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MIXDESIGNMIXDESIGN
MIXPROPORTIONING
MIXPROPORTIONING
ICT andICT and statisticsstatistics asas usefuluseful toolstoolsICT and ICT and statisticsstatistics as as usefuluseful tools tools forfor the the optimisationoptimisation of the of the productionproduction processprocess ofof concreteconcreteproductionproduction processprocess of of concreteconcrete
Peter Minne Robby Caspeele
Concrete Innovation Forum - February 14th, 2010
Statisticscan becan bedifficult…
R b thi f li ?Remember this feeling ?
MIXDESIGNMIXDESIGN However some objectionsMIX
PROPORTIONINGMIX
PROPORTIONING
However… some objections
Objection 2Objection 1
j
Objection 3
“Do not worry about your difficulties in Mathematics.I can assure you mine are still greater.”
j
4Albert EinsteinRobby Caspeele
MIXDESIGNMIXDESIGN ContentMIX
PROPORTIONINGMIX
PROPORTIONING
Content
1. Introductionusing statistics in concrete production… using statistics in concrete production
2. Water demand and consistency prediction modelsthe building stones for computational concrete design… 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 Conclusions6. Conclusions5
MIXDESIGNMIXDESIGN ContentMIX
PROPORTIONINGMIX
PROPORTIONING
Content
1. Introductionusing statistics in concrete production… using statistics in concrete production
2. Water demand and consistency prediction modelsthe building stones for computational concrete design… 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 Conclusions6. Conclusions6
MIXDESIGNMIXDESIGN Process steering approachMIX
PROPORTIONINGMIX
PROPORTIONING
Process steering approach
effecteffect
input
7
MIXDESIGNMIXDESIGN Process steering approachMIX
PROPORTIONINGMIX
PROPORTIONING
Process steering approach
8
Examples of statistical tools for quality control
MIXDESIGNMIXDESIGN Probabilistic design and MIX
PROPORTIONINGMIX
PROPORTIONINGevaluation of conformity criteria
Unsafe regionAOQL = 5%AOQL = 5%
EN 206-1
Numerical analyses
C10
Monte Carlo simulations
MIXDESIGNMIXDESIGN Bayesian statistics …MIX
PROPORTIONINGMIX
PROPORTIONING… using all available information
Bayesian non-linearregression for updating
Updating strengthdistributions based on
t t lt regression for updating strength prediction models
test results
Assessment of in-situcharacteristic concrete
strength Influence of conformitycontrol on concrete
propertiesp p
Influence of conformitycontrol on the safety level
of concrete structures11
of concrete structures
MIXDESIGNMIXDESIGN Design of concrete strengthMIX
PROPORTIONINGMIX
PROPORTIONINGprediction models
h f c
[MPa
]e
stre
ngth
ompr
essi
ve
non-linear regression
Co
12
W/C
MIXDESIGNMIXDESIGN Updating concrete strengthMIX
PROPORTIONINGMIX
PROPORTIONINGprediction models
MIXDESIGNMIXDESIGN Updating concrete strengthMIX
PROPORTIONINGMIX
PROPORTIONINGprediction models
14
MIXDESIGNMIXDESIGN Quality control chartsMIX
PROPORTIONINGMIX
PROPORTIONING
Quality control charts
i
jjxiC
10
j 1
15
MIXDESIGNMIXDESIGN Quality control chartsMIX
PROPORTIONINGMIX
PROPORTIONING
Quality control charts
Concrete production at a concrete plant
16
MIXDESIGNMIXDESIGN Quality control chartsMIX
PROPORTIONINGMIX
PROPORTIONING
Quality control charts
Actions should be takenin order to avoid non-conformities
17
MIXDESIGNMIXDESIGN Quality control chartsMIX
PROPORTIONINGMIX
PROPORTIONING
Quality control charts
NON-CONFORMITY !
