field tests on a long-span bridge. The results reveal a nonlinear relationship between temperature differences and vision-based displacement measurement errors, and this relationship can be effectively mitigated using the proposed time series forecasting model. Experimental results indicate that correlation coefficients and cosine similarities obtained by the proposed method outperform conventional Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and state-of-the-art architectures (i.e., Transformer, Informer, PatchTST, TimesNet, and N-BEATS). In field tests, the proposed method achieved a Mean Absolute Error (MAE) of approximately 0.002 mm, representing an error reduction of over 90% compared to the Transformer model. The proposed method enables robust multipoint displacement monitoring with integrated temperature self-compensation, providing a reliable data foundation for safety evaluation and health monitoring of engineering structures.

Bearing flatness is a critical for the construction quality inspection of highway and railway bridges during construction. Conventional methods may result in high costs, operational safety risks and low measurement efficiency for high bridge piers in mountainous terrain. To address these issues, this paper developed an innovative quadcopter drone with an optical prism target for rapid and automated measurement of bearing flatness. The contribution of this study includes two aspects: (1) An optimized YOLO-MiniFaster model is developed for bearing padstones detection based on the RepNCSPGELAN module, the Slim-FPN feature fusion architecture, and SimAM attention mechanism. This enhanced architecture enables real-time and accurate detection of bridge bearings with higher computational efficiency. (2) A custom hardware prototype is developed, consisting of a quadcopter drone platform, an optical target module, a high-resolution image acquisition unit, and flight control system. The effectiveness of the developed drone system has been validated through field tests of a newly constructed railway bridge. Experimental results demonstrate that the developed model achieves a mean average precision (mAP) of 87.14% in detecting the bridge bearing padstones with a 14.3% reduction in model parameters compared to the original model. Furthermore, the quadcopter drone system enables a 45% reduction in on-site operational time compared to traditional manual methods. The developed system offers an alternative method that could improve the operational safety and measurement efficiency for bridge construction in hard-to-access locations.

Traditional manual inspection methods for concrete structures exhibit inherent limitations in complex construction environments, including high operational costs, subjective assessment biases, and ...

Bridges are crucial components of transportation networks; however, their structural performance deteriorates over time due to increasing traffic loads, material deterioration, and natural disasters. Rapid assessment of the load-carrying capacity of highway bridges is important for maintenance planning and rescue route management after natural disasters. Conventional load-test-based performance evaluation methods are labor-intensive, expensive, and potentially hazardous. To overcome these challenges, this paper proposes a rapid load-carrying capacity evaluation method for highway bridges using impact testing data and virtual load testing. The contributions of this study include (1) the development of an automated bridge flexibility identification method that employs impact testing data and a multi-scale peak extraction algorithm; and (2) the development of a multi-point deflection prediction and load-carrying capacity assessment method using identified modal flexibility and virtual load testing. Validation through experimental validation of a simply supported T-shaped beam bridge and field testing on a three-span continuous concrete box-girder bridge confirms the effectiveness and robustness of the developed method. This approach significantly simplifies the load-carrying capacity assessment process, offering a practical solution for the rapid evaluation of numerous highway bridges.

Various sensors have been widely employed to measure dynamic responses and assess the performance of flexible barrier systems in mitigating rockfall disasters. However, their real-world applications are hindered by challenges, such as sensor malfunctions, power outages, and other operational disruptions, which may lead to data loss or corruption. To address these limitations, this study presents a virtual sensing approach for estimating internal forces in flexible barrier systems, employing a time series prediction model enhanced by the Transformer- Kolmogorov-Arnold Network (KAN) deep learning model. First, component-specific and impact-related parameters in numerical models of flexible barrier systems are calibrated to extract dynamic responses of critical components under rockfall impacts, forming a virtual sensing dataset. A time series prediction neural network is then trained on this dataset to model the relationship between the measured internal forces of key components and the unmeasured internal forces of other components during rockfall impacts. This virtual sensing model is designed to replace physical sensor measurements in scenarios where long-term sensor deployment is impractical. Comparative analysis with traditional models, conducted using the constructed dataset, demonstrated the superior performance of the proposed method in virtual sensing tasks. Furthermore, the effectiveness and robustness of the proposed method were validated using real sensor data from a full-scale three-span flexible barrier system subjected to rockfall impacts. The results showed that the virtual sensing model can predict internal forces with a peak error within 10% and a mean peak error below 5%, confirming its high accuracy. Overall, the proposed method exhibits significant potential for cost-effective health monitoring of numerous flexible barrier systems in mountainous regions.

Steel-concrete bridges have been widely constructed within road networks due to their distinctive mechanical properties. Nevertheless, issues related to construction quality, such as concrete pouring blockages, steel plate delamination, and steel plate deformation, frequently occur, posing significant risks to the safe operation of these bridges. Laser scanning technology provides an effective solution for rapid construction quality inspection of bridges, owing to its rich information, high data density, and noncontact measurement capabilities. However, conventional data processing methods are hindered by cumbersome procedures and limited accuracy in point cloud registration for deformation extraction. To address these challenges, this study developed a highly accurate and automated full-field deformation measurement method for the construction quality inspection of steel-concrete composite bridge piers using laser scanning technology and deep learning. The innovative contributions of this study are twofold: (1) a geometric feature-based point cloud simplification algorithm was developed to automatically select sampling points while preserving crucial structural information and reducing the data volume by employing the Fast Point Feature Histogram (FPFH) feature and an improved voxel down-sampling technique; (2) a highly accurate point cloud registration method was devised, in which geometric structure embedding and an attention mechanism were utilized to extract point cloud features, and maximal clique constraints were applied to estimate the positional attitude between two point cloud datasets for full-field deformation extraction and construction quality inspection. The effectiveness and robustness of the proposed method were validated using public point cloud datasets and field test data from a steel-concrete composite bridge pier. Results demonstrate that the proposed method surpasses existing feature-based registration methods regarding translation error, inlier ratio, and root mean square error. Furthermore, the maximum deformations on the inner and outer sides of the bridge pier were measured at 8.03 cm and 8.65 cm, respectively. The average relative error of the measured deformations was found to be less than 3% when compared to the results obtained with the hand-held laser scanner. Compared with existing deformation measurement methods, the proposed method can realise bridge deformation measurements at millimeter level, and has the advantages of non-contact, high accuracy and automation, as it does not require the installation of sensors and can process the point cloud data end-to-end, with less human intervention and low technical requirements for operators.