SCIENTIFIC REPORTS. Materials and Structures/Matériaux et Constructions, Vol. 33, June 2000, pp

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1 Materials and Structures/Matériaux et Constructions, Vol. 33, June 2000, pp Application of factorial models to predict the effect of antiwashout admixture, superplasticizer and cement on slump, flow time and washout resistance of underwater concrete M. Sonebi, A. K. Tamimi and P. J. M. Bartos ACM Advanced Concrete and Masonry Centre, Department of Civil, Structural and Environmental Engineering, University of Paisley, Scotland, United Kingdom SCIENTIFIC REPORTS Paper received: June 14, 1999; Paper accepted: November 2, 1999 A B S T R A C T The anti-washout underwater concrete posses different properties from conventional underwater concrete. When an anti-washout admixture is mixed with concrete, the viscosity of the concrete is increased and its resistance to washout is enhanced. Superplasticizer ensures enough concrete fluidity to spread readily in place without vibration. A factorial design was carried out to mathematically model the influence of three key parameters on slump, flow time measured by Orimet and mass loss by washout of underwater concrete. The parameters considered in this investigation were the dosage of cement, the concentrations of anti-washout admixture and superplasticizer. All mixes were made with 0.43 water/cement ratio which correspond a typical underwater concrete. The proposed models are valid for concretes with cement dosage ranging between 420 kg/m 3 to 520 kg/m 3 and the concentrations of anti-washout admixture and superplasticizer varying between 0.02% % and 0.4% - 1.8%, by mass of cement, respectively. Test results of the models indicated that the slump is highly affected, in order of importance, by dosage of cement and the dosage of superplasticizer, then by the concentration of anti-washout admixture. The mass loss by washout is influenced, in order of significance, by the concentration of anti-washout admixture, the dosage of cement, the concentration of superplasticizer and the interaction between the dosages of cement and antiwashout admixture. The flow time is affected, in order of importance, by the dosage of cement, the concentration of superplasticizer and the interaction between these parameters. This model can be used as a tool to facilitate the test protocol required to optimise the underwater concrete. R É S U M É Le concept du béton sous l eau contenant un agent colloïdal anti-lessivage est différent de celui du béton conventionnel. En ajoutant un agent colloïdal, la viscosité du béton est augmentée et la résistance au lessivage est améliorée. L utilisation du superplastifiant assure une bonne fluidité permettant ainsi une mise en place du béton sous l eau sans vibration. Un plan factoriel a été établi pour étudier mathématiquement l effet de trois paramètres clés sur l affaissement, le temps d écoulement mesuré par l appareil Orimet et la perte de masse par lessivage. Les paramètres étudiés sont le dosage en ciment, les concentrations en agent colloïdal et en superplastifiant. Tous les mélanges sont fabriqués avec un rapport eau/ciment de 0,43 qui correspond à un béton typique coulé sous l eau. Les modèles proposés sont valides pour des bétons ayant des dosages en ciment situés entre 420 et 520 kg/m 3 et en agent colloïdal et en superplastifiant variant respectivement de 0,02% à 0,13% et 0,4% à 1,8% de la masse de ciment. Les résultats des modèles montrent que l affaissement est sensiblement affecté, dans l ordre, par le dosage en ciment, le dosage en superplastifiant et enfin celui en agent colloïdal. Alors que le lessivage est influencé, par l ordre d importance, par les dosages de l agent colloïdal, du ciment et du superplastifiant ainsi que par l interaction entre les dosages en ciment et agent colloïdal. Le temps d écoulement est affecté par les dosages de ciment et du superplastifiant ainsi que par leur interaction. Ces modèles peuvent être utilisés en tant que moyens d aide à l optimisation du béton sous l eau. Editorial Note Dr. P. J. M. Bartos is a RILEM Senior Member. He is the Chairman of RILEM TC 145-WSM: Workability of special concrete mixes. He participates also in the work of RILEM TC 162-TDF: Test and design methods for steel fibre reinforced concrete and that of TC 174-SCC: Self-compacting concrete /00 RILEM 317