18
MIXDESIGNMIXDESIGN Monte Carlo simulationsMIX
PROPORTIONINGMIX
PROPORTIONING
Monte Carlo simulations
Random numbers: realizations of U(0,1)
“pseudo-random numbers” ripseudo random numbers ri
Monte Carlo simulations
How to calculate realizations x from X with FX(x)?
iXii1
Xi xFrorrFx iXiiXi xFrorrFx
19
MIXDESIGNMIXDESIGN Monte Carlo simulationsMIX
PROPORTIONINGMIX
PROPORTIONING
Monte Carlo simulations
i bl 1
Monte Carlo simulations
100
120
140
MODELvariable 1
variable n
RESPONSE
0
20
40
60
80
100
.2
.8
.4
.9
.5
.1
.6
.2
.8
.3
.9
Freq
uenc
y
…
136
141
147
152
158
164
169
175
180
186
191
Water demand
20
MIXDESIGNMIXDESIGN ContentMIX
PROPORTIONINGMIX
PROPORTIONING
Content
1. Introductionusing statistics in concrete production… using statistics in concrete production
2. Water demand and consistency prediction models… the building stones for computational concrete design… 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 Conclusions6. Conclusions21
MIXDESIGNMIXDESIGN Water demand and consistencyMIX
PROPORTIONINGMIX
PROPORTIONINGprediction models
Modelling of solids and voids: 1 grain
3D model: cubes
Volume solids:
Volume voids: 3X
3D
Voids ratio: 3
3
DXU
22
MIXDESIGNMIXDESIGN Water demand and consistencyMIX
PROPORTIONINGMIX
PROPORTIONINGprediction models
Modelling of solids and voids: 2 grains
mD1D0
X"D +mD
m, z spatial parameters
X0"D0+mD1
X"D0+mD1
(1+z)D0
Volume of voids of fine grains increases
Volume of voids of course grains increasesdue to the introduction of fine grains
(loosening effect)
gdue to the introduction of course
(wall effect)
"UU 23"
00 UU (loosening effect) 11 UU
MIXDESIGNMIXDESIGN Water demand and consistencyMIX
PROPORTIONINGMIX
PROPORTIONINGprediction models
0.9
1.0
U1
- In the range U0M :
)1( UnUU
Power’s diagramU1
0.7
0.8
- In the range MU1 :
)1( 00 UnUUn
U
oids
rat
io U
0.5
0.6 U0 1nUUn
)1(0
UUUnM
U0
0 2
Vo
0.3
0.4 )1( 10 UUM
)1(0
UUnX
00.1
0.2
X
M
)1(10
UUUUUM
)1( 0UX O
X
M
Fine fraction n
0.500.1
X0.30.2 0.4 0.80.6 0.7 1.00.9
24
)1( 10 UU
MIXDESIGNMIXDESIGN Water demand and consistencyMIX
PROPORTIONINGMIX
PROPORTIONINGprediction models
Power’s diagram – Dewar’s real mix
+
U0 U1 UnU0U0’’
25U1U1’’
MIXDESIGNMIXDESIGN Water demand and consistencyMIX
PROPORTIONINGMIX
PROPORTIONINGprediction models
"
1.2
U"
Power’s diagram – Dewar’s real mix
size ratior=0.080
0.9
1.0
1.1 U0"
U1
U1
+0.7
U 0 6 U
0.8F +
Voi
ds r
atio
B
A
0.4
0.6
0.5
U0
D
E
=
0.1
0.2 M
0.3DC
Fine fraction n
0.500.1
X0.30.2 0.4 0.80.6 0.7 1.00.9 26
MIXDESIGNMIXDESIGN Water demand and consistencyMIX
PROPORTIONINGMIX
PROPORTIONINGprediction models
1D3.0 1D75.0 1D3 1D5.7
27
MIXDESIGNMIXDESIGN Water demand and consistencyMIX
PROPORTIONINGMIX
PROPORTIONINGprediction models
0.9
1.0Influence of ratio of average grain size r
0.8
0.7
0
A
= 0.80U r=1size ratio
= 0.80
E
1
F
U
D
oids
rat
io U 0.6
0.5B
C
r=0.2grains course of size grain av.
