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Frontiers of Structural and Civil Engineering

ISSN 2095-2430

ISSN 2095-2449(Online)

CN 10-1023/X

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Front. Struct. Civ. Eng.    2016, Vol. 10 Issue (1) : 1-11    https://doi.org/10.1007/s11709-016-0312-7
RESEARCH ARTICLE
Experimental and modeling studies on installation of arc sprayed Zn anodes for protection of reinforced concrete structures
Xianming SHI1,2()
1. School of Civil Engineering and Architecture, Wuhan Polytechnic University, Wuhan 430023, China
2. Department of Civil & Environmental Engineering, Washington State University, P.O. Box 642910, Pullman, WA 99164-2910, USA
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Abstract

Arc sprayed zinc (Zn) anode on concrete surfaces has been an emerging technology for protecting reinforced concrete structures from rebar corrosion in coastal environments. Many cathodic protection (CP) systems with arc sprayed Zn anodes will reach or exceed their design life in the near future and thus may function improperly or insufficiently, making it necessary to replace the aged anodes. However, prior to this study, little was known about the most effective profile for the concrete surface, for either new concrete or old concrete with existing Zn anodes removed. This work develops criteria to properly prepare the concrete surface before the application of new Zn anode. Experimental studies were conducted both in the laboratory and for a field structure in Oregon. Artificial neural network was used to achieve better understanding of the complex cause-and-effect relationships inherent in the Zn-mortar or Zn–concrete systems and was successful in finding meaningful, logical results from the bond strength data. The goal is to achieve strong initial bond strength of new Zn to concrete, which is essential for long-term performance of the CP system. The results from this case study suggest that it is necessary to adjust the anode removal and surface sandblasting based on the electrochemical age of the existing concrete. In all cases of sandblasting, minimize the exposure of large aggregates (e.g., those bigger than 19 mm in diameter).
Keywords arc sprayed Zn      anode replacement      reinforced concrete      bridge preservation      neural networks      surface profile     
Corresponding Author(s): Xianming SHI   
Online First Date: 12 January 2016    Issue Date: 19 January 2016
 Cite this article:   
Xianming SHI. Experimental and modeling studies on installation of arc sprayed Zn anodes for protection of reinforced concrete structures[J]. Front. Struct. Civ. Eng., 2016, 10(1): 1-11.
 URL:  
https://academic.hep.com.cn/fsce/EN/10.1007/s11709-016-0312-7
https://academic.hep.com.cn/fsce/EN/Y2016/V10/I1/1
Fig.1  Bond strength of periodically wetted arc sprayed Zn anodes on concrete as a function of electrochemical age in accelerated ICCP tests [16]
Fig.2  Structure of a typical ANN (Wih = connection weight from the ith input neuron to the hth hidden neuron; Whk= interconnection weight between the hth hidden neuron and the kth output neuron; p, q, and r = number of input, hidden, and output neurons, respectively)
air pressure nozzle size No. of passes
2 2 2
2 2 2
1 3 2
2 3 3
2 1 1
2 2 2
2 2 2
3 3 2
1 2 3
3 2 1
1 2 1
1 1 2
3 1 2
1 3 3
3 3 3
2 3 1
1 1 3
1 1 1
3 3 1
3 2 3
1 3 1
2 1 3
3 1 3
3 1 1
Tab.1  A uniform design table for sandblasting the PCC and mortar samples: U24(33)=24 combinations with three factors each varying at three levels
Fig.3  (a) A worker sandblasting a concrete sample; (b) arc spraying the mortar and concrete samples; (c) key steps in evaluating RMS macro-roughness
Fig.4  A step used in quantifying the percent of exposed rock at the bond test site
