The intelligent monitoring of bridges has become vital as the number of bridges increases. Displacement measurements are useful information for structural health monitoring, which is the key method to ensure structural safety. However, most cable-based displacement measurements are costly and labor-intensive, and wireless measurements are limited by transmission distance and have difficulties in equipment maintenance and data management. The Internet of things approach provides a new technical method for addressing this challenge. This paper designs a low-cost IoT-based sensor system for bridge displacement monitoring, which consists of a sensor transducer, an amplifier circuit, an microprogrammed control unit module, an IoT wireless communication module and a cloud server. The proposed system can collect the displacement data accurately and send the data to the server by utilizing low-power wireless communication technology-NB-IoT. The analysis and visualization can be remotely performed via a web interface. Experiments and field tests show that the system can be successfully implemented in bridge displacement monitoring applications.

Scour is one of the primary reasons for the collapse of bridges, as it severely reduces the stability of their piers. This study developed a model updating method based on an improved genetic algorithm (GA) to identify scour depth, combined with a parallel computing technique to accelerate the model updating procedure. The proposed GA adopted a gradient-like calculation as the mutation operation, while the population in GA was initialized based on the exponential distribution. A finite element model of a continuous girder bridge, whose soil-structure interaction was simulated by winkle beam theory, was established to give a sensitivity analysis of the scour effect. A moving mass-spring model was used to simulate the vehicle-bridge interaction, and dynamic responses under various scour damages were generated to verify the method’s performance. The results of the numerical simulation indicated that the transverse vibration modes of a continuous girder bridge were sensitive to the scour effect, and the proposed method can identify the scour depth with an error of less than 0.31 m.

As an important part of a bridge, bearings easily suffer damage when exposed to various loading and environmental changes during their lifetime. Bearing replacement should be performed in a timely manner if damage occurs to high-speed railway (HSR) bridges. The unique structural form and strict operation requirements mean that bearing replacements of HSR bridges are much more difficult than those of highway bridges. However, compared with bearing replacements of highway bridges, far fewer scientific and engineering studies have been conducted to guide the bearing replacement of HSR bridges. In this paper, based on a three-span continuous concrete box girder HSR bridge, an innovative bearing replacement method using reverse disassembly and assembly techniques is proposed and was implemented at a location under operating HSRs. In addition, safety monitoring measures were undertaken to guarantee the safety of the bridge during bearing replacement. In contrast to the usual monitoring practice focusing on the displacement and the external strain of the structure, this monitoring work adopted the acoustic emission (AE) detection technique to explore the internal structural damage formed in the process of construction. The results suggest that the proposed bearing replacement method, which requires the girder to be jacked up by no more than 9 mm, is a promising practice for this complicated construction, and the start time and evolution of damage in the concrete of a bridge during construction can be captured accurately by AE detection with a parameter-based analysis method. All practices presented in this case study can guide future bearing replacements in similar projects.

Concrete piers are the key load-bearing components of wading bridges. Most detection equipment and methods focus on the surface defects of underwater piers, whereas the detection methods of internal defects are very deficient. This study proposes an ultrasonic computed tomography method combining linear travel time interpolation and simultaneous iterative reconstruction techniques for detecting internal defects in underwater concrete piers. The two-dimensional median filtering and self-organizing feature map are utilized to post-process tomographic images and the confidence approach is employed to detect defects. The efficacy of the proposed method is validated through numerical simulation and underwater experiments, respectively. The Comsol Multiphysics is utilized to simulate the two-dimensional random aggregate model of the concrete and internal defects are added to validate the method’s effectiveness from a mesoscopic perspective. Then actual scale piers containing embedded defects are cast and detected. The results demonstrate that the method can identify and locate internal defects in numerical simulations and practical tests. The numerical simulation experiments results are superior to those of scale concrete specimens. In addition, underwater concrete piers with larger internal defects, square holes, and no steel reinforcements tend to have better detection results.