2 Materials and Structures/Matériaux et Constructions, Vol. 33, June INTRODUCTION The construction of a wide range of structures including bridge piers, harbours, sea and river defences over many decades, and more recently the development of offshore oils fields, has required placement of concrete underwater. This process can be successfully carried out and sound, good quality concrete will be produced if sufficient attention is paid to the concrete mix design and the production method applied. The required stability of fresh concrete depends on the rheological properties and placement conditions. It can be characterised by the concrete resistance to washout, segregation and bleeding and is affected by the mix proportioning, aggregate shape and gradation, admixture, vibration and placement conditions. The differential velocity at the interface between the freshly cast concrete and surrounding water can erode some cement and others fines. Such erosion can increase the turbidity and contamination of the water surrounding and impair strength, bond to reinforcement steel and existing surfaces, as well as durability. The improvement of the in-situ properties of underwater concrete have related to the enhancement in washout resistance [1-3]. The superplasticizer (SP) is used to ensure high fluidity and reduce the water/powder ratio (W/P). The anti-washout admixture (AWA) is incorporated to enhance the yield value and viscosity of the mix, hence the washout resistance and segregation [1, 4]. Majority of AWAs are water-soluble polymers that increase the yield value and viscosity of cement paste and concrete [4, 5]. A statistical design approach was used to establish statistical models and could provide an efficient means to evaluate the influence of key mix variables on fresh and hardened concrete characteristics that affect the performance of underwater concrete [6, 7]. The derived models include mixes with 380 to 600 kg/m 3 of powder, 0.34 to 0.46 W/P, 0.42 to 0.50 for the sand/aggregate ratio, as well as AWA and SP dosages varying between 0.005% Table 1 - Coded units of variables considered in the experimental design AWA SP C Mix Coded Absolute Coded Absolute Coded Absolute Model Centre points for experimental error Table 2 - Chemical and physical properties of cement Composition % Vicat set time SiO Initial (min) 119 Al 2 O Final (min) 164 Fe 2 O CaO 63.7 MgO 2.6 Na 2 O eq Free CaO 1.6 LOI % and 0.05% %, respectively. The slump flow and the washout resistance are influenced, in order of importance, by the concentrations of AWA and cement, then by the water/cement and dosage of SP and various coupled of these parameters. The sand/aggregate ratio had a secondary effect on these properties [6, 7]. The objective of this paper is to develop and evaluate a factorial experimental design to optimise the parameters that have significant effect on slump, flow time measured by Orimet test [8] and washout resistance of underwater concrete. Such models can illustrate the relative significance of primary mix parameters and the two-way interaction of such parameters on concrete properties required to ensure successful underwater placement. This paper illustrates the trade-off between the mix parameters that affects underwater concrete performance, including the influence of changing the cement content and antiwashout admixture dosage on slump. 2. FACTORIAL DESIGN APPROACH The target for this investigation is to study the effect of anti-washout admixture, superplasticizer and dosage of cement on the fresh properties, such as slump, washout resistance and flow time. Three key variables that could influence the properties of underwater concrete were identified to derive mathematical models for slump, washout resistance and flow time. These variables included the dosage of AWA and SP and the cement content. An empirical statistical model would be a useful design tool provided the levels for each measured response are orthogonal and obtained over a reasonable working range. A 2 3 statistical experimental design was used to measure the influence of two different levels of each variable on fresh concrete slump, washout resistance and flow time. For the development of models for workability, flow time and washout resistance, initial levels of the three selected mix variables were carefully chosen. Coded units of variables 1 and +1 considered in the factorial design corresponded to 0.02 and 0.13% for the AWA, 0.4 and 1.8% for the SP dosage and 420 and 520 kg/m 3 for cement.

