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Bridge Bearings are important devices for transferring loads between the upper and lower structures of bridges. They can ensure the safety of bridges and regulate deformation. They can also prevent bridge displacement caused by temperature changes, seismic forces, and other factors in various structural types. The status of bridge bearings can effectively reflect the healthy status of the bridge. Monitoring of stresses on bridge bearings can be used to evaluate the healthy status of the bridge structure. However, bridge bearings are installed in invisible locations, and traditional manual inspection methods cannot accurately determine their damage and degree of destruction. In this paper, an intelligent bearing pair is designed to resolve this problem. The designed intelligent bearing status assessment system with multi-level threshold values can trigger a warning when the threshold is exceeded. Experiments have been conducted to verify the accuracy of its signal processing.

Since the damage of the bridge structure may cause great disasters, it is necessary to monitor its health status, especially the bridge bearing, the important connecting component of the bridge's upper and lower structures. Nowadays, manual inspection is the main method to get the information of the bridge bearings’ work status. However, occasional damage of bridge bearing may not be detected in time, and sometime the installation position of the bearing makes the manual inspection on bridge bearing difficult and even impossible. Therefore, in order to know the work status of the bridge bearings timely, an intelligent remote monitoring system for the bridge bearing is developed. A 32-channel real-time acquisition system is designed by using an AD7768-1 analog-to-digital converter and Xilinx Spartan-6 Field Programmable Gate Array for interface stress continuously acquired in the bridge bearing. To assure the good linearity and low noise performance of the monitoring system, the data acquisition card is meticulously designed to reduce noise from both hardware and software and realize high-precision acquisition. Through the establishment of the monitoring server, the compressive stress data can be displayed synchronously and the overpressure situation can be alarmed in real-time. The experimental results show that the accuracy of the calibrated sensor is within 1.6%, and the detection error of the acquisition board is less than 200 μV. The acquisition system is deemed to have considerable advantages in accuracy and applicability.

Since the damage of the bridge structure may cause great disasters, it is necessary to monitor its health status, especially the bridge bearing, the important connecting component of the bridge's upper and lower structures. Nowadays, manual inspection is the main method to get the information of the bridge bearings’ work status. However, occasional damage of bridge bearing may not be detected in time, and sometime the installation position of the bearing makes the manual inspection on bridge bearing difficult and even impossible. Therefore, in order to know the work status of the bridge bearings timely, an intelligent remote monitoring system for the bridge bearing is developed. A 32-channel real-time acquisition system is designed by using an AD7768-1 analog-to-digital converter (ADC) and Xilinx Spartan-6 FPGA for interface stress continuously acquired in the bridge bearing. To assure the good linearity and noise performance of the monitoring system, the data acquisition card is meticulously designed to reduce noise from both hardware and software and realize high-precision acquisition. Through the establishment of the monitoring server, the compressive stress data can be displayed synchronously and the overpressure situation can be alarmed in real-time. The experimental results show that the accuracy of the calibrated sensor is within 1.6%, and the detection error of the acquisition board is less than 200µV. The acquisition system is deemed to have considerable advantages in accuracy and applicability.