Acoustic emission diagnostics of corrosion monitoring in prestressed concrete using hidden Markov and semi-Markov models

Abstract

This article presents a framework based on hidden Markov modeling for monitoring corrosion damage in prestressed concrete structures through acoustic emission measurements. Both hidden Markov models and hidden semi-Markov models are investigated to determine the merits of each under typical challenges encountered in real-world monitoring scenarios: an unknown initial corrosion condition and an uncertain corrosion rate. To enable hidden semi-Markov models to perform effectively, they are constructed using extended state hidden Markov models in a manner which is able to easily accommodate these scenarios. Furthermore, a framework is proposed for adopting frequency- and topological-based features of acoustic emission data as indicators of corrosion for the models. Accelerated corrosion tests were carried out on two prestressed concrete specimens; one specimen was used for training the models and the other one for testing. The testing specimen was additionally subjected to accelerated weathering to approximate certain conditions found in the field. A combination of established visual inspection, mass loss measurement, and acoustic emission activity analysis was first used to obtain a benchmark corrosion assessment of the specimens. Frequency- and topological-based acoustic emission features were then used as the basis of the hidden Markov modeling framework. The results demonstrate that both hidden Markov models and hidden semi-Markov models are highly reliable under an unknown initial condition. However, in these tests, the hidden semi-Markov model showed slightly greater reliability against error in the assumed corrosion rate. To improve accuracy in this scenario, a procedure for combining predictions from various candidate corrosion rates was also proposed. The diagnostics obtained from this work may provide useful information for established prognostic models of corrosion mechanisms in prestressed concrete.

Keywords

Acoustic emission corrosion hidden Markov models hidden semi-Markov models nondestructive evaluation prestressed concrete structural health monitoring topological data analysis

Get full access to this article

View all access options for this article.

References

Broomfield

. Corrosion of steel in concrete. 2nd ed. London: Taylor & Francis, 2007.

Yuan

C-Q

. Corrosion propagation of prestressing steel strands in concrete subject to chloride attack. Constr Build Mater 2011; 25(10): 3878–3885.

Podolny

. Corrosion of prestressing steels and its mitigation. PCI J 1992; 37(5): 34–55.

Tuutti

. Corrosion of steel in concrete (Technical Report). Stockholm: Swedish Cement and Concrete Research Institute, 1982.

Weyers

. Service life model for concrete structures in chloride laden environments. ACI Mater J 1998; 95(4): 445–451.

Darmawan

Stewart

. Spatial time-dependent reliability analysis of corroding pretensioned prestressed concrete bridge girders. Struct Saf 2007; 29(1): 16–31.

El Maaddawy

Soudki

. A model for prediction of time from corrosion initiation to corrosion cracking. Cement Concr Comp 2007; 29(3): 168–175.

Karuppanasamy

Pillai

. Probabilistic estimation of corrosion propagation period for prestressed concrete structures exposed to chlorides. In: Proceedings of the 4th international conference on the durability of concrete structures (ICDCS 2014), West Lafayette, IN, 24–26 July 2014.

Cabrera

. Deterioration of concrete due to reinforcement steel corrosion. Cement Concr Comp 1996; 18(1): 47–59.

10.

Andrade

Alonso

Molina

. Cover cracking as a function of bar corrosion: part I-experimental test. Mater Struct 1993; 26(8): 453–464.

11.

Austin

Lyons

Ing

. Electrochemical behavior of steel-reinforced concrete during accelerated corrosion testing. Corrosion 2004; 60(2): 203–212.

12.

Di Benedetti

Loreto

Matta

, et al. Acoustic emission historic index and frequency spectrum of reinforced concrete under accelerated corrosion. J Mater Civ Eng 2014; 26(9): 04014059.

13.

Dubuc

Ebrahimkhanlou

Salamone

. Corrosion monitoring of prestressed concrete structures by using topological analysis of acoustic emission data. Smart Mater Struct 2019; 28(5): 055001.

14.

Laguerre

Treyssède

. Non destructive evaluation of seven-wire strands using ultrasonic guided waves. Eur J Environ Civ Eng 2011; 15(4): 487–500.

15.

Farhidzadeh

Salamone

. Reference-free corrosion damage diagnosis in steel strands using guided ultrasonic waves. Ultrasonics 2015; 57: 198–208.

16.

Dubuc

Ebrahimkhanlou

Salamone

. Stress monitoring of prestressing strands in corrosive environments using modulated higher-order guided ultrasonic waves. Struct Health Monit 2020; 19(1): 202–214.

17.

Weng

Dunn

Hartt

, et al. Application of acoustic emission to detection of reinforcing steel corrosion in concrete. Corrosion 1982; 38(1): 9–14.

18.

