Design System of Structural Health Monitoring System Using

Wireless Sensor Network

Aris Budiyarto, Abyanuddin Salam and Bayan Naufal

Automation and Mechatronics Department, Politeknik Manufaktur Bandung, Bandung, Indonesia

Keywords: SHMS, WSN, Web Based Monitoring, Static Routing, Linear Communication Topology.

Abstract: This research discusses a system design of a Structure Health Monitoring System (SHMS) using Wireless

Sensor Network (WSN) which has successfully performed its main function of reading sensor data from each

node for web based monitoring. The system consists of 4 types of sensor which are temperature humidity

sensor (DHT11), Accelerometer Gyro sensor (GY521), strain sensor (Load Cell), and two kinds of

displacement sensor (TOF and US100). The microcontroller used is WemosD1 mini and ESP32. The system

works with a linear communication topology with a static routing protocol. Active alarms that indicate if the

sensor values exceed the upper or lower limits of the system is successfully carried out. The error of the testing

which carried out by DHT 11 sensor is below 2.3%. While the testing error of the GY521 is below 7%. 0.58%

is the error for load cell. And the overall error of TOF and US100 is below 1%. For latency data reading on

the website at the DHT11, GY521, Load Cell, TOF, and US100 sensors are 4.35%, 1.96%, 4.68%, 2.11%,

and 7.68%. The latency data varies due to data transmission errors from nodes and internet network instability

for the website.

1 INTRODUCTION

Bridges are an example of complex infrastructure in

structures from civil engineering. Infrastructure

cannot be easily predicted in the event of damage or

anomalous conditions. The cause of infrastructure

damage that needs to be considered is the loss of life.

According to research (Amalina, 2016), there are

several bridge infrastructure damage events that

cause casualties, including an incident that occurred

in Nepal on December 25, 2007. On that day, the

Chhinchu Suspension Bridge, which is 187 meters

long, was overloaded as a result resulting in 13 dead

and 32 injured. Furthermore, the collapse of the Kutai

Kertanegara Bridge on November 26, 2011 is an

incident that shows how important monitoring and

scheduled maintenance are on a bridge. The incident

on the Kutai Kertanegara Bridge was caused by a load

that exceeded the limit passing through the bridge

(Arifin, 2014).

These events may be avoided if the party

concerned implements a system with technology that

is able to monitor the condition of the bridge as has

been applied in developed countries. The technology

is the Structural Health Monitoring System (Arifin,

2014). According to research (Hartono, 2001) SHMS

is a system used to observe all matters relating to the

operation and monitoring of structural health

conditions, helping to take corrective action through

manual or automatic commands by several existing

devices. Based on communication, SHMS on bridges

is divided into two types, namely wired and wireless

SHMS technology. This cable-based technology has

the advantage that the data sent back by the sensor is

very accurate and no additional electrical power is

required when the sensor is operating because

electrical power has been provided by the control

center. However, cable-based technology also has

limitations, such as the need for cabling, where

installation is a complicated job.

To reduce the cost of cabling and the number of

components used, the most suitable solution is to use

a Wireless Sensor Network (WSN) in the SHMS

system (Amalina, 2016). WSN is a network

consisting of several sensors in different locations.

This allows sensors to perform monitoring processes

on certain objects and transmit data wirelessly

(Cahya, 2016). WSN is formed from a set of small

autonomous devices with several sensors contained in

it, so this device is called a sensor node (Sutaya,

2019).

Budiyarto, A., Salam, A. and Naufal, B.

Design System of Structural Health Monitoring System Using Wireless Sensor Network.

DOI: 10.5220/0011821400003575

In Proceedings of the 5th International Conference on Applied Science and Technology on Engineering Science (iCAST-ES 2022), pages 561-566

ISBN: 978-989-758-619-4; ISSN: 2975-8246

561

The topology and architecture of a Wireless

Sensor Network (WSN) generally depends on the

geographical area where the sensors are placed. A

Linear Wireless Sensor Network (LWSN) is a special

case, where the physical topology of the network is a

line. The applications of LWSN are diverse, e.g.

monitoring of large infrastructure such as bridges and

dams, road traffic observation, and border control

(Domga, 2019).

