Keywords

1 Introduction

Bridges, as an important part of highways, are key hubs of transportation systems. Bridge construction has made great progress in recent years around the world. The wide application of bridges makes logistics and transportation more convenient, and traffic volume increases day by day. However, due to design defects, erosion, and aging of the stressed members, the bridges may be damaged with different degrees. As a result, bridge collapse may lead to different degrees of economic losses and human casualties. To reduce the occurrence of similar accidents, the traditional practice is to conduct periodic inspections of bridge structures. The reliability and safety of the structure are assessed based on the inspection results. However, this approach is costly, non-real-time, and poor in integrity and real-time. Thus, it cannot effectively ensure the safety and durability of bridges during usage. With the development of electronic information technologies, intelligent health monitoring systems for bridge structures have come into being [1,2,3,4]. Unlike the static observation method of structural inspection, intelligent monitoring of bridge structures is real-time dynamic monitoring. By analyzing the real-time stress signal of the bridge, the safety status of the bridge can be obtained. The system mainly includes components such as advanced sensor subsystems [5, 6], stable data acquisition and data transmission systems [7, 8], and reliable monitoring and safety assessment platforms [9, 10]. The modules of the system operate in different hardware or software environments and work in concert to accomplish the intelligent health monitoring and safety assessment functions of bridge structures.

2 The Bearing Capacity Experiment of Bridge Intelligent Bearing

2.1 Equipment and Purpose of the Experiment

The purpose of this experiment is to verify the accuracy of signal processing of force measuring bearings and calibrate the relationship between the calibrated measuring point stress and the intelligent bearing sensor. The experiment data is used to determine the threshold values of each level for multi-level warning of the bearing status, and to provide data support for the intelligent bearing status evaluation system.

A press with a maximum value of 300 MN was used for this experiment, which can realize the functions of equal-rate loading, equal-rate displacement test, load holding, etc. The physical diagram of the pressure test machine is shown in Fig. 1. The intelligent support adopts model GPZ (II)-2.0–10%-GD, as shown in Fig. 2.

Fig. 1
A photograph of a machine that has a bridge structure at the top with a dark cylindrical device in the middle, a bridge intelligent bearing, sensor devices, acquisition equipment, and several other components.

Pressure machine

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Fig. 2
2 photographs of the rectangular-shaped device, which has a spherical-shaped structure in the middle that is connected with several bolts. It has some text that is written in a foreign language.

The appearance of Bridge Intelligent Bearing

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2.2 Process of the Experiment

Before the experiment starts, the sensors will be installed in the four corresponding positions of the Bridge Intelligent Bearing. The collection equipment will be installed on the test bench and powered on for testing. The test is officially conducted after the equipment works normally. The equipment arrangement is shown in Figs. 3 and 4.

Fig. 3
A photograph of the rectangular-shaped device, which has several holes, connecting wires, and several other components that are connected to a laptop.

The arrangement of acquisition equipment

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Fig. 4
A photograph of the device, which has two rectangle-shaped steel frames on both the top and bottom. It has an intelligent bearing device in the middle, with several connecting probes and other components.

The arrangement of Bridge Intelligent Bearing

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During the experiment, the vertical application pressure is loaded and unloaded at 11 levels, which are 0, 0.55, 0.868, 1, 1.468, and 1.71 MN. When the pressure machine is loaded to a certain level, it is held under that load for three minutes. When the level of 1.71 MN grade is reached, unloading is performed in a graded manner. The experiment test has been repeated three times.

2.3 The Results of the Experiment

Firstly, the vertical applied pressure F (MN) of the press is converted into the compressive stress P (MPa) of the rubber sheet, as shown in Eq. (1).

$$ P = \frac{F}{{\uppi \times \left( {\frac{{\text{d}}}{2}} \right)^{2} }} $$
(1)

where P is the value of compressive stress applied vertically in MPa; F is the vertical applied pressure in MN; d is the diameter of the rubber sheet 0.33 m.

The calculated values were used as theoretical values, and the data were compared with the compressive stress values of the four measurement points obtained from the acquisition equipment to obtain the relationship curves, as shown in Fig. 5.

Fig. 5
A line graph plots the value of the acquisition device versus vertical pressure. It plots 4 concave-down increasing curves and 1 linear increasing line, with data points that provide data for 4 different measurement points and a theoretical value, respectively.

The relationship curve between stress and theoretical value at each measurement point of fixed support

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The average error percentage of each measurement point is obtained by summing and averaging according to the relative error, as shown in Eq. (2).

$$ z = \frac{{\sum\nolimits_{i = 0}^{n} {\frac{{\left( {\sum\nolimits_{j = 0}^{m} {x_{i} } } \right) - y_{i} }}{{y_{i} }}} }}{n + 1} $$
(2)

where z, xi, and yi denote the average error ratio, measured value, and theoretical value, respectively. The average error percentages of measurement points 1, 2, 3, and 4 are 8.38%, 10.60%, 8.90%, and 11.60%, respectively. The results show that the compressive stress values from the acquisition system are highly accurate.

3 Conclusion

To realize bridge health monitoring, we designed the intelligent bearing system by mearing the force loaded on the bearings. The signal accuracy of the force measuring was verified through experiments. The whole system errors could be eliminated by data obtained from the experiment. The experiments we conducted verified the feasibility of the intelligent bearing, which provides theoretical support for the practical application of the subsequent health monitoring system.