Use of the Arduino Platform for the Piezoresistivity Analysis in Self-Sensing Cement Composites

Silva, João Batista Lamari Palma e; Lintz, Rosa Cristina Cecche; Gachet, Luísa Andréia

doi:10.1590/1980-5373-MR-2022-0520

João Batista Lamari Palma e Silva ^**e-mail: lamaripalma@hotmail.com

Universidade Estadual de Campinas, Faculdade de Tecnologia, Programa de Pós-Graduação em Tecnologia, Limeira, SP, Brasil.

http://orcid.org/0000-0001-7115-6385

Rosa Cristina Cecche Lintz

Universidade Estadual de Campinas, Faculdade de Tecnologia, Programa de Pós-Graduação em Tecnologia, Limeira, SP, Brasil.

http://orcid.org/0000-0002-7201-3260

Luísa Andréia Gachet

Universidade Estadual de Campinas, Faculdade de Tecnologia, Programa de Pós-Graduação em Tecnologia, Limeira, SP, Brasil.

http://orcid.org/0000-0002-1661-2605

*e-mail: lamaripalma@hotmail.com

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Figure 1
Scheme of self-sensing cement without load (a); with load compression (b).

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Figure 2
Electrical resistance measurement scheme in a piezoresistive specimen with embedded electrodes.

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Figure 3
Circuit for measuring electrical resistance in piezoresistive composites.

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Figure 4
Flow of experimental stages.

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Figure 5
Sample of raw materials utilized in mortar production (measurement in millimeters).

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Figure 6
Granulometric distribution (standard NBR 17054⁵⁸58 ABNT: Associação Brasileira de Normas Técnicas. ABNT NBR 17054: aggregates: determination of granulometric composition: test method. Rio de Janeiro: ABNT; 2022.) of sand and graphite particle sizes.

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Figure 7
Mortar and specimen preparations.

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Figure 8
Specimens with electrodes and strain gauges installed.

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Figure 9
Setup of compression tests.

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Figure 10
Prototype hardware design with Fritzing software.

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Figure 11
Hardware of the prototype developed.

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Figure 12
Information flow in the app (a) and the app's data acquisition screen (b).

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Figure 5
Sample of raw materials utilized in mortar production (measurement in millimeters).

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Figure 6
Granulometric distribution (standard NBR 17054⁵⁸58 ABNT: Associação Brasileira de Normas Técnicas. ABNT NBR 17054: aggregates: determination of granulometric composition: test method. Rio de Janeiro: ABNT; 2022.) of sand and graphite particle sizes.

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Figure 7
Mortar and specimen preparations.

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Figure 8
Specimens with electrodes and strain gauges installed.

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Figure 9
Setup of compression tests.

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Figure 10
Prototype hardware design with Fritzing software.

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Figure 11
Hardware of the prototype developed.

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Figure 12
Information flow in the app (a) and the app's data acquisition screen (b).

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Figure 13
Arduino without complementary module in compression test with mix proportions T0 (a) and TG (b).

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Figure 14
Arduino with ADS1115 module in compression test with mix proportions T0 (a) and TG (b).

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Figure 15
Arduino with HX711 module in compression test with mix proportions T0 (a) and TG (b).

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Figure 16
Tukey test between Arduino-ADS1115 (a), Arduino-HX711 (b), and ADS1115-HX711 (c).

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Figure 17
Correlations analysis (Pearson’s r and R-Square) with different devices (Arduino, ADS1115 and HX711).

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Figure 18
Graphical comparison between correlation analyses with different devices (Arduino, ADS1115 and HX711).

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Figure 19
Polarization effect between prototype and reference DAQ with mortar T0.

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Figure 20
Variation of the polarization effect in an interval of 10 seconds with mortar T0.

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Figure 21
Polarization effect between prototype and reference DAQ with mortar TG.

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Figure 22
Variation of the polarization effect in an interval of 10 seconds with mortar TG.

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Figure 23
Comparative results between FCR and mechanical parameters, using the reference DAQ.

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Figure 24
Comparative results between FCR and mechanical parameters, using the developed prototype and the reference DAQ.

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Figure 25
Tukey Test between prototype and reference DAQ.

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Figure 26
Correlations analysis (Pearson’s r and R-Square) between prototype and reference DAQ.

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Figure 27
Graphical comparison between correlation analyses of the prototype and reference DAQ.

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Table 1
Applications with Arduino in SHM.

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Table 2
Mix proportions of the mortar in terms of cement mass.

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Table 3
Physical properties of the raw materials.

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Table 4
Sensitivity analysis with different devices (Arduino, ADS1115 and HX711).

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Table 5
ANOVA between different devices (Arduino, ADS1115 and HX711).

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Table 6
Sensitivity analysis with prototype and reference DAQ.

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Table 7
ANOVA between prototype and reference DAQ.