So this research will make about "Designing

Structural Health Monitoring System Using Wireless

Sensor Network" as an alternative solution to the

above problems. In this study, bridge supervisors are

made easier to monitor and control the bridge because

it can be monitored via the website, bridge control is

also made easier because monitoring can be real time

so that it is like in research (Abadi, 2020), which has

been able to facilitate officers in maintaining and

supervising building resources remotely, and for the

output of each sensor there will be an alarm on the

website indicating that the bridge condition is critical

according to the threshold that can be adjusted

according to the bridge environment. In WSN

routing, a communication protocol is needed between

nodes. For this protocol was chosen because of

research (Abadi, 2020), in his research using static

routing because in his research it is explained that this

type of routing is suitable for small-scale and non-

moving networks and in finding information about

the intended network is configured manually by the

admin or network manager. In this research, the

network is small-scale and non-moving so it is

suitable to use a static routing protocol and manual

configuration is used to change the path if the sending

node is being turned off by the bridge supervisor or

the sending node is in maintenance.

2 SOLUTION METHOD

This section will explain the system design, system

flow diagram and system architecture.

2.1 System Design

Figure 1: System Overview.

Figure 2.1 describes the system designed for

monitoring data sent from each node installed linearly

as shown in Figure 2.1. The communication uses

radio signals that use Nrf24l01 as the module.

Then the data that has been collected according to

the predetermined path according to Figure 2.1, the

data is forwarded to the server. Once received, the

data continues to the website for monitoring in real

time which can be accessed via laptop or gadget by

the user. Monitoring that can be done includes:

critical condition alarms, real-time sensor readings,

and node alarms that are being turned off by bridge

supervisors or in maintenance.

2.2 System Flowchart

Figure 2: Flowchart of Control System.

iCAST-ES 2022 - International Conference on Applied Science and Technology on Engineering Science

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Figure 2 describes the flow chart of the

microcontroller used to process data from each sensor

collected on the server for monitoring on the

dashboard and database. The first stage is turning on

the power for each sensor node. The next stage is to

check the switch on each sensor node whether it is on

or not, if it is on then the next sensor will

automatically activate and continue the existing data

before the node whose switch is active, otherwise the

node will continue according to the flow chart in

Figure 2.2. Finally, the server that has collected the

data checks the connection to Node-Red, if

connected, the data will be displayed on the

dashboard and database using MySQL.

2.3 System Architecture

Figure 3: System Architecture.

The figure 3 explains the system architecture starting

from the contents of each node to the sensor reading

on the website by the user. The SHMS monitoring

control system uses 5 types of sensors, namely: 3 axis

time of flight sensors and 3 axis ultrasonic for

displacement detection (position movement), GY-

521 sensors for tilt and vibration detection, DHT 11

sensors to detect temperature, and load cells to detect

weight on the bridge. The data will be processed by

the WemosD1 mini microcontroller and ESP32

which gets power from a battery. Furthermore, the

data will be sent with a linear topology and state

routing protocol using a toggle switch. For sending to

the server using Nrf24l01 radio signal.

Then, the data will be received by ESP32 as a

microcontroller on the server. Through the internet

line, the data will proceed to the MQTT Broker

Server and then enter the Node-red which aims to

monitor the system on the dashboard and database,

then the data that has been stored is forwarded to a

website that can be accessed by a gadget or laptop

online. Finally, users can monitor data from sensors

via a laptop or gadget to the website, where what can

be accessed is an alarm when the sensor reading

exceeds the predetermined upper and lower limits and

real-time sensor data readings to see the state of the

bridge in real time. There is also a user who gets

access to be an admin, who can excessively change

the upper and lower limits for each sensor.

3 EXPERIMENTAL RESULTS

AND DISCUSSION

3.1 DHT 11 Sensor Test

Table 1: DHT11 Sensor Test.