grains fine of size grain av.r
r=0.04Vo
0.4
0.3r=0.01
0
r ↑0.2
0.1
r=0
28
0.70.5 0.60.40.2 0.30.1
Fine fraction n
1.00.90.8
MIXDESIGNMIXDESIGN Water demand and consistencyMIX
PROPORTIONINGMIX
PROPORTIONINGprediction models
cement 1+2 sand 1+2 gravel 1+2
+ cement 3 + sand 3 + gravel 3
29
cement + sand voids skeleton
MIXDESIGNMIXDESIGN Water demand and consistencyMIX
PROPORTIONINGMIX
PROPORTIONINGprediction 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 Fs
501 SLF 506
501
SLSLFs
30
MIXDESIGNMIXDESIGN ContentMIX
PROPORTIONINGMIX
PROPORTIONING
Content
1. Introductionusing statistics in concrete production… using statistics in concrete production
2. Water demand and consistency prediction modelsthe building stones for computational concrete design… 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 Conclusions6. Conclusions31
MIXDESIGNMIXDESIGN Mix design, mix proportioning:MIX
PROPORTIONINGMIX
PROPORTIONINGa multifunctional ICT tool
32
MIXDESIGNMIXDESIGN Mix design, mix proportioning:MIX
PROPORTIONINGMIX
PROPORTIONINGa multifunctional ICT tool
G d t ffGrondstoffen
Management of raw materials
GrondstoffenGrondstoffenCementCement Fijn GranulaatFijn Granulaat Grof GranulaatGrof GranulaatToevoegselToevoegsel HulpstofHulpstof
Parameters
Korrelkromme Abosolute volumieke massa Waterbehoefte bij standaard consistentie
Cementtypes
Portlandtypes Portlandcomposietcement Hoogovencement
Parameters
Volumieke massa Droge materie Chloride gehalte
Parameters Korrelkromme Korrelvolumieke massa Schijnbare volumieke massa Waterbehoefte bij standaard consistentie
Blaine waarde Beta-p-waarde Cementsterkte (1, 2, 7, 28 – dagen) Natrium-equivalent Chloride gehalte k-waarde
Berekende grootheden
Hoogovencement Samengesteld cement
Toevoegsels
Vliegas Kalksteen
Chloride gehalte Natrium-equivalent
Waterabsorptie Natrium-equivalent Chloride gehalte Deeltjes < 63 µm
Berekende grootheden Gemiddelde korrelafmeting Holle Ruimten Ratio S ifi k l k
Types
Plastificeerder S l tifi de e e de g oo ede
Gemiddelde korrelafmeting Holle Ruimten Ratio
Specifiek oppervlak Specifiek oppervlak Day Fijnheidsmodulus
Superplastificeerder Luchtbelvormer
33
MIXDESIGNMIXDESIGN Mix design, mix proportioning:MIX
PROPORTIONINGMIX
PROPORTIONINGa multifunctional ICT tool
Concrete specifications according to NBN EN 206-1 and NBN B 15-001
34
MIXDESIGNMIXDESIGN Mix design, mix proportioning:MIX
PROPORTIONINGMIX
PROPORTIONINGa multifunctional ICT tool
V ki l tV erw erk in g g ran ula ten- Last analysis
Processing of raw materials
V erw erk in g g ran ula tenV erw erk in g g ran ula ten- Random analysis
- Mean of analyses
- Mean over a time period
- Previous calculated mean
Descriptive statistics of variables
35
MIXDESIGNMIXDESIGN Mix design, mix proportioning:MIX
PROPORTIONINGMIX
PROPORTIONINGa multifunctional ICT tool
G ranulatensam enstellingG ranulatensam enstelling Design of inert skeleton
Design of grain size distribution
- target curves
- Power’s diagram and Dewar real mixesDewar real mixes
36
MIXDESIGNMIXDESIGN Mix design, mix proportioning:MIX
PROPORTIONINGMIX
PROPORTIONINGa multifunctional ICT tool
C t t th di ti d l
Feret
