An effective approach using time reversal technique and lead zirconate titanate (PZT) transducer was described in this paper to monitor the loading status of pin-connected structures, which are widely used in the construction industry. A tension-controllable structure with a pin connection was fabricated and used for experimentation. Two PZT patch sensors were mounted on the pin and on the connected structural surface. In the experiment, the time reversal process was performed by varying the tension on the pin joint, and the experimental results demonstrate that the peak value of the focused signal, detected by the PZT sensor, increases with the load on the pin connection. In addition, the entire experimental process was repeated seven times to verify the repeatability and consistency of the proposed method. Subsequently, the reliability of the experimental results was verified in an environment with high disturbances. All the test results show that the proposed method using PZT sensors along with the time reversal technique has potential to be employed in practical applications, especially monitoring the load of a cable-deck connection in a cable-strut bridge in an environment with severe disturbances.

Accurate and objective evaluation of existing bridges is critical for ensuring the bridge’s safety and optimizing maintenance strategies. This study proposes an integrated Fuzzy Best and Worst Method and fuzzy comprehensive evaluation (FBWM-FCE) model to evaluate uncertainties in expert judgments and complex decision-making. A four-layer evaluation indicator system and five distinct grades for bridges were established, aligned with the JTG 5120-2004 and JTG/T H21-2011 standards. The FBWM innovatively employs triangular fuzzy numbers (TFNs) to reduce linguistic uncertainties and cognitive bias in bridge evaluation. Subsequently, by integrating FCE for multi-level fuzzy comprehensive operations, the method translates qualitative evaluations into quantitative evaluations using membership matrices and weights. A case study of Ding Jia Bridge and Jigongling Bridge validated the FBWM-FCE model, revealing Class III Bridge (fail condition), consistent with on-site inspections in the 2020 Bridge Inspection and Evaluation Report (Highway Administration of Hubei Provincial Department of Transportation). Comparative analysis demonstrated FBWM’s operational efficiency, requiring 20% fewer pairwise comparisons than AHP while maintaining higher consistency than BWM. The model’s reliability stems from its systematic handling of epistemic uncertainties, offering a high reduction in procedural complexity compared to standardized methods. These advancements provide a scientifically rigorous yet practical tool for bridge management, balancing computational efficiency with evaluation accuracy to support maintenance decisions.

For vision‐based structural displacement measurement and health monitoring, the scale factor (SF) calculation plays a pivotal role in converting the pixel displacement into the actual one. On the other hand, for the current SF calculation methods, the object distance, between the to‐be‐measured points on the object plane and the optical center of the shooting instrument, has to be measured in advance due to the existence of the pitch and horizontal angle under off‐axis measurement. Unfortunately, it is usually inefficient, difficult, and even impossible to obtain the object distances for all the to‐be‐measured points, especially for the full‐field displacement monitoring on huge scale structures. In this paper, a novel SF calculation method was proposed to calculate the full‐field scale factor of the host structure under off‐axis measurement by combining with the similarity relation of the camera imaging model. With the help of this method, all the scale factors of the to‐be‐measured points can be calculated accurately and highly efficiently, as long as the object distance of any one point or the geometric dimensions of any feature object on the object surface is provided. In addition, to quantitatively assess the SF calculation accuracy of the method, a static vision‐based measurement investigation was firstly conducted, and then, two experimental investigations about the multipoint and full‐field structural displacement measurement on a cable‐stayed bridge model and a simply supported beam model were conducted to validate the effectiveness and feasibility of the proposed method. Finally, all the results demonstrated that the proposed method exhibits an excellent performance on the SF calculation under off‐axis measurement and provides a great potential to be utilized for the vision‐based structural multipoint and full‐field displacement measurement and health monitoring.

Concrete columns are critical in supporting the weight of an entire structural frame and also play a key role in force transferring among structural members. Therefore, integrity of the columns, especially their axial load bearing capacity, directly affects the stability and safety of the entire structure. In this study, a wearable smart hoop is designed to monitor the axial load of the concrete columns. The smart hoop measures the shift in impedance frequency of its integrated piezoelectric transducer and correlates the frequency to the structural state of the column. In order to validate the feasibility of the smart hoop, an experiment on two concrete columns with different dimensions is carried out. The smart hoop is installed on each column. Then, an increasing axial load was applied onto the specimen, and the admittance of the PZT patch is acquired under different load levels by using an impedance analyzer. Finally, frequencies corresponding to the peak and trough in the susceptance of the admittance signal are collected as the monitoring index to estimate the axial load variation on the specimen. The experimental results demonstrated a downward shift in frequency corresponding to an increase of axial load. The results validate the feasibility of the wearable smart hoop in monitoring axial load for concrete columns and show potential for retrofit on existing columns.