Concrete Innovation Forum - February 14 th, 2010
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1 ICT and statistics as useful tools for the optimisation of the production process of concrete Peter Minne Robby Caspeele Concrete Innovation Forum - February 14 th, 2010
2 Statistics can be difficult
3 Remember this feeling?
4 Objection 1 However some objections Objection 2 Objection 3 Do not worry about your difficulties in Mathematics. I can assure you mine are still greater. Albert Einstein Robby Caspeele 4
5 Content 1. Introduction using statistics in concrete production 2. Water demand and consistency prediction models the building stones for computational concrete design 3. The Mix design, Mix proportioning software a multifunctional ICT tool 4. Some properties of raw material and their variation input for statistical simulation models 5. Case study influence of variation of concrete properties on the water demand 6. Conclusions 5
6 Content 1. Introduction using statistics in concrete production 2. Water demand and consistency prediction models the building stones for computational concrete design 3. The Mix design, Mix proportioning software a multifunctional ICT tool 4. Some properties of raw material and their variation input for statistical simulation models 5. Case study influence of variation of concrete properties on the water demand 6. Conclusions 6
7 Process steering approach effect input 7
8 Process steering approach 8
9 Examples of statistical tools for quality control
10 Probabilistic design and evaluation of conformity criteria Unsafe region AOQL = 5% EN Numerical analyses Monte Carlo simulations 10
11 Bayesian statistics using all available information Updating strength distributions based on test t results Bayesian non-linear regression for updating strength prediction models Assessment of in-situ characteristic concrete strength Influence of conformity control on concrete properties p Influence of conformity control on the safety level of concrete structures 11
12 Design of concrete strength prediction models Co ompressive e strength f c [MPa] non-linear regression W/C 12
13 Updating concrete strength prediction models
14 Updating concrete strength prediction models 14
15 Quality control charts C i i x j j
16 Quality control charts Concrete production at a concrete plant 16
17 Quality control charts Actions should be taken in order to avoid nonconformities 17
18 Quality control charts NON-CONFORMITY! 18
19 Monte Carlo simulations Random numbers: realizations of U(0,1) pseudo-random numbers r i Monte Carlo simulations How to calculate realizations x from X with F X (x)? x i 1 F r or r F x X i i X i 19
20 variable 1 variable n Monte Carlo simulations Monte Carlo simulations 100 MODEL RESPONSE Frequency Water demand 20
21 Content 1. Introduction using statistics in concrete production 2. Water demand and consistency prediction models the building stones for computational concrete design 3. The Mix design, Mix proportioning software a multifunctional ICT tool 4. Some properties of raw material and their variation input for statistical simulation models 5. Case study influence of variation of concrete properties on the water demand 6. Conclusions 21
22 Water demand and consistency prediction models Modelling of solids and voids: 1 grain 3D model: cubes Volume solids: Volume voids: Voids ratio: 3 D X 3 U X D
23 Water demand and consistency prediction models Modelling of solids and voids: 2 grains m, z spatial parameters D0 md1 D D0+mD1 X" D0+mD1 X0" (1+z)D0 Volume of voids of fine grains increases due to the introduction of course Volume of voids of course grains increases (wall effect) due to the introduction of fine grains " (loosening effect) U1 U1 " U0 U 0 23
24 Water demand and consistency prediction models Vo oids ratio U Power s diagram U 1 - In the range U 0 M : U 1 U n U 0 n( ( 1 U0 - In the range MU 1 : 0.6 U 0 0 U n nu1 0.5 U0 n M 0.4 (1 U 0 U ) ) ( O M X Fine fraction n n X X U M U0 (1 U ( 0 ) U0U1 (1 U U ( 0 U1 ) 24
25 Water demand and consistency prediction models Power s diagram Dewar s real mix + U 0 U 1 U n U 0 U 0 U 1 U 1 25
26 Water demand and consistency prediction models Power s diagram Dewar s real mix " U size ratio r=0.080 U " U U 1 + F 0.7 U Voids ratio U A B C D E = M X Fine fraction n 26
27 Water demand and consistency prediction models 0.3 D 1.75 D1 0 3 D1 7.5 D1 27
28 Water demand and consistency prediction models 1.0 Influence of ratio of average grain size r U0 = 0.80 r=1 A size ratio E F U 1 = 0.80 Vo oids ratio U B r=0.2 C r=0.04 r=0.01 D r r av. grain size of fine grains av. grain size of course grains 0.2 r= Fine fraction n 28
29 Water demand and consistency prediction models cement 1+2 sand 1+2 gravel cement 3 + sand 3 + gravel 3 cement + sand voids skeleton 29