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Arduino model	Application	Quantity properties	Reference
Genuino	Concrete slab	Acceleration (vibration)	Nunes et al.¹⁴14 Nunes DS, Brito JLV, Doz GN. A low-cost data acquisition system for dynamic structural identification. IEEE Instrum Meas Mag. 2019;22(5):64-72.
Mega	Concrete beams	Strain, force and displacement	Silva et al.¹⁵15 Silva JBLP, Jacintho AEPGA, Forti NCS, Pimentel LL, Branquinho OC. Desenvolvimento de sistema de baixo custo para monitoramento de integridade estrutural. Materia. 2019;24(4):e12528.
Nano	Metallic stair	Humidity, temperature, acceleration (vibration), strain and impact	Malik et al.¹⁶16 Malik H, Khattak KS, Wiqar T, Khan ZH, Altamimi AB. Low cost internet of things platform for structural health monitoring. In: 2019 22nd International Multitopic Conference (INMIC); 2019; Islamabad, Pakistan. Proceedings. New York: IEEE; 2019. p. 1-7.
Uno	Small-scale bridge model	Acceleration (vibration)	Patel et al.¹⁷17 Patel T, Jain A, Gameti D, Patel U, Parekh R. Remote structural health monitoring system for bridges. In: IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS); 2020; Bhopal, India. Proceedings. New York: IEEE; 2020. p. 1-4.
Uno	Railway bridge	Acceleration (vibration)	Ozdagli et al.¹¹11 Ozdagli AI, Liu B, Moreu F. Low-cost, efficient wireless intelligent sensors (LEWIS) measuring real-time reference-free dynamic displacements. Mech Syst Signal Process. 2018;107:343-56.
Uno	Concrete beams	Acceleration (vibration)	Khan et al.¹⁸18 Khan SM, Hanif MU, Khan A, Hassan MU, Javanmardi A, Ahmad A. Damage assessment of reinforced concrete beams using cost-effective MEMS accelerometers. Structures. 2022;41:602-18.
untold	Joint in steel frame	Acceleration (vibration)	Serdjuks et al.¹⁹19 Serdjuks D, Kurtenoks V, Tatarinovs A, Buka-Vaivade K, Lapkovskis V, Mironovs V, et al. Non-model vibration analysis method for health monitoring of structural joints. Procedia Struct Integr. 2022;37:555-62.
ProMini	Concrete blocks	Sonic speed of propagation	Misra et al.²⁰20 Misra D, Das G, Das D. An IoT based building health monitoring system supported by cloud. J Reliab Intell Environ. 2020;6(3):141-52.
Due	Aluminium cantilever	Acceleration (vibration)	Buckley et al.²¹21 Buckley T, Ghosh B, Pakrashi V. Edge Structural Health Monitoring (E-SHM) using low-power wireless sensing. Sensors. 2021;21(20):6760.

ID	Cement	Sand	w/c ratio	Graphite
T0	1	4	0.50	0
TG	1	4	1.05	0.25

Properties	Cement	Sand	Graphite
Specific mass [g/cm³]	2.95^a a NBR 1660556;	2.64^b b NBR 1691657;	2.41^a
Unit weight [g/cm³]	0.91^d d No standardization;	1.48^c c Chapman vessel;	0.38^d
Maximum diameter [mm]	-	4.80^e e NBR 1705458	0.15^e
Fineness modulus	-	2.03^e	0.25^e

Device	Sample	Figure		Peak Value
Device	Sample	Figure	Time (s)	Δ Stress (MPa)	Δ Bytes	Sensitivity Equation 6 (Bytes/MPa)
Arduino	T0	13a	12.3	1.39	24	17.3
			28.3	1.31	26	19.9
			42.7	1.64	34	20.8
	TG	13b	24.3	1.22	31	25.4
			38.7	1.06	37	35.0
			60.6	1.26	29	23.1
ADS1115	T0	14a	22.1	1.22	1,941	1,585.4
			36.1	1.32	1,926	1,459.8
			49.8	1.43	2,430	1,699.2
	TG	14b	24.8	1.23	6,810	5,534.5
			41.6	1.32	7,390	5,583.3
			56.3	1.31	7,574	5,803.0
HX711	T0	15a	17.8	1.24	592,736	476,522.1
			36.3	1.40	635,674	454,477.5
			52.3	1.42	609,341	428,604.6
	TG	15b	14.4	1.49	2,086,021	1,404,446.1
			28.0	1.42	1,940,115	1,362,089.5
			42.5	1.42	1,946,317	1,370,129.0

Comparison		Degree of freedom	Sum of Squares	Mean Square	F ^* * F is the ratio between Mean Squares	p-value
*Arduino vs.* ADS1115**	Model	3	2.11059E7	6.33176E7	2423.578	3.5896E-12
*Arduino vs.* ADS1115**	Error	8	8708.56192	69668.49538	2423.578	3.5896E-12
*Arduino vs.* HX711**	Model	3	3.80263E12	1.26754E12	4688.935	2.5680E-13
*Arduino vs.* HX711**	Error	8	2.16261E9	2.70326E8	4688.935	2.5680E-13
*HX711 vs.* ADS1115**	Model	3	3.78298E12	1.26099E12	4664.552	2.6221E-13
*HX711 vs.* ADS1115**	Error	8	2.16268E9	2.70335E8	4664.552	2.6221E-13

Device	Sample	Figure		Peak Value		Sensitivity Equation 7 (MPa^-1)
Device	Sample	Figure	Time (s)	Δ Stress (MPa)	FCR (%)	Sensitivity Equation 7 (MPa^-1)
Reference	T0	23a	20.0	0.44	259.68	5.97
			33.8	0.48	329.52	6.90
			52.1	0.54	469.09	8.76
	TG	23c	10.5	0.70	843.04	12.03
			25.6	0.64	708.38	11.10
			40.9	0.38	426.75	11.15
Prototype	T0	24a	11.4	0.66	20.39	0.31
			20.4	0.81	30.16	0.37
			29.6	0.67	24.64	0.37
	TG	24c	38.0	0.49	11.98	0.24
			58.5	0.83	22.00	0.27
			85.2	0.83	21.47	0.26

Comparison		Degree of freedom	Sum of Squares	Mean Square	F ^* * F is the ratio between Mean Squares	p-value
*Prototype vs.* Ref. DAQ**	Model	3	270.37094	90.12365	157.0501	1.89956E-7
*Prototype vs.* Ref. DAQ**	Error	8	4.59082	0.57385	157.0501	1.89956E-7

[1] *e-mail: lamaripalma@hotmail.com