Concrete strength prediction models
- Feret
- Bolomey
Abrams- Abrams
- Dutron
- Dewar- Dewar
- Buist
- HankeHanke
37
MIXDESIGNMIXDESIGN Mix design, mix proportioning:MIX
PROPORTIONINGMIX
PROPORTIONINGa multifunctional ICT tool
Mix DesignMix Design“Ontwerp betonmengsel”
38
MIXDESIGNMIXDESIGN Mix design, mix proportioning:MIX
PROPORTIONINGMIX
PROPORTIONINGa multifunctional ICT tool
Mix ProportioningMix ProportioningProduction of actual concrete mixes
Variatie van
grondstoffen
Variatie in
de productie
Variatie van
grondstoffen
Variatie in
de productie
Mix Proportioning
Vochtgehalte grondstoffen
Mix Proportioning
Vochtgehalte grondstoffen
Mix ProportioningAanpassing recuperatiewaterMix ProportioningAanpassing recuperatiewater
Beoordeling ten opzichte vanbestaande Mix DesignBeoordeling ten opzichte vanbestaande Mix Design
39
MIXDESIGNMIXDESIGN Mix design, mix proportioning:MIX
PROPORTIONINGMIX
PROPORTIONINGa multifunctional ICT tool
Updating concrete strength prediction models
40
MIXDESIGNMIXDESIGN Mix design, mix proportioning:MIX
PROPORTIONINGMIX
PROPORTIONINGa multifunctional ICT tool
Simulation of water demand and concrete strength
41
MIXDESIGNMIXDESIGN Mix design, mix proportioning:MIX
PROPORTIONINGMIX
PROPORTIONINGa multifunctional ICT tool
CusumCusumQuality control of concrete productionQuality control of concrete production
42
MIXDESIGNMIXDESIGN ContentMIX
PROPORTIONINGMIX
PROPORTIONING
Content
1. Introductionusing statistics in concrete production… using statistics in concrete production
2. Water demand and consistency prediction modelsthe building stones for computational concrete design… 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 Conclusions6. Conclusions43
MIXDESIGNMIXDESIGN Some properties of raw materialsMIX
PROPORTIONINGMIX
PROPORTIONINGand their variation
- The raw materials which are used for concrete have intrisic properties and fabrication characteristicsfabrication characteristics
- All these properties and characteristics are subjected to large variations
- Only limited attention is paid to these important variations
- Most often only the concrete strength is systematically predicted and os o e o y e co c e e s e g s sys e a ca y p ed c ed a dmonitored in time
- The water demand and consistency are most often not predicted, althoughy p gthese are significant variables influencing many concrete properties
44
MIXDESIGNMIXDESIGN Some properties of raw materialsMIX
PROPORTIONINGMIX
PROPORTIONINGand their variation
sand (fine sand 0/2) - grain size distributions
l i ti80
100
analyses in time
40
60
100
0
20
60
80
0.1 1 10
d [ mm]
20
40
Y [%
]
0
20
0.1 1 10
d [mm]
45
d [mm]
MIXDESIGNMIXDESIGN Some properties of raw materials
DOORVAL fijn zand 0/2fijn zand_0/2_fractie 0.125-0.250
MIXPROPORTIONING
MIXPROPORTIONING
and their variation
100
fijn zand_0/2_fractie 0.250-0.500
fijn zand_0/2_fractie 0.125-0.500 analyses in time of ‘passing through’
80
60
Y [%
]
40
20
0
11-01-08 20-04-08 29-07-08 6-11-08 14-02-09 25-05-09 2-09-09 11-12-09 21-03-10
tijd
46
MIXDESIGNMIXDESIGN Some properties of raw materialsMIX
PROPORTIONINGMIX
PROPORTIONINGand their variation
sand (fine sand 0/2) - grain size distributions
