RETRACTED ARTICLE: Application of genetic programming (GP) and ANFIS for strength enhancement modeling of CFRP-retrofitted concrete cylinders

Jalal, Mostafa; Ramezanianpour, Ali A.; Pouladkhan, Ali R.; Tedro, Payman

doi:10.1007/s00521-012-0941-2

RETRACTED ARTICLE: Application of genetic programming (GP) and ANFIS for strength enhancement modeling of CFRP-retrofitted concrete cylinders

Original Article
Published: 05 May 2012

Volume 23, pages 455–470, (2013)
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Neural Computing and Applications Aims and scope Submit manuscript

Mostafa Jalal¹,
Ali A. Ramezanianpour¹,
Ali R. Pouladkhan² &
…
Payman Tedro²

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This article was retracted on 21 June 2021

This article has been updated

Abstract

Soft computing modeling of strength enhancement of concrete cylinders retrofitted by carbon-fiber-reinforced polymer (CFRP) composites using adaptive neuro-fuzzy inference system (ANFIS) and genetic programming has been carried out in the present work. A comparative study has also been presented using artificial neural network, multiple regression and some existing empirical models. The proposed models are based on experimental results collected from literature. The models represent the ultimate strength of concrete cylinders after CFRP confinement that is in terms of diameter and height of the cylindrical specimen, ultimate circumferential strain in the CFRP jacket, elastic modulus of CFRP, unconfined concrete strength and total thickness of CFRP layer used. The results obtained from different models are presented and compared among which the ANFIS models are considered to be the most accurate so far and quite satisfactory as compared to the experimental results.

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A new formulation for prediction of the shear capacity of FRP in strengthened reinforced concrete beams

Article 07 September 2019

Reza Kamgar, Mohammad Hadi Bagherinejad & Heisam Heidarzadeh

Application of Genetic Programming for Uniaxial and Multiaxial Modeling of Concrete

Soft Computing Techniques for the Prediction and Analysis of Compressive Strength of Alkali-Activated Alumino-Silicate Based Strain-Hardening Geopolymer Composites

