Application of Edge Computing in Structural Health Monitoring of Simply Supported PCI Girder Bridges

Abstract

This study proposes an innovative method for structural health monitoring of simply supported PCI girder bridges based on dynamic strain and edge computing. Field static and dynamic load tests were conducted on a bridge consisting of a span with newly replaced PCI girders and numerous spans with old PCI girders. Both the static and dynamic test results showed that the flexural rigidity of the old PCI girders decreased significantly due to deterioration. To improve the efficiency of on-site monitoring data transmission and data analysis, this study developed a smart dynamic strain gauge node with the function of edge computing. Continuous data with a sampling frequency of 100 Hz were computed at the sensor node. Among the computed results, only the maximum dynamic strain data caused by the passage of the heaviest vehicle within 1 min were transmitted. The on-site monitoring results indicated that under routine traffic conditions, the dynamic strain response of the new PCI girder was smaller than that of the deteriorated PCI girder. When the monitored dynamic strain response has a tendency to magnify, attention should be paid to the potential prestress loss or other deterioration behaviors of the bridge.

Keywords: dynamic strains; edge computing; flexural rigidity; prestressed concrete I girders; structural health monitoring.

Figure 1

Photograph of the investigated bridge: (a) side view; (b) cracks along the tendon duct.

Figure 2

Schematic of the static test: (a) elevated view; (b) section A-A view.

Figure 3

Schematic of the deployment of two strain gauges for the static test.

Figure 4

Strain response of the seventh northward span under the static test.

Figure 5

Strain responses of northward spans during the static test: (a) Span 4; (b) Span 5; (c) Span 6; (d) Span 8.

Figure 6

Schematic of the moving vehicle test.

Figure 7

The deployment of the strain gauge for the dynamic test.

Figure 8

Dynamic strain response of the G5 girder of the seventh span when the vehicle passed over the girder.

Figure 9

Dynamic strain responses of the G5 girders of the northward spans when the vehicle passed over the girders: (a) Span 4; (b) Span 5; (c) Span 6; (d) Span 8.

Figure 10

Smart node: (a) Wheatstone bridge; (b) data acquisition circuit; (c) smart node configuration.

Figure 11

Dynamic strain of the investigated bridge: (a) continuous data output; (b) outputting of a maximum value every minute.

Figure 12

Schematic of the monitoring architecture of the wireless dynamic strain gauge based on NB-IoT that was used in this study.

Figure 13

Verification of the functionality of the smart nodes of the wireless dynamic strain gauge: (a) strain gauge installation; (b) two smart nodes used in the functionality verification.

Figure 14

Results of dynamic strain verification: (a) output of continuous waveforms; (b) output of edge computing.

Figure 15

Dynamic strain monitoring device: (a) on-site installation; (b) solar charging panels with rechargeable batteries.

Figure 16

Vehicle loading test: (a) image of the vehicle, which weighed 38.46 metric tons, and (b) photo of the vehicle when it moved on the bridge.

Figure 17

Vehicle loading test: strain responses of the (a) fifth and (b) seventh spans.

Figure 18

Dynamic strain response caused by the passage of vehicles over the fifth span of the investigated bridge.

Figure 19

Dynamic strain response caused by the passage of vehicles over the seventh span of the investigated bridge.