3 Sonebi, Tamimi, Bartos The experimental design was expanded to include five replicate centre points to estimate the degree of experimental error. The central points consisted of mixes with three variables set at coded values of zero corresponding to 470 kg/m 3, 0.075% and 1.1% for cement content, dosages of AWA and SP, respectively. The 13 mix combinations, expressed in coded values, considered in the experimental design are shown in Table 1. The coded units of variable were calculated as follows: Coded AWA = (absolute AWA 0.075) / Coded SP = (absolute SP 1.1) / 0.7 Coded C = (absolute C 470) / EXPERIMENTAL PROGRAMME 3.1 Materials The concrete mixes investigated in this study were prepared using a Portland cement. The cement used conformed to BS 12:1996. The chemical and physical properties of cement are presented in Table 2. A coarse aggregate consisting of round natural quartz and sandstone particles with a nominal aggregate size of 20 mm was used. A well-graded quartzite sand with a finesse modulus of 2.74 was employed. The relative density values of the coarse aggregate and sand were 2.50 and 2.56, respectively, and their absorption rates were 1.7 and 1%, respectively. A new generation of copolymer-based SP was used which have solid content and specific gravity of 30% and 1.11, respectively. The SP was used at dosages varying from 0.4 to 2%, by mass of cement. Welan gum was selected for the AWA. Welan gum is a high molecular-weight, water-soluble polysaccharide obtained through a controlled microbial fermentation [9]. Welan gum is used to increase the viscosity of mixing water, and hence that of the cement paste. The powder-based welan gum was mixed with part of the mixing water, 10% solution, using a high-shear mixer. This was done to prevent the AWA from continuing its hydration during mixing and agitation. 3.2 Mixing and test methods All mixes were prepared in 25 litre batches and mixed in drum mixer. The mixing sequence consisted of homogenising the sand and coarse aggregate for 30 s, then adding 50% of the mixing water in 15 s. After mixing for a 2-3 min, the mixer is stopped for 5 min while the contents being covered. The cement is then added along with the remaining solution of water and SP. The AWA is added last. The concrete is then mixed for further 3 min. The workability of concrete was evaluated using the slump test. Because of the viscous nature of concrete containing an AWA, the readings of the measurement was delayed for one minute following the removal of the slump cone. The test consists of determining the mass loss of fresh concrete sample weighing 2.0 ± 0.2 kg which is placed in a perforated basket and allowed to freely fall three times through a 1.7 m-high column of water (CRD C 61-89A) [10]. The Orimet test was used for determination of the flow time of a fresh concrete mix [8, 11]. The Orimet consists of a vertical pipe fitted with an interchangeable conical orifice at its lower end. A quick-release trap door is used to close the orifice. The Orimet is provided with an orifice which reduces the internal diameter from 120 mm within casting pipe to 90 mm at the end of the orifice. A sample of at least 7.5 l of fresh mix is used. The flow time is measured when the trap door is opened until the flow of the concrete from the orifice is finished. Three measurements of the time flow were determined. 4. TESTS AND RESULTS 4.1 Modelling slump, flow time and washout resistance The mix proportions and test results of 13 mixes used in the factorial design are given in Table 3. These results were used to develop prediction models of slump, flow time and washout resistance. Table 3 - Mix proportioning and test results of mixes considered in experimental design M ix A B C D E F W/C SP (%) AWA (%) Cement (kg/m 3 ) Water (kg/m 3 ) Sand (kg/m 3 ) Coarse aggregate (kg/m 3 ) Slump (mm) Washout after 3 drops (%) Flow time (s) > 40 > >

