Abstract
Accurate control of hanger rod forces is crucial for ensuring structural safety and effective force distribution in long-span bridge construction. Traditional contact-based force measurement methods, such as strain gauges and accelerometers, face challenges including high costs, susceptibility to environmental interference, and intensive equipment demands, limiting their practical efficiency in complex environments. To address these limitations, this work proposes an innovative multi-stage construction method that integrates the influence matrix method with groundbased microwave radar interferometry to achieve rapid, high-precision, and non-contact force detection during hanger rod tensioning. Specifically, multi-stage construction employs a stepwise, iterative approach, dynamically adjusting rod forces in multiple stages based on real-time radar monitoring. This significantly reduces the risk of construction errors, minimizes equipment requirements, and enhances operational feasibility and safety. Based on this, a series of field tests on an actual long-span bridge validated the proposed approach, showing that after two tensioning stages, the rod forces approached target values closely, maintaining errors within 10%. The results confirm that this integrated multi-stage approach provides a practical, safe, and efficient solution for hanger rod tensioning in long-span bridges, offering substantial improvements in construction accuracy, efficiency, and applicability.
Key Words
dynamic deflection detection; ground-based microwave radar; hanger rod forces; influence matrix method; long-span bridge; multi-stage construction method
Address
(1) Huanzhong Sun, Weiqi Zheng, Zhihui Zhu, Yonghong Yang, Xingwang Sheng:
School of Civil Engineering, Central South University, Changsha, China;
(2) Huanzhong Sun:
China Railway 14th Bureau Second Engineering Co., Ltd., Tai'an, China;
(3) Weiqi Zheng, Xingwang Sheng:
Hunan Tieyuan Civil Engineering Testing Co., Ltd., Changsha, China;
(4) Yonghong Yang:
Shanghai-Hangzhou Passenger Dedicated Line Railway Co., Ltd., Shanghai, China.
Abstract
For a truss structure, the most common damage manifestation is the reduction of elastic modulus due to the degradation of material properties and the reduction of effective cross-sectional area caused by corrosion, etc. Based on the single parameter identification gradient regularization method (GRM), this paper proposes an elastic modulus and cross-sectional area dual-parameter joint identification method. Firstly, a mathematical and physical model for dual-parameter joint identification is constructed, followed by the derivation of the solution formula for dual-parameter identification based on GRM and the development of a general finite element solution program using MATLAB. A series of numerical simulation experiments are conducted. The results of which show that the identification results under different working conditions are in good agreement with the actual design parameters, and the solution precision can be as low as 10-7 with less than 10 iterations, and the error is below 4% under 12% measurement noise. The method is further applied to a steel truss experimental model, the results of which furtherly demonstrate the high accuracy and robustness of the method.
Address
(1) Ao Zhou, YuXin Zhang, Jiacheng Zhang:
School of Civil Engineering, Shanghai Normal University, No. 100, Haisi Road, Shanghai 201400, P.R. China;
(2) B.F. Spencer Jr.:
Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA;
(3) Hexin Zhang:
School of Computing, Engineering and the Built Environment, Edinburgh Napier University, Edinburgh EH10 5DT, Scotland, UK.
Abstract
The structural health monitoring (SHM) of utility tunnels is critical for urban safety, where automated crack detection plays a vital role in early damage identification. Crack detection in utility tunnels remains challenging due to the subtle appearance of cracks and complex background interference, which often degrades the performance of general-purpose algorithms. To address this, DG-YOLOv8, a lightweight instance segmentation model enhanced with attention mechanisms, is proposed. Specifically, a Ghost module is integrated into the backbone to reduce redundancy and computational cost, while a Dynamic Head introduces a feature selection mechanism to improve adaptability. Experimental results demonstrate that DG-YOLOv8 outperforms the baseline YOLOv8, achieving a 1.6% increase in mAP50 while reducing the number of parameters, GFLOPs, and inference time by 37%, 28%, and 49.5%, respectively. The proposed DG-YOLOv8 model offers a robust and efficient intelligent solution for automated visual inspection, contributing to the development of smart SHM systems for utility tunnels.
Key Words
attention mechanism; instance segmentation; lightweight network; utility tunnels