Article 14 February 2021

K. K. Yaswanth, J. Revathy & P. Gajalakshmi

Change history

21 June 2021
A Correction to this paper has been published: https://doi.org/10.1007/s00521-021-06174-5

References

Hollaway LC (2004) Advanced polymer composites for structural applications in construction: ACIC 2004. Woodhead, Abingdon, UK
Book Google Scholar
Leung CKY, Ng MYM, Luk HCY (2006) Empirical approach for determining ultimate FRP strain in FRP-strengthened concrete beams. J Compos Constr (ASCE) 10(2):125–138
Article Google Scholar
Matthys S, Toutanji H, Audenaert K, Taerwe L (2005) Axial load behavior of largescale columns confined with fiber-reinforced polymer composites. ACI Struct J 102(2):258–267
Google Scholar
Lam L, Teng JG, Cheng CH, Xiao Y (2006) FRP-confined concrete under axial cyclic compression. Cem Concr Res 28(10):949–958
Article Google Scholar
Deniaud C, Neale KW (2006) An assessment of constitutive models for concrete columns confined with fiber composite sheets. Compos Struct 73(3):318–330
Article Google Scholar
Teng JG, Yu T, Wong YL, Dong SL (2007) Hybrid FRP-concrete-steel tubular columns: concept and behavior. Constr Build Mater 21(4):846–854
Article Google Scholar
Lee C, Hegemier GA (2009) Model of FRP-confined concrete cylinders in axial compression. J Compos Constr (ASCE) 13(5):442–454
Article Google Scholar
Jodaei A, Jalal M, Yas MH (2011) Free vibration analysis of functionally graded annular plates by state-space based differential quadrature method and comparative modeling by ANN. Compos Part B 43:340–353
Article Google Scholar
Ashrafi HR, Jalal M, Garmsiri K (2010) Prediction of load–displacement curve of concrete reinforced by composite fibers (steel and polymeric) using artificial neural network. Expert Syst Appl 37:7663–7668
Article Google Scholar
Unal O, Demir F, Uygunoglu T (2007) Fuzzy logic approach to predict stress–strain curves of steel fiber-reinforced concretes in compression. Build Environ 42:3589–3595
Article Google Scholar
Saafi M, Toutanji HA, Li Z (1999) Behavior of concrete columns confined with fiber reinforced polymer tubes. ACI Mater J 96(4):500–509
Google Scholar
Rochette P, Labossiere P (2000) Axial testing of rectangular column models confined with composites. J Compos Constr (ASCE) 4(3):129–136
Article Google Scholar
Xiao Y, Wu H (2000) Compressive behavior of concrete confined by carbon fiber composite jackets. J Mater Civ Eng 12(2):139–146
Article Google Scholar
Harmon TG, Slattery KT (1992) Advanced composite confinement of concrete. In: First international conference on advanced composite materials in bridges and structures, Sherbrooke, Québec, Canada, pp 299–306
Picher F, Rochette P, Labossiére P (1996) Confinement of concrete cylinders with CFRP. In: Proceedings of the first composites in infrastructure ICCI’96, 15–17 January, Tucson, Arizona, pp 829–841
Watanabe K, H Nakamura, Y Honda, Toyoshima M, Iso M, Fujinaki T, Kaneto M (1997) Confinement effect of FRP sheet on strength and ductility of concrete cylinders under uni-axial compression. In: Proceedings of the third international symposium on non-metallic FRP for concrete structures, Japan, pp 233–240
Kono S, Inazumi M, Kaku T (1998) Evaluation of confining effects of CFRP sheets on reinforced concrete members. In: Proceedings of the second international conference on composites in infrastructure ICCI’98, Tucson, Arizona, 5–7 January, pp 343–355
Toutanji HA (1999) Stress–strain characteristics of concrete columns externally confined with advanced fiber composite sheets. ACI Mater J 96(3):397–404
Google Scholar
Matthys S, Taerwe L, Audenaert K (1999) Tests on axially loaded concrete columns confined by fiber reinforced polymer sheet wrapping. In: Fourth international symposium on fiber reinforced polymer reinforcement for reinforced concrete structures, SP-188, American Concrete Institute, Farmington, Michigan, USA, pp 217–229
Shahawy M, Mirmiran A, Beitelman A (2000) Test and modeling of carbon-wrapped concrete columns. Compos Part B Eng ASCE 31:471–480
Article Google Scholar
Micelli F, Myers JJ, Murthy S (2001) Effect of environmental cycles on concrete cylinders confined with FRP. In: Proceedings of CCC2001 international conference on composites in construction, Porto, Portugal
Mirmiran A, Shahawy M, Samaan M (1999) Strength and ductility of hybrid FRPconcrete beam-columns. J Struct Eng ASCE 125(10):1085–1093
