11-14 May 2008, Istanbul Durability of clay roofing tiles: assessing the reliability of prediction models M. Raimondo, M. Dondi, C. Ceroni*, G. Guarini ISTEC-CNR Institute of Science and Technology for Ceramics, Faenza (Italy) mariarosa.raimondo@istec.cnr.it *DICASM, University of Bologna, Bologna (Italy)
Water-solid interactions.. depend on several variables: expansion of water in freezing freezing rates saturation of bodies variation of Young modulus solid pore amount solid pore size distribution, etc Freezing resistance has to be thought to be influenced by a combination of such variables Blachere and Young, JACS 57, [5], 1974
Experimental approach Study of the tile microstructure MIP, BET OP, BD, WA (ASTM C67, C373) SEM, XRPD (Rietveld refinement) Water-solid interaction Capillary rise (UNI EN 772-11, UNI 10859) Surface wettability Microstructural variables Porosity related behaviour Durability indices Target Clay roofing tiles with improved frost/thaw resistance Experimental test UNI EN 539-2 Part 2
Models to calculate durability factors Pore amount (MIP, WA) Pore size (MIP) Saturation Coefficient Cs Kinetics of WA (WA 5h /WA 24h ) Maage 3.2/PV 2.4 P3(%) - - Arnott 424 (WA 5h /WA vac ) 0.919 P3(%) 100 K 4 C s 0.487 K s Vincenzini - Ф 90 - - Franke/ Bentrup - Ф 50 - - Koroth 451/(2.94 + WA 5h ) - 330 (0.97 C s ) - Robinson (WA 24h -10) - 1/(1-C s ) 0.1 K s
M dbmc 7 Roofing tile typologies 1 G Sampling A H F 5 D I C K B L E J Coppo Over and under tile Marseillaise Portuguese
UNI EN 539-2. Part 2: Test for frost resistance Climatic cell Freeze/thaw cycles Tests were carried out in the climatic cell from - 15 C to +15 C performing 400 freeze/thaw cycles; this number of cycles - much higher than those (150) scheduled by the reference standard - was chosen in order to simulate very extreme conditions.
400 350 300 B 375 cycles 250 Loss of interlocking rib 200 Results of UNI EN 539-2: Number of cycles and defect typology G 100 cycles Breack 400 cycles: 3 samples 375: 1 sample 250 cycles: 2 samples < 100 cycles: 4 samples < 50 cycles: 3 samples J 100 cycles Delamination, Exfoliation 150 100 K 100 cycles I 75 cycles M 50 cycles 50 0 A B C D E F G H I J K L M Structural crack Peeling Chip, Surface cracks, Delamination
A dbmc Coarser grains, big pores and a quite regular smooth microstructure Frost resistance and products microstructure 400 350 300 250 200 150 100 50 C 0 A B C D E F G H I J K L M BET = 0.7 m 2 /g BET = 1.8 m 2 /g Small and oriented grains, finer matrix and very few big pores
1. Freeze/thaw resistance vs Open porosity 30 28 C OP = 28% Sample C Open porosity (%) 26 24 22 20 18 M I J K F G L B H D A 16 14 E 12 0 50 100 150 200 250 300 350 400 450 Number of cycles OP = 13% Sample E
2. Freeze/thaw resistance vs Specific surface area 2,4 2,2 M SS = 2.2 m 2 /g Sample M Specific surface (m 2 /g) 2,0 1,8 1,6 1,4 1,2 C I J G F K L E B D H 1,0 0,8 A 0,6 0 50 100 150 200 250 300 350 400 450 Number of cycles SS = 0.7 m 2 /g Sample A
UNI 539-2 vs Maage index Role of pore size DF 3.2 PV 180 2.4P3 MAAGE DF > 70 Resistant 55 < DF < 70 Doubtful behaviour DF < 55 Non resistant 160 P3 (%) A DF 60 50 40 30 20 10 140 120 100 0 80 60 40 20 G E M I B J F L D H C K 0 0 50 100 150 200 250 300 350 400 450 A B C D E F G H I J K L M Cycles GELO
UNI EN 539-2 vs Ф 90 Role of pore size 5 DIAMETRO CRITICO A Critical diameter DCR 4 3 2 1 C M I G K J F E Φ 90 1.8 μm Frost resistant B H L D Φ 90 0.5 μm Not frost resistant 0 0 50 100 150 200 250 300 350 400 450 Cycles GELO
UNI EN 539-2 vs Arnott index 9.2 P3 0.5 K s + 423 (WA 5h /WA) -100 K s C s -84.5 Arnott's durability factor 1000 900 800 700 600 500 400 300 200 C M r 2 = 0,129 I G FK J E L 0 50 100 150 200 250 300 3500 400 450 A B C D E F G H I J K L M Freeze/thaw cycles 60 50 40 30 20 10 B A H D The model proposed by Arnott present a good agreement with the P3 (%) experimental performances of scarcely (<100 cycles) and high resistant (>250 cycles) products; the main exceptions are always represented by samples L, B, H and D having excellent durability. The insertion into the multiple regression analysis of different physical and technological parameters, provides a model where the role of each variable is quantified by a statistical different weight: the amount of greater pores (P3) seems to be the most influent so that, accordingly, index values increases as the percentage of P3 increases.