analyses of grain size distribution
Histogram fractie 0.125 -0.250mm Histogram fractie 0.250 -0.500mm
25
30
20
25
15
20
Freq
uent
ie
10
15Fr
eque
ntie
0
5
10
0
5
7 5 5 5 5 5 5 5 r
47
0
9.2
13.0
25
16.8
5
20.6
75
24.5
28.3
25
32.1
5
35.9
75
Mee
r
Doorval [%]
54.7
58.0
375
61.3
75
64.7
125
68.0
5
71.3
875
74.7
25
78.0
625
Mee
r
Doorval [%]
MIXDESIGNMIXDESIGN Some properties of raw materialsMIX
PROPORTIONINGMIX
PROPORTIONINGand 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))
)fractionsizetheofsizemeanlog(xpropn.vol)sizemean(Log
100
analyses of derived properties: mean size
80
mean size = 0.3145 mm
40
60
20
48
00.1 1 10
d [ mm]
MIXDESIGNMIXDESIGN Some properties of raw materialsMIX
PROPORTIONINGMIX
PROPORTIONINGand their variation
sand (fine sand 0/2) - grain size distributions Distribution of mean size
Monte Carlo simulations of mean size
25
Monte Carlo simulations of mean size
[mm]gemiddelde 0.3099
15
20
entie
gst. Dev 0.0187
5
10Freq
ue
0
5
794
9165
3039 615
3284
4065
3529
6515
Mee
r
49
0.2
0.29 0.3
0.31 0.3
0.34 0.3
0.36 M
Mean size [mm]
MIXDESIGNMIXDESIGN Some properties of raw materialsMIX
PROPORTIONINGMIX
PROPORTIONINGand their variation
sand (fine sand 0/2) - grain size distributions
analyses of derived properties: volumetric mass and voids ratio
i d it ( k t )• grain density (pyknometer)
• bulk density (recipient)
• voids ratio
50
MIXDESIGNMIXDESIGN Some properties of raw materialsMIX
PROPORTIONINGMIX
PROPORTIONINGand their variation
sand (fine sand 0/2) - grain size distributions Distribution of voids ratio
Monte Carlo simulations of voids ratio
1820
Monte Carlo simulations of voids ratio
[-]gemiddelde 1.1201
10121416
entie
st. Dev 0.0801
468
10
Freq
ue
024
9042
8375
9475 125
9475
3875
0025
6625
Mee
r
0.9
0.95
18
0.99
9
1.04
71
1.09
1.14
23
1.19
0
1.23
76 M
Voids ratio [-]51
MIXDESIGNMIXDESIGN ContentMIX
PROPORTIONINGMIX
PROPORTIONING
Content
1. Introductionusing statistics in concrete production… using statistics in concrete production
2. Water demand and consistency prediction modelsthe building stones for computational concrete design… 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 Conclusions6. Conclusions52
MIXDESIGNMIXDESIGN Influence of variation of concrete MIX
PROPORTIONINGMIX
PROPORTIONINGproperties on the water demand
i bl 1 100
120
140Monte Carlo simulations water demand
MODELvariable 1
variable n
RESPONSE
0
20
40
60
80
100
.2
.8
.4
.9
.5
.1
.6
.2
.8
.3
.9
Freq
uenc
y
…
136
141
147
152
158
164
169
175
180
186
191
Water demand
53
MIXDESIGNMIXDESIGN Influence of variation of concrete MIX
PROPORTIONINGMIX
PROPORTIONINGproperties on the water demand
Concrete recipe
Mix Design [kg/m³]CEM III/A 42.5 LA 260
Fly ash 15Fly ash 15Water 170
Sand 0/2 504Sand 0/4 393Sand 0/4 393
Coarse Aggregates 6/20 954Water reducer (Sky) 1.62
54
MIXDESIGNMIXDESIGN Influence of variation of concrete MIX
PROPORTIONINGMIX
PROPORTIONINGproperties on the water demand
Statistical characteristics of raw materials(based on an observation period of 1 year)
M i V id ti
(based on an observation period of 1 year)
Constituents Mean size mean [mm] (st.dev.)
Voids ratiomean [-] (st.dev.)