DHT11 HTC-01 Error( %)

Temp

)

Humidity

(%)

Temp

)

Humidity

(%) Temp

Humidity

24,7 81 24,3 83 1,65 2,41

25,7 73 25,1 75 2,39 5,67

3 9,1 61 9,2 63 1,09 3,17

4 24,4 87 23,2 92 5,17 5,43

5 29,4 50 30 43 2 16,28

6 25 74 24,1 80 3,73 7,5

7 25,3 76 24,2 79 4,55 3,8

8 24,9 73 24,5 78 1,63 6,41

9 24,6 75 24,6 78 0 3,85

10 24,4 74 24,6 77 0,81 3,9

Average Error 2.3 2.27

In this test, it is carried out to determine the

uncertainty value of a device that will be used. The

value obtained from this test is the error value of the

DHT 11 sensor reading compared to the HTC-01

value as a benchmark.

The value obtained during this 10-time test is done

by comparing the DHT 11 value through the same

ambient and room temperature. There is an average

error value of 2.3% for temperature and 2.27% for

humidity error.

3.2 GY-521 Sensor Tilt Test

This test was conducted to determine the error value

of the GY-521 sensor for pitch and roll tilt readings.

The error value is obtained from the GY-521 sensor

reading compared to the protractor as a measuring

instrument.

Testing is done by entering 9 parameter values for

the angle, where each angle will be tested 3 times to

get the pitch and roll error values.

The values obtained are an average error of 4.07%

for the pitch angle and 6.96% for the roll angle error.

3.3 GY-521 Sensor Frequency Test

This test was conducted to determine the error value

of the GY-521 sensor for frequency readings. The

error value is obtained from the GY-521 sensor

reading which is compared with a gadget application

called Vibrations as a measuring tool.

Design System of Structural Health Monitoring System Using Wireless Sensor Network

563

Table 2: Test of GY-521 Sensor tilt measurement.

Table 3: Test of GY-521 Sensor Frequency measurement.

Freq (Hz) GY-521(Hz) Error (%)

1 7 7,43 6,14

2 5,04 5,16 2,38

3 4,42 4,92 11,31

4 5,11 5,58 9,2

5 4,3 4,37 1,63

6 6 6,98 16,33

7 5,32 5,38 1,13

8 6,2 6,52 5,16

9 1,7 1,58 7,06

10 5,9 5,98 1,36

Avera

e Error 6,17

The test was carried out by entering 10 times the

input obtained was an average error of 6.17% for the

GY-521 sensor frequency.

3.4 Load Cell Sensor Test

In this test, it was carried out to determine the error

value of the Load Cell sensor for weight reading. The

error value is obtained from the GY-521 sensor

reading which is compared to the scale as the

measuring instrument.

Table 4: Test of Load Cell Sensor weight measurement.

Weight

(gram)

Test No.

Load Cell

Sensor

Error (%)

11,4 1 11,46 0,53%

2 11,48 0,70%

3 11,46 0,53%

4 11,43 0,26%

5 11,44 0,35%

23,7 1 23,58 0,51%

2 23,56 0,59%

3 23,51 0,80%

4 23,63 0,30%

5 23,59 0,46%

8,5 1 8,5 0,00%

2 8,45 0,59%

3 8,51 0,12%

4 8,5 0,00%

5 8,52 0,24%

18,1 1 18,22 0,66%

2 18,2 0,55%

3 18,26 0,88%

4 18,2 0,55%

5 18,26 0,88%

10,9 1 10,77 1,19%

2 10,81 0,83%

3 10,78 1,10%

4 10,79 1,01%

5 10,8 0,92%

Avera

e Error 0,58%

The test is conducted by entering 5 values for

weight in grams, where each angle will be tested 5

times to get the weight error value.

The value obtained is an average error of 0.58%

for the Load Cell sensor.

3.5 TOF and US-100 Sensor Testing

This test was conducted to determine the error value

of the readings of the 3 TOF and US-100 sensors

compared to the ruler as a test tool.