30 Water demand and consistency prediction models Modelling the consistency Reference slump = 50 mm The difference in water demand for other slump values independent of raw material and concrete parameters Empirical function for the difference in water demand F s F s 1 SL 50 6 SL 50 30
31 Content 1. Introduction using statistics in concrete production 2. Water demand and consistency prediction models the building stones for computational concrete design 3. The Mix design, Mix proportioning software a multifunctional ICT tool 4. Some properties of raw material and their variation input for statistical simulation models 5. Case study influence of variation of concrete properties on the water demand 6. Conclusions 31
32 Mix design, mix proportioning: a multifunctional ICT tool 32
33 Mix design, mix proportioning: a multifunctional ICT tool Management of raw materials Grondstoffen d t Cement Cement Toevoegsel Toevoegsel Fijn Granulaat Fijn Granulaat Grof Granulaat Grof Granulaat Hulpstof Hulpstof Parameters Korrelkromme Abosolute volumieke massa Waterbehoefte bij standaard consistentie Blaine waarde Beta-p-waarde Cementsterkte (1, 2, 7, 28 dagen) Natrium-equivalent Chloride gehalte k-waarde Berekende ee e degoo grootheden Gemiddelde korrelafmeting Holle Ruimten Ratio Cementtypes Portlandtypes Portlandcomposietcement Hoogovencement Samengesteld cement Toevoegsels Vliegas Kalksteen Parameters Korrelkromme Korrelvolumieke massa Schijnbare volumieke massa Waterabsorptie Natrium-equivalent Chloride gehalte Deeltjes < 63 µm Berekende grootheden Gemiddelde korrelafmeting Holle Ruimten Ratio Specifiek oppervlak Specifiek oppervlak Day Fijnheidsmodulus Parameters Volumieke massa Droge materie Chloride gehalte Natrium-equivalent Types Plastificeerder Superplastificeerder Luchtbelvormer 33
34 Mix design, mix proportioning: a multifunctional ICT tool Concrete specifications according to NBN EN and NBN B
35 Mix design, mix proportioning: a multifunctional ICT tool Processing of raw materials Verwerking V granulaten l ten - Last analysis - Random analysis - Mean of analyses - Mean over a time period - Previous calculated mean Descriptive statistics of variables 35
36 Mix design, mix proportioning: a multifunctional ICT tool Granulatensamenstelling Design of inert skeleton Design of grain size distribution - target curves - Power s diagram and Dewar real mixes 36
37 Mix design, mix proportioning: a multifunctional ICT tool Concrete strength th prediction models - Feret - Bolomey - Abrams -Dutron - Dewar -Buist - Hanke 37
38 Mix design, mix proportioning: a multifunctional ICT tool Mix Mix Design Ontwerp betonmengsel 38
39 Mix design, mix proportioning: a multifunctional ICT tool Mix Mix Proportioning Production of actual concrete mixes Variatie van grondstoffen Variatie in de productie Vochtgehalte grondstoffen Mix Proportioning Aanpassing recuperatiewater Beoordeling ten opzichte van bestaande Mix Design 39
40 Mix design, mix proportioning: a multifunctional ICT tool Updating concrete strength prediction models 40
41 Mix design, mix proportioning: a multifunctional ICT tool Simulation of water demand and concrete strength 41
42 Mix design, mix proportioning: a multifunctional ICT tool Cusum Quality control of concrete production 42
43 Content 1. Introduction using statistics in concrete production 2. Water demand and consistency prediction models the building stones for computational concrete design 3. The Mix design, Mix proportioning software a multifunctional ICT tool 4. Some properties of raw material and their variation input for statistical simulation models 5. Case study influence of variation of concrete properties on the water demand 6. Conclusions 43
44 Some properties of raw materials and their variation - The raw materials which are used for concrete have intrisic properties and fabrication characteristics - All these properties and characteristics are subjected to large variations - Only limited attention is paid to these important variations -Most often only the econcrete ceestrength is systematically y predicted pedcedand monitored in time - The water demand and consistency are most often not predicted, although these are significant variables influencing many concrete properties 44
45 Some properties of raw materials and their variation sand (fine sand 0/2) - grain size distributions analyses in time d [ mm] Y [%] d[mm] 45
46 analyses in time of passing through 100 Some properties of raw materials and their variation DOORVAL fijn zand 0/2 fijn zand_0/2_fractie fijn zand_0/2_fractie fijn zand_0/2_fractie Y [%] tijd
47 Some properties of raw materials and their variation sand (fine sand 0/2) - grain size distributions analyses of grain size distribution Histogram fractie mm Histogram fractie mm Frequentie Frequentie Doorval [%] Meer Doorval [%] r Meer 47