Article Google Scholar
De Lorenzis L, Micelli F, La Tegola A (2002) Influence of specimen size and resin type on the behavior of FRP-confined concrete cylinders. In: Shenoi RA, Moy SSJ, Hollaway LC (eds) Advanced polymer composites for structural applications in construction, proceedings of the first international conference. Thomas Telford, London, pp 231–239
Chapter Google Scholar
Dias da Silva V, Santos JMC (2001) Strengthening of axially loaded concrete cylinders by surface composites. In: Figueiras J, Juvandes L, Faria R, Marques AT, Ferreira A, Barros J, Appleton J (eds) Composites in constructions, proceedings of the international conference. A.A. Balkema Publishers, Lisse, The Netherlands, pp 257–262
Pessiki S, Harries KA, Kestner JT, Sause R, Ricles JM (1997) Axial behavior of reinforced concrete columns confined with FRP jackets. J Compos Constr ASCE 5(4):237–245
Article Google Scholar
Wang P, Cheong KK (2001) RC columns strengthened by FRP under uniaxial compression. In: Teng JG (ed) FRP composites in civil engineering, proceedings of the international conference. Elsevier, Oxford, pp 327–334
Google Scholar
Shehata IAEM, Carneiro LAV, Shehata LCD (2002) Strength of short concrete columns confined with CFRP sheets. Mater Struct 35(1):50–58
Article Google Scholar
Kshirsagar S, Lopez-Anido RA, Gupta RK (2000) Environmental aging of fiber reinforced polymer-wrapped concrete cylinders. ACI Mater J 97(6):703–712
Google Scholar
Berthet JF, Ferrier E, Hamelin P (2005) Compressive behavior of concrete externally confined by composite jackets. Part A: experimental study. Constr Build Mater 19:223–232
Article Google Scholar
Lin CT, Li YF (2003) An effective peak stress formula for concrete confined with carbon fiber reinforced plastics. Canadian J Civil Eng 30:882–889
Article Google Scholar
Richart FE, Brandtzaeg A, Brown RL (1928) A study of the failure of concrete under combined compressive stress. University of Illinois, Engineering Experimental Station, Illinois
Google Scholar
Saadatmanesh H, Ehsani MR, Li MW (1994) Strength and ductility of concrete columns externally reinforced with fiber composite straps. ACI Struct J 91(4):434–447
Google Scholar
Samaan M, Mirmiran A, Shahawy M (1998) Model of concrete confined by fiber composites. J Struct Eng ASCE 124(9):1025–1031
Article Google Scholar
Lam L, Teng JG (2002) Strength models for fiber-reinforced-plastic-confined concrete. J Struct Eng ASCE 128(5):612–623
Article Google Scholar
Zadeh LA (1994) Soft computing and fuzzy logic. IEEE Softw 11(6):48–56
Article Google Scholar
Fausett LV (1994) Fundamentals of neural networks: architectures, algorithms, and applications. Prentice Hall, Englewood Cliffs, NJ
MATH Google Scholar
Jodaei A, Jalal M, Yas MH (2011) Free vibration analysis of functionally graded annular plates by state-space based differential quadrature method and comparative modeling by ANN. Compos B 34(2):340–353
Article Google Scholar
Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor
Google Scholar
Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading, MA
MATH Google Scholar
Haupt RL, Haupt SE (2004) The practical handbook of genetic algorithms, 2nd edn. Chapman & Hall/CRC, London/Boca Raton
MATH Google Scholar
Chambers L (2001) The practical handbook of genetic algorithms, 2nd edn. Chapman & Hall/CRC, London/Boca Raton
Google Scholar
Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge, MA
MATH Google Scholar
Ferreira C (2002) Gene expression programming: mathematical modeling by an artificial intelligence. Angra do Heroismo, Portugal
Ferreira C (2001) Gene expression programming in problem solving. In: Invited tutorial of the 6th online world conference on soft computing in industrial applications, pp 10–24
Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. Complex Systems 13(2):87–129
MathSciNet MATH Google Scholar
http://www.gepsoft.com/
Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cyber 23(3):665–685
Article Google Scholar
Jang JSR, Sun CT (1995) Nuro-fuzzy modeling and control. Proc IEEE 83(3):378–406
Article Google Scholar
Ramezanianpour AA, Sobhani J, Sobhani M (2004) Application of an adaptive neurofuzzy system in the prediction of HPC compressive strength. In: Proceedings of the fourth international conference on engineering computational technology. Civil-Comp Press, Lisbon, Portugal, p 138