4 Materials and Structures/Matériaux et Constructions, Vol. 33, June 2000 Table 4 - Repeatability of test parameters Slump Mass loss by washout Flow time Mean (N = 5) 178 mm 3.2% 9.4 s Coefficient of variation 12.1% 6.8% 3.1% Estimate error (95% confidence limit) 18 mm 0.18% 0.42 s Relative error 10% 5.6% 2.5% Table 5 - Parameter estimates of three statistical models Slump R 2 = 0.94 Mass loss by washout Flow time R 2 = 0.95 R 2 = 0.99 Parameter Estimate Prob.> t Estimate Prob.> t Estimate Prob.> t Intercept AWA NA NA SP C AWA.C NA NA NA NA SP.C NA NA NA NA Table 6 - Statistical models and relative influence on fresh properties Slump (mm) Mass loss by washout (%) Flow time (sec) C -2.7 AWA C 55.6 SP 0.76 C -7.2 SP AWA 0.34 SP 6.3 C. SP AWA. C The mixes 9, 10, 11, 12 and 13 are replicates at the centre points of experimental matrix made to verify the experimental error of the statistical models. Table 4 shows the mean measured responses of the five replicate mixes, coefficients of variations, as well as the standard errors with 95% confidence limit for each of the three measured properties. The relative experimental errors for flow time and mass loss by washout are shown to be limited to approximately 5.6%. On the other hand, the relative error for slump response was 10% indicating the greater degree of the experimental error for the workability model. The derived statistical models for slump, flow time and mass loss by washout along with correlation coefficients and Prob. > t values are shown in Table 5. The estimates for each parameter refer to the coefficients of the model found by least squares. The Prob. > t is the probability of getting an even greater t statistic, in absolute value, that tests whether the true parameter is zero. Probabilities less than 0.05 are often considered as a significant evidence that the parameter is not zero, i.e. that the contribution of the proposed parameter has a highly significant influence on the measured response. 320 The presentation in Table 6 enables the comparison of various parameters as well as the interactions of the three measured responses. Except for few interactions between some variables, the probabilities that the derived coefficients of the various parameters influencing each response are limited to 5%. This signifies that there is less than 5% chance, or 95% confidence limit, that the contribution of given parameter to the tested response exceeds the value of the specified coefficient. The correlation coefficients of the proposed models for slump, mass loss by washout and flow time are 0.94, 0.99 and 0.95, respectively. The high correlation coefficient of the washout response demonstrates excellent correlation where it can be considered that at least 95% of the measured values can be accounted for this the proposed models. Contributions of the various parameters to the three measured responses are listed in order of significance in Table 6. A negative estimate signifies that an increase of the given parameter results in a reduction of the measured response. For each model, the effect of the primary parameter with the highest estimates vs. the contributions of other main parameters are given in Table 6. The increase in the dosage of cement within the range of the model can then be interpreted to have 1.6 times greater influence on enhancing the slump than the increase in AWA concentration, given the SP dosage is held constant. Similarly, the increase in AWA can be said to reduce mass loss by washout by 3.5 times more than the reduction in SP dosage. In comparing the relative effectiveness of each parameter, it is essential to consider the effect of independent variables. For example, the trade off between the effect of AWA and SP can be evaluated for mixes containing a fixed dosage of cement since both variables are expressed in term of cement dosage. For any given response, the presence of parameters with coupled terms (SP.SP or AWA.AWA) indicates that the influence of the model is quadratic. Table 6 shows that the slump is influenced, in the order of significance, by the dosage of cement, the dosage of SP and the concentration of AWA. The mass loss by washout is affected, in order of importance, by the concentration of AWA, dosage of cement, SP and the interaction between the concentration of AWA and dosage of cement. The flow time is influenced only by the parameters, in order of importance, by the dosages of cement and SP and the interaction between cement and SP. 4.2 Effect of AWA and SP dosages on slump and mass loss by washout The following mathematical models for slump, mass loss by washout and flow time have been derived after series of experimental results: Slump model (R 2 = 0.94) Slump = C SP 35.6 AWA Mass loss by washout model (R 2 = 0.99)