Table 5: TOF Sensor Testing and US-100 distance

measurement.

Distance

(mm)

Test

No.

TOF Sensor US-100 Sensor

X-Axis

Y-

Axis

Z-

Axis

X-

Axis

Y-

Axis

Z-

Axis

1 100 1 96 101 100 100 94 96

2 95 98 97 102 96 100

2 200 1 205 205 198 201 200 199

2 201 200 204 200 204 201

3 300 1 295 303 304 299 301 300

2 301 304 299 303 296 297

4 400 1 402 400 403 403 393 400

2 401 397 400 405 403 400

5 500 1 500 502 497 495 500 502

2 502 499 502 503 501 496

6 600 1 601 599 604 603 602 598

2 599 597 601 602 600 595

7 700 1 700 706 710 712 699 698

2 704 703 708 704 702 706

8 800 1 803 801 796 803 803 817

2 805 798 804 805 799 800

9 900 1 904 898 903 905 904 910

2 894 903 898 902 901 896

10 1000 1 1010 1020 1005 1014 1004 1008

2 1005 1007 1012 1009 1006 1001

Average Error 0,98% 0,74% 0,80% 0,73% 0,96% 0,73%

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Tests were carried out by entering 10 values for the

distance in mm, where each distance will be tested

twice to get the distance error value for 3 TOF sensors

x, y, z axis and 3 US-100 sensors x, y, z axis.

The values obtained are the average error for

TOF x, y, and z axes which are 0.98%, 0.74%, and

0.8%. As for the US-100 x, y, and z axes, the reading

errors are 0.73%, 0.96%, and 0.73%.

3.6 Latency Testing of Data Reading

for Each Node on the Website

Testing is done to get the latency or delay reading

value for each node that is read in the Node-red

dashboard. The reading is done using a stopwatch

available on the gadget, where the stopwatch is turned

on when the data is read until the next data is read.

Table 6: Latency Testing of Data Reading for Each Node

on the Website.

Test

No.

Delay (second)

Node

DHT-11

Node

GY-521

Node Load

Cell

Node

TOF

Node

US-100

1 1 2,16 0,63 3,6 0,59 8,98

2 2 10,28 4,43 5,38 0,98 4,93

3 3 1,67 0,88 2,25 0,69 3,26

4 4 1,32 0,39 8,17 3,27 6,74

5 5 5,41 4 4,01 0,44 6,43

6 6 2,43 0,5 1,94 3,32 8,68

7 7 2,88 3,93 3,91 2,91 21,02

8 8 9,88 1,01 6,75 5,02 3,43

9 9 5,1 2,15 7,9 2,91 4,37

10 10 2,41 2,21 2,87 0,95 8,92

Average

Error

4,35 2,01 4,68 2,11 7,68

Testing was carried out 10 times and there were 5

nodes, namely DHT 11, GY-521, Load Cell, TOF,

and US-100 nodes where the average latency in order

was 4.35%, 1.96%, 4.68%, 2.11%, and 7.68%.

3.7 Latency Testing of Switch Alarm

Reading for Each Node on the

Website

Testing is done to get the latency value or reading

delay for the switch alarm for each node that reads the

alarm on the Node-red dashboard which indicates the

switch is doing its job properly. The reading is done

using a stopwatch available on the gadget, where the

stopwatch is turned on when the switch is turned on

until the alarm is read.

Testing was carried out 10 times and the nodes

that used the switch were 5 nodes, namely the DHT

11, GY-521, Load Cell, TOF, and US-100 nodes

where the average latency readings in order were

8.4%, 17.645%, 9.847%, 11.06%, and 16.244%.

Table 7: Latency Data & Transmission.