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Authors and Affiliations

Concrete Technology and Durability Research Center, Amirkabir University of Technology, Tehran, Iran
Mostafa Jalal & Ali A. Ramezanianpour
Department of Civil Engineering, Razi University, Kermanshah, Iran
Ali R. Pouladkhan & Payman Tedro

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Mostafa Jalal
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Ali A. Ramezanianpour
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Ali R. Pouladkhan
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Correspondence to Mostafa Jalal.

Appendix

Ref.	Code	Geometry			FRP properties			\( f_{cc,\exp }^{\prime } \,({\text{Mpa}}) \)
Ref.	Code	d (mm)	h (mm)	nxt (mm)	E _FRP (Mpa)	ε _rup (mm)	\( f_{c}^{\prime } \,({\text{Mpa}}) \)	\( f_{cc,\exp }^{\prime } \,({\text{Mpa}}) \)
Harmon and Slattery [14]	HA1	51	102	0.089	235,000	0.0113	41	86.1
	HA2	51	102	0.179	235,000	0.01	41	120.54
	HA3	51	102	0.344	235,000	0.0075	41	158.26
	HA4	51	102	0.179	235,000	0.002	103	130.81
	HA5	51	102	0.344	235,000	0.00725	103	193.64
	HA6	51	102	0.689	235,000	0.0055	103	303.85
	HA7	153	305	0.36	83,000	0.0084	39.7	55.98
Picher et al. [15]	PI1	100	200	0.67	223,400	0.00667	30.2	104.49
Watanabe et al. [16]	WA1	100	200	0.14	611,600	0.0025	30.2	41.68
	WA2	100	200	0.28	611,600	0.00167	30.2	55.87
	WA3	100	200	0.42	611,600	0.0021	30.2	63.42
	WA4	100	200	0.167	235,000	0.0084	34.3	57.28
Kono et al. [17]	KO1	100	200	0.167	235,000	0.0092	34.3	64.83
	KO2	100	200	0.167	235,000	0.0096	32.3	61.69
	KO3	100	200	0.167	235,000	0.0063	32.3	57.82
	KO4	100	200	0.334	235,000	0.0077	32.3	86.89
	KO5	100	200	0.334	235,000	0.0066	34.8	82.82
	KO6	100	200	0.334	235,000	0.0091	34.8	103.36
	KO7	76	305	0.236	72,600	0.0163	30.93	60.93
Toutanji [18]	TO1	76	305	0.22	230,500	0.0125	30.93	94.96
	TO2	76	305	0.33	372,800	0.0055	30.93	94.03
	TO3	150	300	0.117	220,000	0.0126	34.9	46.07
Matthys et al. [19]	MA1	150	300	0.235	500,000	0.0031	34.9	45.72
	MA2	150	300	0.12	200,000	0.0115	34.9	44.3
	MA3	150	300	0.12	200,000	0.0108	34.9	42.2
	MA4	150	300	0.24	420,000	0.0019	34.9	41.3
	MA5	150	300	0.24	420,000	0.0018	34.9	40.7
	MA6	153	305	0.36	82,700	0.006	49	59.29
Shahawy et al. [20]	SH1	153	305	0.66	82,700	0.006	49	76.44
Shahawy et al. [20]	SH2	100	200	0.6	82,700	0.0089	42	73.5
Rochette and Labossiere [12]	RL1	100	200	0.6	82,700	0.0095	42	73.5
	RL2	100	200	0.6	82,700	0.008	42	67.62
	RL3	100	200	0.35	72,400	0.0101	32	54.08
Micelli et al. [21]	MC1	100	200	0.35	72,400	0.0099	32	48
Micelli et al. [21]	MC2	152	435	0.8	32,000	0.017	35	52.85
Saafi et al. [11]	SA1	152	435	0.11	367,000	0.013	35	54.95
Saafi et al. [11]	SA2	152	305	1.44	37,233	0.0123	30.86	53.7
Mirmiran et al. [22]	MS1	152	305	1.44	37,233	0.0177	29.64	66.99
Mirmiran et al. [22]	MS2	152	305	0.38	105,000	0.012	33.7	47.9
Xiao and Wu [13]	XW1	152	305	0.38	105,000	0.0124	33.7	49.4
	XW2	152	305	0.38	105,000	0.0098	43.8	54.8
	XW3	152	305	0.38	105,000	0.0047	43.8	52.1
	XW4	152	305	0.38	105,000	0.0037	43.8	48.7
	XW5	152	305	0.38	105,000	0.0069	55.2	57.9
	XW6	152	305	0.38	105,000	0.0048	55.2	62.9
	XW7	152	305	0.38	105,000	0.0049	55.2	58.1
	XW8	152	305	0.76	105,000	0.0081	55.2	77.6
	XW9	120	240	0.3	91,100	0.007	43	58.5
De Lorenzis et al. [23]	LO1	120	240	0.3	91,100	0.008	43	65.6
	LO2	150	300	0.45	91,100	0.008	38	62
	LO3	150	300	0.45	91,100	0.008	38	67.5
	LO4	150	600	0.111	240,000	0.0026	28.2	31.4
Dias da Silva and Santos [24]	SS1	150	600	0.222	240,000	0.0118	28.2	57.4