5 Sonebi, Tamimi, Bartos Fig. 1 Contour diagrams of slump and mass loss by washout (C = 420 kg/m 3 ). Mass loss = AWA C SP 1.61 AWA.C Flow time model (R 2 = 0.95) Flow time = C 7.2 SP C.SP The derived models can be applied to generate contour diagrams showing the influence of various mix parameters on key properties affecting the quality of underwater concrete. The influence of the dosages of AWA and SP on slump and mass loss by washout for concrete made with fixed cement contents of 420 kg/m 3 and 520 kg/m 3 are plotted in Fig 1 and 2. All mixes had W/C ratio of It is important to note that the AWA dosage is expressed in terms of the mass of cement, as it is often specified in practice. The AWA dosages are constant in terms of both the masses of cement and water. As Fig. 2 Contour diagrams of slump and mass loss by washout (C = 520 kg/m 3 ). expected, the contour diagrams in Figs. 1 and 2 indicate that the increase of SP dosage (for a given value of AWA and cement) increases the slump along with the mass loss by washout. For a given dosage of SP, the increase in AWA dosage reduces the slump and mass loss by washout. For example, as can be shown in Fig. 2, concrete mix with 520 kg/m 3 cement and 1.1% of SP can exhibit a drop in mass loss by washout from 14 to 2% when the AWA dosage increases from 0.01 to 0.13%. Such reduction can be 7 to 4% for mixes made with 420 kg/m 3 incorporating similar SP and AWA dosages. For a constant dosage of cement, the contours diagrams presented in Figs. 1 and 2 suggest the needed AWA dosage required to maintain a given level of slump when the SP dosage is increased. This can be used to select an optimal combination of admixtures to maximum slump and minimum mass loss by washout. Fig. 3 Contour diagrams of slump with dosage of a AWA = 0.075% and 0.13%. 4.3 Effect of cement and SP dosages on slump and mass loss by washout Slump contour diagrams showing the trade-off between SP and cement for mixes containing 0.075% and 0.13% of AWA are plotted in Fig. 3. In case of mix containing 0.075% of AWA, the achievement of a slump of 210 mm 321

6 Materials and Structures/Matériaux et Constructions, Vol. 33, June 2000 can be expected with mixes made with 1.8% of SP and 492 kg/m 3 of cement. This can be obtained with a mix containing 1.37% SP and 520 kg/m 3 of cement. The increase in concentration from 0.075% to 0.13% of AWA can provide less workability for a given cement content and increase in SP necessary to maintain a required slump value. For example, for a fixed cement content of 500 kg/m 3 and 1.35% of SP, a concrete made with 0.075% and 0.13% of AWA can develop slump values of 185 mm and 140 mm, respectively. With a 0.075% of AWA concentration, the same SP is required to reduce the cement dosage from 520 to 450 kg/m 3 compared to mix made with 0.13% of AWA. For example, for mix prepared with 0.075% of AWA, the concentration of SP can be expected to increase from 1.07 to 1.24% when reducing the cement dosage from 520 to 450 kg/m 3 to maintain an approximate slump of 185 mm. On the other hand, for mix made with 0.13% of AWA, the SP content can increase from 1.5 to 2.5% to maintain a slump of 185 mm as the cement content drops from 520 to 450 kg/m Effect of cement and SP on flow time The influence of cement content and the concentration of SP on flow time is plotted in Fig. 4. The effect of AWA dosage is not significant on flow time. The range of AWA dosage would not be appropriate to show the effect of AWA. The increase of SP dosage or cement content showed a reduction in the flow time. For example, for a concrete made with 470 kg/m 3 of cement can reduce the flow time from 18 to 10 s when the SP concentration increases from 1.1 to 1.9%. When SP increases more than 2.5%, it would increase of the flow time. This fact could be attributed to the risk of blockage in the Orimet by separation of coarse aggregate from the paste. 