Development of the Maturity Sensor Prototype and Method of Its Placement in the Structure

Maturity sensors are used to determine concrete strength by the non-destructive method. The method of placement of the maturity sensors determines their number required for a certain frame of a monolithic building. This paper proposes a cheap prototype of an embedded wireless sensor for monitoring concrete structures, as well as an alternative strategy for placing sensors based on the transitional boundaries of the temperature distribution of concrete curing, which were determined by building a heat map of the temperature distribution, where unknown values are calculated by the method of inverse distance weighing. The developed prototype can simultaneously measure temperature and relative humidity over a smartphone-controlled time interval. It implements a maturity method to assess the in-situ strength of concrete, which is considered an alternative to the traditional shock impulse and compression testing method used in Kazakhstan. The prototype was tested in laboratory and field conditions. The tests were aimed at studying the effect of internal and external temperature and relative humidity on concrete's strength gain. Based on an experimentally poured concrete slab with randomly integrated maturity sensors, it the transition boundaries form elliptical forms were determined. Temperature distribution over the largest diameter of the ellipses was plotted, resulting in correct and inverted parabolas. As a result, the distance between the closest opposite crossing points of the parabolas is accepted as the maximum permissible step for setting the maturity sensors. The proposed placement strategy can be applied to sensors that measure various continuous phenomena such as relative humidity. Prototype testing has also revealed Bluetooth inconvenience due to weak signal and inability to access multiple prototypes simultaneously. For this reason, further prototype upgrades are planned in the future work.





References:
[1] Kibar H., Ozturk T. Determination of concrete quality with destructive and non-destructive methods // Comput. Concr. Techno-Press, 2015. Vol. 15, № 3. P. 473–484.
[2] Malek J., Kaouther M. Destructive and non-destructive testing of concrete structures // Jordan J. Civ. Eng. Jordan University of Science and Technology: Deanship of Research, 2014. Vol. 159, № 3269. P. 1–10.
[3] Putman B.J., Neptune A.I. Comparison of test specimen preparation techniques for pervious concrete pavements // Constr. Build. Mater. Elsevier, 2011. Vol. 25, № 8. P. 3480–3485.
[4] Thandavamoorthy T.S. Determination of concrete compressive strength: A novel approach // Pelagia Res. Libr. Adv. Appl. Sci. Res. 2015. Vol. 6, № 10. P. 88–96.
[5] Rehman S.K.U. et al. Nondestructive test methods for concrete bridges: A review // Constr. Build. Mater. Elsevier, 2016. Vol. 107. P. 58–86.
[6] Helal J., Sofi M., Mendis P. Non-destructive testing of concrete: A review of methods // Electron. J. Struct. Eng. 2015. Vol. 14, № 1. P. 97–105.
[7] Erdal H. et al. Prediction of concrete compressive strength using non-destructive test results // Comput. Concr. Techno-Press, 2018. Vol. 21, № 4. P. 407–417.
[8] Lim Y.Y. et al. Non-destructive concrete strength evaluation using smart piezoelectric transducer—A comparative study // Smart Mater. Struct. IOP Publishing, 2016. Vol. 25, № 8. P. 85021.
[9] Hannan M.A., Hassan K., Jern K.P. A review on sensors and systems in structural health monitoring: Current issues and challenges // Smart Struct. Syst. Techno Press, 2018. Vol. 22, № 5. P. 509–525.
[10] Dutta S., Samui P., Kim D. Comparison of machine learning techniques to predict compressive strength of concrete // Comput. Concr. Techno-Press, 2018. Vol. 21, № 4. P. 463–470.
[11] Apostolopoulour M. et al. Prediction of compressive strength of mortars using artificial neural networks // Proceedings of the 1st international conference TMM_CH, transdisciplinary multispectral modelling and cooperation for the preservation of cultural heritage, Athens, Greece. 2018. P. 10–13.
[12] Gazder U. et al. Predicting compressive strength of bended cement concrete with ANNs // Comput. Concr. Techno-Press, 2017. Vol. 20, № 6. P. 627–634.
[13] Fick G.J. et al. Field reference manual for quality concrete pavements. 2012.
[14] Utepov Y.B. et al. Maturity sensors placement based on the temperature transitional boundaries. // Mag. Civ. Eng. 2019. Vol. 90, № 6. P. 93–103.
[15] Taheri S. A review on five key sensors for monitoring of concrete structures // Constr. Build. Mater. Elsevier, 2019. Vol. 204. P. 492–509.
[16] Ge Z., Wang K. Modified heat of hydration and strength models for concrete containing fly ash and slag // Comput. Concr. Techno-Press, 2009. Vol. 6, № 1. P. 19–40.
[17] Zemajtis J.Z. Role of concrete curing // Portl. Cem. Assoc. Skokie. 2014.
[18] Anwar Hossain K.M. Influence of extreme curing conditions on compressive strength and pulse velocity of lightweight pumice concrete // Comput. Concr. Techno-Press, 2009. Vol. 6, № 6. P. 437–450.
[19] Chen H.-J., Yang T.-Y., Tang C.-W. Strength and durability of concrete in hot spring environments // Comput. Concr. 2009. Vol. 6, № 4. P. 269–280.
[20] Zhang B., Cullen M., Kilpatrick T. Spalling of heated high performance concrete due to thermal and hygric gradients // Adv. Concr. Constr. 2016. Vol. 4, № 1. P. 1–14.
[21] Farzampour A. Temperature and humidity effects on behavior of grouts // Adv. Concr. Constr. 2017. Vol. 5, № 6. P. 659.
[22] Kessler F., Battersby S. Working with Map Projections: A Guide to Their Selection. CRC Press, 2019. 65 p.
[23] Nath P., Hu Z., Mahadevan S. Sensor placement for calibration of spatially varying model parameters // J. Comput. Phys. Elsevier, 2017. Vol. 343. P. 150–169.
[24] Weighting I.D. Interpolation // GISGeography URL https//gisgeography. com/inverse-distance-weighting-idw-interpolation/(датаобращения 27.06. 2019).