ROB 6 5 4 3 2 1 0-1 -2 R 2 = 0.134 C M I K J F G UNI EN 539-2 vs Robinson index ROBINSON L E Ks Y 101 C Low Y = high frost resistance High Y = low resistance B H A D S E 24H 10-3 0 50 100 150 200 250 300 350 400 450 Cycles GELO
Frost resistance and phase composition Quartz 6 42 % Plagioclase 0 44 % K-feldspar 1 13 % Pyroxene 0 32 % Melilite 0 6 % Illite 0 13 % Glassy phase 8 58 % Ca-silicates D C L H Unreacted phases: Quartz, K-feldspar New-formed crystalline phases: Ca-silicates New formed or residual phases: Amorphous, Illite A E F J K I G B Qz+Felds M Amorphous+Illite
Frost resistance and phase composition High amount of amorphous and residual phases Underfired product from a mineralogical point of view Amorphous + Illite (%) 60 50 40 30 20 K B M I F J G E L H D C A 10 0 50 100 150 200 250 300 350 400 450 Number of cycles
Frost resistance and phase composition Ca-silicates/amorphous + illite 4,5 4,0 3,5 3,0 2,5 2,0 1,5 1,0 0,5 0,0 C D A L H E J F I K G B M -0,5 0 50 100 150 200 250 300 350 400 450 Number of cycles High ratio between Ca-silicates amorphous and residual phases Higher sintering degree
1. Statistical analysis: extraction of the main components A. Extraction of the main components (StatSoft software 6.0) Inserted variables microstructural: BD, TP, Φ 50, Φ 90, P3, BET compositional: amount of Ca-silicates, Quartz and amorphous Number of cycles Factor 1 1,0 0,8 0,6 0,4 0,2 0,0-0,2-0,4-0,6 BD Qz F90 P3 F50 Freeze/thaw cycles Am Ca-sil TP -0,8 BET -1,0-1,0-0,8-0,6-0,4-0,2 0,0 0,2 0,4 0,6 0,8 1,0 Factor 2
Multiple regression analysis (Statsoft Statistica 6.0) Dependent variable: Number of freeze/thaw cycles Independent variables: Φ 50, Φ 90, BET, BD, TP, P3, Quartz, Amorphous and Ca-silicates Stepwise multiple regression analysis. R = 0.97025; R 2 = 0.94138; p < 0.00184 N=13 ß Std. Err. B Std. Err. p-level Intercept -7063.22 1052.63 0.0005 BET -0.821 0.201-338.80 83.12 0.0065 Amorphous -0.775 0.381-9.20 4.53 0.0885 BD 1.857 0.231 4227.63 527.29 0.0002 Quartz -0.882 0.368-14.33 5.98 0.0054 Ca-silicates 0.779 0.223 7.34 3.99 0.0110 Φ 50 0.252 0.182 101.05 73.41 0.2180
Multiple regression analysis : observed vs predicted values of freeze/thaw durability 450 400 R 2 = 0.941 B HA D 350 300 E L Observed values 250 200 150 100 50 0 C K M F I G J 4,0 3,0 2,0 1,0 Ca-silicates/Amorphous -50 0,0-50 0 50 100 150 200 250 300 350 400 450 A B C D E F G H I J K L M Predicted values Confidenza 95%
Conclusions Pore amount and pore size/morphology: Expected general trend only for samples with medium performances; Controversial informations about the excellent behaviour of the others samples Durability indices Maage: all samples are no frost resistant, but A according with its greater pore dimensions; Vincenzini: critical diameter is nor statistical significant for our sample; Arnott: good correlations are provided but just for samples having medium peformances; presence of many exceptions. Robinson: very scattered data. Behaviour of the most resistant products A deep understanding of their performances needs also the analysis of the production process and suggests the possible role played by the relative amount of mineralogical phases
Conclusions Designing of high performance roofing tiles: is it possible? Microstructural properties Open and total porosity Amount of pores >3 μm Pore dimensions Evaluation of structural properties, i.e body elasticity Sintering process and conditions Sintering degree Amount of new formed crystalline phases Ratio between amorphous, residual and new crystalline phases Highly frost resistant bricks
Process and product design Process variables: Firing temperature ( 900 C ) Increased amount of new formed phases; Optimum for frost resistance: Amorphous + Illite 25%; Crystalline/amorphous + Illite 1.5; Product variables Modulation of microstructure in terms of pore dimensions (Φ 50 and pores >3 μm) Increasing CaO content of the starting formulations Attention to the microstructure in order to obtain higher porosity but controlling the pore dimensions (BET should be lower)
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