Delay (second)

Nod

T-

Des

Nod

GY-

521

Des

Node

Load

Cell

scr

ipt

Nod

TOF

cri

pti

Node

US-

100

scr

ipt

1 1

6,6

23,9

S 24,89 S

12,5

S 3,94 S

2 2

6,5

29,8

S 15,38 S

36,9

S 6,98 S

3 3

12,

11,0

S 1,35 S

23,0

27,4

4 4

9,1

23,2

S 3,74 S 2,78 S 6,65 S

5 5

5,0

S 5,5 S 31,76 S 4,28 S 2,79 S

6 6 4,5 S 17,2 S 2,44 S 9,09 S

31,4

7 7

15,

35,7

S 5,07 S 4,71 S

12,1

8 8

5,9

13,8

S 6,77 S 6,32 S

50,1

9 9

12,

S 6,8 S 3,6 S 6,93 S

14,8

5,3

S 9,27 S 3,47 S 3,97 S 6,02 S

Avera

8,40 17,65 9,85 11,06 16,24

S = Succesfull

US = Unsuccesfull

4 CONCLUSIONS

Based on the results of the design and testing of the

monitoring system for bridge health, it has

successfully performed its main function, namely

reading sensor data from each node for monitoring

and reading the switch alarm which is a static routing

protocol system that is used if the node is turned off

due to problems or maintenance, then there is also

history data on the dashboard of each node which is

intended for bridge supervisors and bridge engineers

to view past data. Where when testing the error from

the DHT 11 sensor is below 2.3%. Then for the GY-

521 error which is below 7%. Furthermore, the Load

Cell sensor has an error of 0.58%. And the last sensor

test is the distance on TOF and US-100 where the

overall error is below 1%.

For data reading latency at each node on the

website at nodes DHT 11, GY-521, Load Cell, TOF,

and US-100 where the average latency in order is

4.35%, 1.96%, 4.68%, 2.11%, and 7.68% then the

latency of the switch alarm reading data is node DHT

11, GY-521, Load Cell, TOF, and US-100 where the

average latency readings in order are 8.4%, 17.645%,

9.847%, 11.06%, and 16.24% where latency data

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varies due to data transmission errors from nodes and

internet network instability for the website.

REFERENCES

N. Amalina, E. Setijadi, and Suwadi. (2016). “Desain

Topologi Komunikasi Wireless Sensor Network (Wsn)

Pada Aplikasi Sistem Structural Health Monitoring

(Shm) Jembatan,” Jurnal Ilmu Komputer dan DKV,

vol. 1, no. 1, pp. 1–13.

M. Z. Arifin. (2014). ”Implementasi Protokol Ad-Hoc Pada

Wsn Module Untuk Shms Pada Jembatan Bentang

Panjang ,” Tugas Akhir, Institut Teknologi Sepuluh

Nopember.

J. Hartono and U. Khoiroh. (2001). “Evaluasi Rencana

Pemasangan Sensor Structure Health Monitoring

System Jembatan Pulau Balang Ii,” Teras Jurnal, vol.

11, no. 2, pp. 1–14.

P. Cahya. (2016). ”Implementasi Dan Pengujian Sistem

Monitoring Kesehatan Struktur Pada Jembatan

Surabaya-Madura,” Tugas Akhir, Institut Teknologi

Sepuluh Nopember.

I. W. Sutaya, K. U. Ariawan, and D. G. H. Divayana.

(2019). “Implementasi Jaringan WSN Mesh Berbasis

Radio Wireless nRF24l01 Pada Sistem Meteran Air

Rumah Kos,” Jurnal EECCIS, vol. 13, no. 1, pp. 1–6.

R. K. Domga, R. Stanica, M. Tchuente, and F. Valois.

(2019). “Sensor deployment in wireless sensor

networks with linear topology using virtual node

concept,” Wireless Networks, vol. 25, no. 8, pp. 4947–

4962.

S. C. Abadi, A. Prafanto, M. Eriyadi, A. Suryadi, and G. D.

Ramadi. (2020). “PENERAPAN PROTOKOL

PUBLISH / SUBSCRIBE MESSAGING SYSTEM

PADA SISTEM WIRELESS BUILDING NETWORK

BERBASIS NRF24L01,” JTT (Jurnal Teknologi

Terapan), vol. 6, no. 2, p. 172.

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