48 Some properties of raw materials and their variation sand (fine sand 0/2) - grain size distributions Log (mean size of the size fraction)=0.5(log(upper size)+log(lower size)) analyses of derived properties: mean size vol.propn x log(mean size of the size fraction Log(mean size) ) mean size = mm d [ mm] 48
49 Some properties of raw materials and their variation sand (fine sand 0/2) - grain size distributions Distribution of mean size Monte Carlo simulations of mean size [mm] gemiddelde st. Dev Freque entie Meer Mean size [mm] 49
50 Some properties of raw materials and their variation sand (fine sand 0/2) - grain size distributions analyses of derived properties: volumetric mass and voids ratio grain density (pyknometer) bulk density (recipient) voids ratio 50
51 Some properties of raw materials and their variation sand (fine sand 0/2) - grain size distributions Distribution of voids ratio Monte Carlo simulations of voids ratio Freque entie [-] gemiddelde st. Dev Voids ratio [-] Meer 51
52 Content 1. Introduction using statistics in concrete production 2. Water demand and consistency prediction models the building stones for computational concrete design 3. The Mix design, Mix proportioning software a multifunctional ICT tool 4. Some properties of raw material and their variation input for statistical simulation models 5. Case study influence of variation of concrete properties on the water demand 6. Conclusions 52
53 Influence of variation of concrete properties on the water demand variable 1 Monte Carlo simulations water demand MODEL RESPONSE variable n Frequency Water demand 53
54 Influence of variation of concrete properties on the water demand Concrete recipe Mix Design [kg/m³] CEM III/A 42.5 LA 260 Fly ash 15 Water 170 Sand 0/2 504 Sand 0/4 393 Coarse Aggregates 6/ Water reducer (Sky)
55 Influence of variation of concrete properties on the water demand Statistical characteristics of raw materials (based on an observation period of 1 year) Constituents Mean size Voids ratio mean [mm] (st.dev.) mean [-] (st.dev.) CEM III/A 42.5 LA ( ) (0.0126) Fly ash ( ) 00105) (0.0247) 0247) Sand 0/ (0.0205) (0.0822) Sand 0/ (0.1275) (0.1114) Coarse Aggregates (1.3259) (0.0605) 55
56 Influence of variation of concrete properties on the water demand Random simulation of the water demand Simulation Water demand mean [kg] (st.dev.) Properties of all constituents are varying (9.29) Only properties of the binder are varying (1.18) Only properties of sand are varying 173.9(581) (5.81) Only properties of coarse aggregates are varying (7.29) Frequency Concrete strength results estimation of the actual variation in water demand 40 Water demand mean [kg] (st. dev.) 20 Actual variation of the water (11.52) Water demand
57 Influence of variation of concrete properties on the water demand Random simulation of grain size distribution grof_zand_onder grof_zand_boven grof_tussen grof_zand_onder grof_zand_boven f d b grof_tussen
58 Influence of variation of concrete properties on the water demand cumulativ ve passing (%) diameter (mm) Simulation good coarse_sand_u good coarse_zand_a a good coarse_sand_b sand EN12620 sand NBN B11-011_u sand NBN B11-011_a sand NBN B11-011_b Water demand mean [kg] (st.dev.) Sand according to NBN (8.78) Sand according to EN (10.97) Good concrete sand (4.23)
59 Content 1. Introduction using statistics in concrete production 2. Water demand and consistency prediction models the building stones for computational concrete design 3. The Mix design, Mix proportioning software a multifunctional ICT tool 4. Some properties of raw material and their variation input for statistical simulation models 5. Case study influence of variation of concrete properties on the water demand 6. Conclusions 59
60 Conclusions The simulated water demand are comparable with the water demand obtained by the strength results diagrams of Powers and the theory of the particle mixtures of Dewar provide a useful method for estimating the water demand The magnitude of the variation in water demand is of the same magnitude as the variation in the actual water content the variability of the properties of the raw materials is the main origin of the variability in water demand More stringent specifications are required for the acceptable boundaries of the grain size distribution according to the standard The models can be used to predict the water demand for new mix designs and to predict the water demand when the raw materials parameters are changed 60
61 Thank you for your attention! Ing. Peter Minne Gebroeders Desmetstraat Gent [email protected] Dr. ir. Robby Caspeele Technologiepark-Zwijnaarde Zwijnaarde [email protected]
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