Detection of structural changes in concrete using embedded ultrasonic sensors based on autoregressive model
Joyraj Chakraborty 1  
,  
 
 
More details
Hide details
1
NeoStrain Sp. z o.o.
2
Silesian University of Technology
CORRESPONDING AUTHOR
Andrzej Katunin   

Silesian University of Technology
Online publish date: 2018-12-17
Publish date: 2018-12-17
Submission date: 2018-07-27
Final revision date: 2018-11-29
Acceptance date: 2018-12-14
 
Diagnostyka 2019;20(1):103–110
KEYWORDS
TOPICS
ABSTRACT
Embedded ultrasonic transmission measurements can be a cost effective and more user-friendly alternative in comparison to commonly used structural health monitoring systems used in civil engineering to detect operational or environmental changes in structure. They can be used to detect small structural changes in large concrete structures without necessity of placing a sensor on the spot where the changing is taking place. This paper presents the investigations on the possibility of utilising autoregressive model, where the velocity of ultrasonic wave in a medium is dependent on the operational state. The goal is to use the model for localization of operational changes in the large concrete structure by means of embedded ultrasonic transducer networks. In this study, several static load tests and dynamic test on large reinforced concrete beams have been performed using embedded ultrasonic sensors. Using the autoregressive model it is possible to localize operational changes in the concrete structure. The proposed approach of diagnostic signal processing allows for precise evaluation of structural changes in concrete.
 
REFERENCES (24)
1.
Ceylan H. Use of smart sensor systems for health monitoring of the transportation infrastructure system. In proceedings of the 3rd international conference on transportation infrastructure, Pisa, Italy. 2014: 22-25.
 
2.
Yanhua S, Yihua K, Chen Q. A new NDT method based on permanent magnetic field perturbation. NDT & E International. 2011; 44(1): 1-7. https://doi.org/10.1016/j.ndte....
 
3.
Deng L, Cai CS. Applications of fiber optic sensors in civil engineering. Structural Engineering and Mechanics. 2007; 25(5): 577-596. https://doi.org/10.12989/sem.2....
 
4.
Yu X, Kwon EA. Carbon nanotube/cement composite with piezoresistive properties. Smart Materials and Structures. 2009; 18(5): 055010. https://doi.org/10.1088/0957-4....
 
5.
Niederleithinger E, Wolf J, Mielentz F, Wiggenhauser H, Pirskawetz S. Embedded ultrasonic transducers for active and passive concrete monitoring. Sensors. 2015; 15(5): 9756-9772. https://doi.org/10.3390/s15050....
 
6.
Birks AS, Green RE, McIntire P, Eds. The non-destructive testing handbook, Vol. 7. Ultrasonic Testing, 2nd ed. Columbus, OH: American Society for Non-Destructive Testing; 1991.
 
7.
Fröjd P, Ulriksen P. Frequency selection for coda wave interferometry in concrete structures. Ultrasonics. 2017; 80(1): 1-8. https://doi.org/10.1016/j.ultr....
 
8.
Marioli D, Narduzzi C, Offelli C, Petri D, Sardini E, Taroni A. Digital time of flight measurement for ultrasonic sensors. IEEE Transactions on Instrumentation and Measurement. 1992; 41(1): 93-97. https://doi.org/10.1109/19.126....
 
9.
Grennberg A, Sandell M. Estimation of subsample time delay differences in narrowband ultrasonic echoes using the Hilbert transform correlation. IEEE transactions on Ultrasonics, Ferroelectrics, and Frequency Control. 1994; 41(5): 588-595. https://doi.org/10.1109/58.308....
 
10.
Nair KK, Kiremidjian SA, Law HK. Time series-based damage detection and localization algorithm with application to the ASCE benchmark structure. Journal of Sound and Vibration 2006; 29(1): 349-368. https://doi.org/10.1016/j.jsv.....
 
11.
Li WK, Tong H. Time series: advanced methods. International Encyclopedia of the Social and Behavioural Sciences. 2001:15699-15704. https://doi.org/10.1016/B0-08-....
 
12.
Larose E, Obermann A, Digulescu A, Planes T, Chaix JF, Mazerolle F, Moreau G. Locating and characterizing a crack in concrete with diffuse ultrasound: A four-point bending test. The Journal of the Acoustical Society of America. 2015; 138(1): 232-241. https://doi.org/ 10.1121/1.4922330.
 
13.
Bogas AJ, Gomes MG, Gomes A. Compressive strength evaluation of structural lightweight concrete by non-destructive ultrasonic pulse velocity method. Ultrasonics 2013; 53(5): 962-972. https://doi.org/10.1016/j.ultr....
 
14.
Wu TT, Liu PL. Advancement on the nondestructive evaluation of concrete using transient elastic waves. Ultrasonics. 1998; 36(1): 197-204. https://doi.org/10.1016/S0041-....
 
15.
Krautkramer J, Krautkramer H. Ultrasonic Testing of Materials, 4th ed. Berlin, Heidelberg: Springer; 1990. https://doi.org/10.1007/978-3-....
 
16.
Grêt AA, Snieder R, Scales J. Time-Lapse monitoring of rock properties with coda wave interferometry. Journal of Geophysical Research: Solid Earth 2006; 111(B3): 0148-0227. https://doi.org/10.1029/2004JB....
 
17.
Lu Y, Michaels EJ. A methodology for structural health monitoring with diffuse ultrasonic waves in the presence of temperature variations. Ultrasonics. 2005; 43(2): 717-731. https://doi.org/10.1016/j.ultr....
 
18.
Fugate M, Sohn H, Farrar CR. Vibration-based damage detection using statistical process control. Mechanical Systems and Signal Processing. 2001; 15(4): 707-721. https://doi.org/10.1006/mssp.2....
 
19.
Kewalramani AM, Gupta R. Concrete compressive strength prediction using ultrasonic pulse velocity through artificial neural networks. Automation in Construction. 2006; 5(3): 374-379. https://doi.org/10.1016/j.autc....
 
20.
Michaels JE, Michaels TE. Detection of structural damage from the local temporal coherence of diffuse ultrasonic signals. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control. 2005; 52(10): 1769-1782. https://doi.org/10.1109/TUFFC.....
 
21.
Sohn H, Czarnecki J, Farrar CR. Structural health monitoring using statistical process control. Journal of Structural Engineering. 2000; 126(11): 1356-1363. https://doi.org/10.1061/(ASCE)....
 
22.
Allen D, Sohn H, Worden K, Farrar C. Utilizing the sequential probability ratio test for building joint monitoring. Proc. SPIE 2002; 4704: 1-11. https://doi.org/10.1117/12.470....
 
23.
Clark AG. Cable damage detection using time domain reflectometry and model-based algorithms. Lawrence Livermore National Laboratory 2008; document No. LLNL-CONF-402567.
 
24.
Figueiredo E, Figueiras J, Park G, Farrar CR, Worden K. Influence of the autoregressive model order on damage detection. Computer-Aided Civil and Infrastructure Engineering. 2011; 26(3): 225-238. https://doi.org/10.1111/j.1467....
 
eISSN:2449-5220
ISSN:1641-6414