High precision and real-time acquisition system for interface stress measurement in bridge bearing

Author:

Cao Xinning,Li Guangming,Li Zhe,Sun Wujie,Yan Fabao,Jiang Ruijuan

Abstract

AbstractSince 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.

Funder

the National Natural Science Foundation of China

the Technology Developing Project of Shenzhen

the Key Coordinative Innovation Plan of Guangdong Province

Weihai Science and Technology Development Plan

Shandong postdoctoral innovation project

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Deep learning-based detection and condition classification of bridge elastomeric bearings;Automation in Construction;2024-10

2. Finite Element Analysis and Experimental Validation of 72000kN Vibration Isolation Device Testing System;2024 5th International Conference on Mechatronics Technology and Intelligent Manufacturing (ICMTIM);2024-04-26

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