Dunn

Young

Hartt

, et al. Acoustic emission characterization of corrosion induced damage in reinforced concrete. Corrosion 1984; 40(7): 339–343.

19.

Zdunek

Prine

, et al. Early detection of steel rebar corrosion by acoustic emission monitoring. In: CORROSION95. Houston, TX: NACE International, 1995, pp. 1–9, https://rosap.ntl.bts.gov/view/dot/12999

20.

Ramadan

Gaillet

Tessier

, et al. Detection of stress corrosion cracking of high-strength steel used in prestressed concrete structures by acoustic emission technique. Appl Surf Sci 2008; 254(8): 2255–2261

21.

Yang

Zhang

. Cluster analysis of stress corrosion mechanisms for steel wires used in bridge cables through acoustic emission particle swarm optimization. Ultrasonics 2017; 77: 22–31.

22.

Jiang

Saito

Sinha

. Bridge performance prediction model using the Markov chain. Transp Res Rec 1988; 1180: 25–32.

23.

Cesare

Santamarina

Turkstra

, et al. Modeling bridge deterioration with Markov chains. J Transp Eng 1992; 118(6): 820–833.

24.

Rammohan

Taha

. Exploratory investigations for intelligent damage prognosis using hidden Markov models. In: International conference on systems, man and cybernetics, Waikoloa, HI, 12 October 2005. New York: IEEE.

25.

Ebrahimkhanlou

Choi

Hrynyk

, et al. Detection of the onset of delamination in a post-tensioned curved concrete structure using hidden Markov modeling of acoustic emissions. In: Sohn

Lynch

Wang

K-W

(eds) Sensors and smart structures technologies for civil, mechanical, and aerospace systems, vol. 10598. Bellingham, WA: SPIE, 2018, p. 1059821.

26.

Zhou

Kovvali

Reynolds

, et al. On the use of hidden Markov modeling and time-frequency features for damage classification in composite structures. J Intel Mater Syst Struct 2009; 20(11): 1271–1288.

27.

Eleftheroglou

Loutas

. Fatigue damage diagnostics and prognostics of composites utilizing structural health monitoring data and stochastic processes. Struct Health Monit 2016; 15(4): 473–488.

28.

Mei

Yuan

Qiu

, et al. Damage evaluation by a guided wave-hidden Markov model based method. Smart Mater Struct 2016; 25(2): 025021.

29.

Nielsen

Sørensen

. Bayesian estimation of remaining useful life for wind turbine blades. Energies 2017; 10: 664.

30.

Eleftheroglou

Zarouchas

Loutas

, et al. Structural health monitoring data fusion for in-situ life prognosis of composite structures. Reliab Eng Syst Safe 2018; 178: 40–54.

31.

Dong

. A segmental hidden semi-Markov model (HSMM)-based diagnostics and prognostics framework and methodology. Mech Syst Signal Pr 2007; 21(5): 2248–2266.

32.

Peng

Dong

. A prognosis method using age-dependent hidden semi-Markov model for equipment health prediction. Mech Syst Signal Pr 2011; 25(1): 237–252.

33.

Moghaddass

Zuo

. An integrated framework for online diagnostic and prognostic health monitoring using a multistate deterioration process. Reliab Eng Syst Safe 2014; 124: 92–104.

34.

Guédon

. Estimating hidden semi-Markov chains from discrete sequences. J Comput Graph Stat 2003; 12(3): 604–639.

35.

Langrock

Zucchini

. Hidden Markov models with arbitrary state dwell-time distributions. Comput Stat Data Anal 2011; 55(1): 715–724.

36.

Dubuc

Ebrahimkhanlou

Sitaropoulos

, et al. Topological-based acoustic emission data analysis for passive corrosion monitoring in prestressed concrete structures. In: Fromme

(eds) Health monitoring of structural and biological systems, vol. 11381. Anaheim, CA: SPIE, 2020, p. 113811T.

37.

Dubuc

. Acoustoelasticity and data processing for acoustic-based corrosion monitoring in structures. PhD Thesis, The University of Texas at Austin, Austin, TX, 2020.

38.

Carlsson

. Topology and data. B Am Math Soc 2009; 46(2): 255–308.

39.

Rabiner

. A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE 1989; 77(2): 257–286.

40.

Bishop

. Pattern recognition and machine learning. Berlin: Springer, 2006.

41.

KAT

Stewart

Mullard

. Corrosion-induced cracking: experimental data and predictive models. ACI Struct J 2005; 102(5): 719–726.

42.

ASTM. ASTM F2832—standard guide for accelerated corrosion testing for mechanical fasteners. West Conshohocken, PA: ASTM, 2011.

43.

Kim

Jang

. Critical corrosion amount to cause cracking of reinforced concrete structures. ACI Mater J 2009; 106(4): 333–339.