	SS2	150	600	0.333	240,000	0.0114	28.2	69.5
	SS3	150	600	0.167	390,000	0.0037	28.2	41.5
	SS4	150	600	0.334	390,000	0.0069	28.2	65.6
	SS5	150	600	0.501	390,000	0.0064	28.2	79.4
	SS6	152	610	1	21,600	0.0115	26.2	38.4
Pessiki et al. [25]	PE1	152	610	2	21,600	0.0124	26.2	52.5
	PE2	152	610	1	38,100	0.0081	26.2	50.6
	PE3	152	610	2	38,100	0.0072	26.2	64
	PE4	200	600	0.36	235,000	0.0085	27.9	82.8
Wang and Cheong [26]	WC1	200	600	0.36	235,000	0.0107	27.9	81.2
Wang and Cheong [26]	WC2	150	300	0.165	235,000	0.0123	29.8	57
Shehata et al. [27]	SH1	150	300	0.33	235,000	0.0174	29.8	72.1
Shehata et al. [27]	SH2	102	201	1.42	19,900	0.0174	38	57
Kshirsagar et al. [28]	KS1	102	204	1.42	19,900	0.0207	39.4	63.1
	KS2	102	204	1.42	19,900	0.0189	39.5	60.4
	KS3	160	320	0.165	230,000	0.00957	22.18	42.8
Berthet et al. [29]	BE1	160	320	0.165	230,000	0.00964	25.03	37.8
	BE2	160	320	0.165	230,000	0.0096	25.03	45.8
	BE3	160	320	0.33	230,000	0.00899	24.98	56.7
	BE4	160	320	0.33	230,000	0.00911	24.98	55.2
	BE5	160	320	0.33	230,000	0.00908	25.04	56.1
	BE6	160	320	0.11	230,000	0.01015	40.16	49.8
	BE7	160	320	0.11	230,000	0.00952	40.32	50.8
	BE8	160	320	0.11	230,000	0.01203	40.33	48.8
	BE9	160	320	0.165	230,000	0.0088	40.07	53.7
	BE10	160	320	0.165	230,000	0.00853	40.22	54.7
	BE11	160	320	0.165	230,000	0.01042	40.16	51.8
	BE12	160	320	0.22	230,000	0.00788	40.07	59.7
	BE13	160	320	0.22	230,000	0.0083	40.2	60.7
	BE14	160	320	0.22	230,000	0.00809	40.13	60.2
	BE15	160	320	0.44	230,000	0.00924	40.18	91.6
	BE16	160	320	0.44	230,000	0.00967	40.18	89.6
	BE17	160	320	0.44	230,000	0.00885	40.09	86.6
	BE18	160	320	0.99	230,000	0.00989	40.11	142.4
	BE19	160	320	0.99	230,000	0.01	40.11	140.4
	BE20	160	320	1.32	230,000	0.00999	40.07	166.3
	BE21	160	320	0.33	230,000	0.00949	51.94	82.6
	BE22	160	320	0.33	230,000	0.00865	52	82.8
	BE23	160	320	0.33	230,000	0.00891	52	82.3
	BE24	160	320	0.66	230,000	0.00667	51.97	108.1
	BE25	160	320	0.66	230,000	0.00871	52.1	112
	BE26	160	320	0.66	230,000	0.00882	51.88	107.9
	BE27	70	140	0.33	230,000	0.00712	112.88	141.1
	BE28	70	140	0.33	230,000	0.00738	112.68	143.1
	BE29	70	140	0.82	230,000	0.00754	112.8	189.5
	BE30	70	140	0.82	230,000	0.00728	113.19	187.9
	BE31	70	140	0.33	230,000	0.00459	171	186.4
	BE32	70	140	0.99	230,000	0.00799	169.37	296.4
	BE33	150	300	0.11	232,000	0.01	17.94	37.89
Lin and Li [30]	LL1	120	240	0.11	232,000	0.01	17.39	42.79
	LL2	100	200	0.11	232,000	0.01	17.51	45.2
	LL3	150	300	0.22	232,000	0.01	17.94	54.68
	LL4	120	240	0.22	232,000	0.01	17.39	62.26
	LL5	100	200	0.22	232,000	0.01	17.51	70.1
	LL6	150	300	0.33	232,000	0.01	17.94	72.17
	LL7	120	240	0.33	232,000	0.01	17.39	83.98
	LL8	100	200	0.33	232,000	0.01	17.51	91.56
	LL9	150	300	0.11	232,000	0.01	22.75	44.55
	LL10	120	240	0.11	232,000	0.01	22.73	48.18
	LL11	200	400	0.11	232,000	0.01	23.04	56.28
	LL12	150	300	0.22	232,000	0.01	22.75	60.8
	LL13	120	240	0.22	232,000	0.01	22.73	75.44
	LL14	100	200	0.22	232,000	0.01	23.04	80.35
	LL15	150	300	0.33	232,000	0.01	22.75	82.86
	LL16	120	240	0.33	232,000	0.01	22.73	89.44
	LL17	100	200	0.33	232,000	0.01	23.04	101.8
	LL18	150	300	0.11	232,000	0.01	24.99	48.09
	LL19	120	240	0.11	232,000	0.01	25.42	55.33
	LL20	200	400	0.11	232,000	0.01	25.01	61.08
	LL21	150	300	0.22	232,000	0.01	24.99	68.49
	LL22	120	240	0.22	232,000	0.01	25.42	79.75
	LL23	100	200	0.33	232,000	0.01	25.01	88.82
	LL24	150	300	0.33	232,000	0.01	24.99	87.04
	LL25	120	240	0.33	232,000	0.01	25.42	96.85
	LL26	100	200	0.33	232,000	0.01	25.01	107.4