CEM III/A 42.5 LA 0.01202 (0.00068) 0.8643 (0.0126) Fly ash 0 01949 (0 00105) 0 6787 (0 0247)Fly ash 0.01949 (0.00105) 0.6787 (0.0247)
Sand 0/2 0.3114 (0.0205) 1.1463 (0.0822) Sand 0/4 1.0259 (0.1275) 0.7794 (0.1114)
Coarse Aggregates 14.0771 (1.3259) 0.9183 (0.0605)Coarse Aggregates 14.0771 (1.3259) 0.9183 (0.0605)
55
MIXDESIGNMIXDESIGN Influence of variation of concrete MIX
PROPORTIONINGMIX
PROPORTIONINGproperties on the water demand
Random simulation of the water demand
Simulation Water demand Simulation mean [kg] (st.dev.) Properties of all constituents are varying 169.8 (9.29) Only properties of the binder are varying 170.4 (1.18)
O l ti f d i 173 9 (5 81)
100
120
140Only properties of sand are varying 173.9 (5.81)
Only properties of coarse aggregates are varying 171.1 (7.29)
60
80
100
Freq
uenc
y Concrete strength results estimation of the actual variation in water demand
0
20
40 Water demand
mean [kg] (st. dev.) Actual variation of the water 173.5 (11.52)
56
136
.2
141
.8
147
.4
152
.9
158
.5
164
.1
169
.6
175
.2
180
.8
186
.3
191
.9
Water demand
MIXDESIGNMIXDESIGN Influence of variation of concrete MIX
PROPORTIONINGMIX
PROPORTIONING
100100100100100100
properties on the water demand
60
80
100
grof_zand_onder60
80
100
grof_zand_onder60
80
100
grof_zand_onder60
80
100
grof_zand_onder60
80
100
grof_zand_onder60
80
100
grof_zand_onderRandom simulation of
20
40
grof_zand_boven
grof_tussen
20
40
grof_zand_boven
grof_tussen
20
40
grof_zand_boven
grof_tussen
20
40
grof_zand_boven
grof_tussen
20
40
grof_zand_boven
grof_tussen
20
40
grof_zand_boven
grof_tussen
Random simulation of grain size distribution
100
00.01 0.1 1 10
100
00.01 0.1 1 10
100
00.01 0.1 1 10
100
00.01 0.1 1 10
100
00.01 0.1 1 10
100
00.01 0.1 1 10
60
80
grof_zand_onder
f d b
60
80
grof_zand_onder
f
60
80
grof_zand_onder
f d b
60
80
grof_zand_onder
f d b
60
80
grof_zand_onder
f
60
80
grof_zand_onder
f
20
40
grof_zand_boven
grof_tussen
20
40
grof_zand_boven
grof_tussen
20
40
grof_zand_boven
grof_tussen
20
40
grof_zand_boven
grof_tussen
20
40
grof_zand_boven
grof_tussen
20
40
grof_zand_boven
grof_tussen
570
0.01 0.1 1 100
0.01 0.1 1 10
00.01 0.1 1 10
00.01 0.1 1 10
00.01 0.1 1 10
00.01 0.1 1 10
MIXDESIGNMIXDESIGN Influence of variation of concrete MIX
PROPORTIONINGMIX
PROPORTIONINGproperties on the water demand
80
100
60
ve p
assin
g (%
)
good coarse_sand_u
good coarse zand a
20
40
cum
ulat
iv good coarse_zand_a
good coarse_sand_b
sand EN12620
sand NBN B11-011_u
00.01 0.1 1 10
sand NBN B11-011_a
sand NBN B11-011_b
diameter (mm)
Simulation Water demand
mean [kg] (st.dev.) Sand according to NBN 163.69 (8.78)g ( )
Sand according to EN 12620 184.9 (10.97) “Good” concrete sand 166.5 (4.23)
MIXDESIGNMIXDESIGN ContentMIX
PROPORTIONINGMIX
PROPORTIONING
Content
1. Introductionusing statistics in concrete production… using statistics in concrete production
2. Water demand and consistency prediction modelsthe building stones for computational concrete design… 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 Conclusions6. Conclusions59
MIXDESIGNMIXDESIGN ConclusionsMIX
PROPORTIONINGMIX
PROPORTIONING
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 g y p
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
changed
Thank you for your attention !Thank you for your attention !Thank you for your attention !Thank you for your attention !
MIXMIXMIX
MIX
DESIGNMIX
MIX
DESIGN
MIXPROPORTIONING
MIXPROPORTIONING
Dr. ir. Robby CaspeeleIng. Peter Minne
Technologiepark-Zwijnaarde 9049052 Zwijnaarde
Gebroeders Desmetstraat 19000 [email protected]