Fig.5  Containment enclosure with negative pressure system and blast pot assembly (a); and a worker applying new anode to the pile cap section of pier structure (b)
Fig.6  Performance of the 3-6-1 ANN model for bond strength of new mortars
Fig.7  Predicted bond strength of new mortar as a function of: (a) pre-roughness and Zn thickness, with 28% exposed aggregates; (b) pre-roughness and surface composition, with 17.5 mils of new Zn
Fig.8  Performance of the 3-5-1 ANN model for bond strength of new concretes
Fig.9  Predicted bond strength of new concretes as a function of: (a) pre-roughness and Zn thickness, with 13.4% exposed aggregates; (b) pre-roughness and surface composition, with 16.8 mils of new Zn
Fig.10  Performance of the 3-11-1 ANN model for bond strength of fully cured concretes
Fig.11  Predicted bond strength of fully cured concretes as a function of pre-roughness and electrochemical age, with 35% exposed aggregates and 17 mils of new Zn
equivalent electro-chemical age (yrs) highest bond strength values generally coincided with surfaces featuring
0 a moderate level of macro-roughness (0.28–0.46 mm) and relatively low concentration of exposed aggregates (5–36%)
8 a moderate level of macro-roughness (0.28–0.38 mm) and relatively low concentration of exposed aggregates (12–36%)
14 a moderate level of macro-roughness (0.28–0.38 mm) and a moderated level of exposed aggregates (44–55%).
20-27 a relatively low level of macro-roughness (0.15–0.28 mm) and a moderate level of exposed aggregates (44–51%).
Tab.2   Predicted trends in the new Zn bond strength vs. electrochemical age of fully cured concrete (assuming 17 mils of new Zn)
1 R B Polder, G Leegwater, D Worm, W Courage. Service life and life cycle cost modelling of cathodic protection systems for concrete structures. Cement and Concrete Composites, 2014, 47: 69–74
2 X Shi, T Anh Nguyen, P Kumar, Y Liu. A phenomenological model for the chloride threshold of pitting corrosion of steel in simulated concrete pore solutions. Anti-Corrosion Methods and Materials, 2011, 58(4): 179–189
3 H Yu, X Shi, W H Hartt, B Lu. Laboratory investigation of reinforcement corrosion initiation and chloride threshold content for self-compacting concrete. Cement and Concrete Research, 2010, 40(10): 1507–1516
4 T Pan, T A Nguyen, X Shi. Assessment of electrical injection of corrosion inhibitor for corrosion protection of reinforced concrete. Transportation Research Record: Journal of the Transportation Research Board, 2008, 2044(1): 51–60
5 M F Montemor, A M P Simoes, M G S Ferreira. Chloride-induced corrosion on reinforcing steel: from the fundamentals to the monitoring techniques. Cement and Concrete Composites, 2003, 25(4): 491–502
6 B Tang. Building More Durable Bridges. Federal Highway Administration FOCUS, Publication No. FHWA-RD-99–107, 1999.
7 Y Liu, X Shi. Modeling cathodic prevention for unconventional concrete in salt-laden environment. Anti-Corrosion Methods and Materials, 2012, 59(3): 121–131
8 P Pedeferri. Cathodic protection and cathodic prevention. Construction & Building Materials, 1996, 10(5): 391–402
9 S Szabó, I Bakos. Cathodic protection with sacrificial anodes. Corrosion Reviews, 2006, 24(1–2): 1–50
10 J Carmona, P Garcés, M A Climent. Efficiency of a conductive cement-based anodic system for the application of cathodic protection, cathodic prevention and electrochemical chloride extraction to control corrosion in reinforced concrete structures. Corrosion Science, 2015, 96: 102–111
11 Y Liu, X Shi. Cathodic protection technologies for reinforced cncrete: Introduction and recent developments. Reviews in Chemical Engineering, 2009, 25(5–6): 339–388
12 A A Sohanghpurwala. Cathodic Protection for Life Extension of Existing Reinforced Concrete Bridge Elements.NCHRP Synthesis 398. Transportation Research Board, Washington, DC, 2009.