4.5 Verification of accuracy of the models Six mixes were prepared to measure various responses to verify the accuracy of the proposed models of slump, mass loss by washout and flow time. The mix proportioning and properties of 6 mixes are summarises Fig. 5 Predicted vs. measured slump and measured mass loss by washout. 322 Fig. 4 Contour diagrams of flow time. in Table 3 (mix A to mix F). The predicted versus measured values of slump and mass loss by washout are plotted in Fig. 5. The errors corresponding to 95% limits of slump (± 18 mm) and mass loss by washout (± 0.2%) are indicated on Fig. 5. The majority of the measured slump and mass loss by washout lies close to the predicted values. On average, the ratios of predicted versus measured slump, mass loss by washout and flow time were 1.02, 1.07 and 1.01, respectively and the coefficient of variation of these ratios were 12.7, 13.2 and 18.7, respectively. The slump, the mass loss by washout and flow time models provides good prediction for actual values. 5. CONCLUSION Based on the above results based on the factorial models, the following conclusions have been reached: 1. The proposed models can reduce the extent of trial batches needed to achieve optimum balance among key mix variables which affect fresh underwater concrete properties such as slump, washout resistance and flow time. These variables affect directly the performance of underwater concrete. 2. The slump is influenced, in the order of significance, by the dosage of SP, the dosage of cement and the concentration of AWA. 3. The mass loss is affected, in order of importance, by the concentration of AWA, dosage of cement, SP and the interaction between the concentration of AWA and dosage of cement. 4. The flow time is influenced, in order of importance, by the dosages of cement and SP and the interaction between cement and SP.

7 Sonebi, Tamimi, Bartos 5. The increase in SP dosage for a given AWA, cement and water/cement ratio increases the slump and mass loss by washout. However, for a fixed dosage of SP, the increase in AWA dosage reduces the slump and mass loss by washout. 6. The proposed models of slump, mass loss by washout and flow time offer a good prediction of measured values. However, the mass loss by washout model appear to be less accurate to predicting response and have high scattering between predicted and measured values. REFERENCES [1] Sonebi, M. Development of high-performance, self-compacting concrete for underwater repair applications, Ph.D. Thesis, Université de Sherbrooke, Canada (Sep. 1997) 420 p. [2] Yamaguchi, M., Tsuchida, T. and Toyoizumi, H. Development of high-viscosity underwater concrete for marine structures, Marine Concrete, International Conference on Concrete in the marine Environment, Concrete Society (Sep. 1986) [3] Khayat, K. H., Gerwick, B. C. and Hester, W. T., Self-levelling and stiff consolidated concretes for casting high-performance flat slabs in water, ACI Concrete International: design and construction 15 (8) (1993) [4] Khayat, K. H., Effects of anti-washout admixtures on fresh concrete properties, ACI Materials Journal 92 (2) (March 1995) [5] Ghio, V. A., Monteirio, P. J. M. and Gjó/ rv, O. E. Effect of polysaccharide gums on fresh concrete properties, Ibid. 91 (6) (Nov. 1994) [6] Khayat, K. H., Sonebi, M., Yahia, A. and Skaggs, C. B., Statistical models to predict flowability, washout resistance and strength of underwater concrete, in Production Methods and Workability of Concrete, Glasgow, June 1996 (E & FN Spon, London, 1996) [7] Khayat, K. H., Yahia, A. and Sonebi, M., Applications of statistical models for proportioning underwater concrete, Fourth International Conference on Recent Advances in Concrete Technology, supplementary Papers, Japan, (June 1998) [8] Bartos, P. M. J, Fresh Concrete, Properties and Tests, Elsever Edition (1992) [9] Ghio, V. A., Monteirio, P. J. M. and Demsetz, L. A., The rheology of fresh cement paste containing polysaccharide gums, Cement & Concrete Research 24 (2) (1994) [10] CRD C61-89A, Test method for determining the resistance of freshly-mixed concrete to washing out in water, US Army Experiment Station, Handbook for Concrete, Vicksburg, Mississippi, Dec. 1989, 3 p. [11] Sonebi, M., Bartos, P. M. J. and Khayat, K. H., Assessment of washout resistance of underwater concrete: a comparison between CRD C61 and new MC-1 tests, Mater. Struct. 32 (May 1999)