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Jalal, M., Ramezanianpour, A.A., Pouladkhan, A.R. et al. RETRACTED ARTICLE: Application of genetic programming (GP) and ANFIS for strength enhancement modeling of CFRP-retrofitted concrete cylinders. Neural Comput & Applic 23, 455–470 (2013). https://doi.org/10.1007/s00521-012-0941-2

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Received: 13 March 2012
Accepted: 11 April 2012
Published: 05 May 2012
Issue Date: August 2013
DOI: https://doi.org/10.1007/s00521-012-0941-2

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RETRACTED ARTICLE: Application of genetic programming (GP) and ANFIS for strength enhancement modeling of CFRP-retrofitted concrete cylinders

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A new formulation for prediction of the shear capacity of FRP in strengthened reinforced concrete beams

Application of Genetic Programming for Uniaxial and Multiaxial Modeling of Concrete

Soft Computing Techniques for the Prediction and Analysis of Compressive Strength of Alkali-Activated Alumino-Silicate Based Strain-Hardening Geopolymer Composites

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RETRACTED ARTICLE: Application of genetic programming (GP) and ANFIS for strength enhancement modeling of CFRP-retrofitted concrete cylinders

Abstract

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A new formulation for prediction of the shear capacity of FRP in strengthened reinforced concrete beams

Application of Genetic Programming for Uniaxial and Multiaxial Modeling of Concrete

Soft Computing Techniques for the Prediction and Analysis of Compressive Strength of Alkali-Activated Alumino-Silicate Based Strain-Hardening Geopolymer Composites

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21 June 2021

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