13 G McGill, T Shike. Rehabilitation and peservation of Oregon’s hstoric cncrete castal bidges. Transportation Research Record, 1997, 1601: 9–12
14 J A Apostolos, D M Parks, R A Carello. Cathodic potection using mtallized Zn. Materials Performance, 1987, 26(12): 22–28
15 G R Holcomb, S J Bullard, B S Covino Jr, S D Cramer, C B Cryer, G E McGill. Electrochemical aing of termal-srayed zinc anodes on concrete. In: Thermal Spray: Practical Solutions for Engineering Problems, Berndt C C, ed. Proceedings of the 9th National Thermal Spray Conference. ASM International, Metals Park, OH, 1996, 185–192
16 S D Cramer, B S Covino, S J Bullard, G R Holcomb, J H Russell, F J Nelson, H M Laylor, S M Soltesz. Corrosion prevention and remediation strategies for reinforced concrete coastal bridges. Cement and Concrete Composites, 2002, 24: 101–117
17 SSPC-SP 13/NACE No. 6, Surface Preparation of Concrete.
18 J G Legoux, S Dallaire. Adhesion mechanisms of arc-sprayed zinc on concrete. Journal of Thermal Spray Technology, 1995, 4(4): 395–400
19 X M Shi, S Soltesz, Y X Li, J D Cross, L Ewan. Electrochemically aged arc sprayed Zn coating to concrete: Bond strength study. Surface Engineering, 2013, 29(1): 55–60
20 Z Ye, Y Xu, D Veneziano, X. Shi Evaluation of winter maintenance chemicals and crashes with an artificial neural network. Transportation Research Record: Journal of the Transportation Research Board, 2014, 2440: 43–50
21 X Shi, S W Goh, M Akin, S Stevens, Z You. Exploring the interactions of chloride deicer solutions with nano/micro-modified asphalt mixtures using artificial neural networks. Journal of Materials in Civil Engineering, 2012, 24(7): 805–815
22 X Shi, Y Liu, M Mooney, M Berry, B Hubbard, T A Nguyen. Laboratory investigation and neural networks modeling of deicer ingress into Portland cement concrete and its corrosion implications. Corrosion Reviews, 2010, 28(3–4): 105–154
23 X Shi, T A Nguyen, Z Suo, J Wu, J Gong, R Avci. Electrochemical and mechanical properties of superhydrophobic aluminum substrates modified with nano-silica and fluorosilane. Surface and Coatings Technology, 2012, 206(17): 3700–3713
24 X Shi, P Schillings, D Boyd. Applying artificial neural networks and virtual experimental design to quality improvement of two industrial processes. International Journal of Production Research, 2004, 42(1): 101–118
25 D E Rumelhart, G E Hinton, R J Williams. Learning internal representations by error propagation. J. L. McClelland and the PDP Research Group, eds. Parallel Distributed Processing, Cambridge, MA: MIT Press, 1986, 318–362
26 Y Sharifi , S Tohidi. Lateral-torsional buckling capacity assessment of web opening steel girders by artificial neural networks—elastic investigation. Frontiers of Structural and Civil Engineering, 2014, 8(2): 167–177
27 M Shahid, S A Hashim. Effect of surface roughness on the strength of cleavage joints. International Journal of Adhesion and Adhesives, 2002, 22(3): 235–244
28 X Shi, J D Cross, L Ewan, Y Liu, K Fortune. Replacing thermal sprayed Zn anodes on cathodically protected steel reinforced concrete bridges. Oregon Department of Transportation. A Final Report Prepared for the Oregon Department of Transportation, 2011.
29 G R Holcomb, S J Bullard, J B S Covino, S D Cramer, C B Cryer, G E McGill. Electrochemical Aging of Thermally-sprayed Zn Anodes on Concrete, DOE/ACR-97–001, 1997
30 S D Cramer, B S Covino, S J Bullard, G R Holcomb, J H Russell, F J Nelson, H M Laylor, S M Soltesz. Corrosion prevention and remediation strategies for reinforced concrete coastal bridges. Journal of Cement and Concrete Composites, 1997, 24: 101–117
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