Percentages Of Superplasticizer Research Articles

AI-driven critical parameter optimization of sustainable self-compacting geopolymer concrete

This study aims to optimize the critical parameters of self-compacting geopolymer concrete (SCGC) with a primary focus on enhancing compressive strength. Optimization of SCGC holds significant importance within the context of sustainable development. To implement the research, a comprehensive database was meticulously compiled by integrating data from published literature, with Fly ash and GGBFS serving as essential binders. Prior to the development of a robust machine learning model, the database underwent careful preprocessing to ensure data quality. In pursuit of optimal performance, three machine learning model's (base model) hyperparameters were diligently fine-tuned employing effective spotted hyena metaheuristic optimization technique. A hybrid model was developed using three base models and a meta-learner. The hybrid model outperformed other models owing to better accuracy and generalizability. Leveraging quasi-Monte Carlo sensitivity analysis, this study effectively identified temperature, Na2SiO3/NaOH, NaOH molarity, and percentage of superplasticizer as critical parameters with the highest impact on compressive strength. Optimization involved adjusting the temperature (25–90 °C), Na2SiO3/NaOH concentration (8–16 M), molarity (1.5–4), and superplasticizer amount (2–10%). The optimization of these crucial parameters was effectively executed using Response Surface Methodology (RSM). This study introduces a novel integrated approach within RSM, deviating from conventional quadratic or polynomial models for interaction characterization. The optimized parameters were consistent across cases involving fly ash, GGBFS, and a blend of both binders, except for temperature, which varied based on the binder type. The optimized mix parameters are used for experimental validation of results obtained through AI-driven analysis and observed to provide an expected result of compressive strength in the range of 39–43 MPa.

The Effect of Superplasticizer (SP) on the Workability and Skid Resistance of Geopolymer Concrete (GPC) Containing High Calcium Precursor (HCP)

A superplasticizer (SP) is one of the admixtures that may be used to improve the workability of concrete. However, its effects on geopolymer concrete (GPC) have yet to be well known or published, especially regarding its skid resistance and workability performance. This research aims to study the impact of the superplasticizer on the skid resistance and workability of geopolymer concrete containing high calcium precursor (HCP) in different proportions of SP, which are 0, 0.5, 1.0, 1.5 and 2.0 % by the weight of the HCP. Seven mix designs were produced: 1 conventional concrete mix, 5 geopolymers concrete mix, and 1 bituminous cold-mix asphalt mix. Slump flow and skid resistance tests were performed in this study. A skid resistance test is achieved using a British Pendulum Tester (BPT) on a 220 mm x 220 mm x 50 mm rectangle. This study found that the effect of SP on skid resistance is insignificant. However, the skid resistance of all GPC mix designs is higher than CC and asphalt mix. The workable flow of geopolymer concrete containing HCP and 0 - 2 % SP was 118-123 mm. However, less than 2 % of SP is given an insignificant difference. The optimum mix design of GPC is mix design GPC with an added 2 % SP (M6) that gives a high workability compared to other GPC mix designs and good skid resistance of 95 BPN. This study also found that workability correlates with skid resistance. The higher the spreading width value, the smaller the skid resistance value. In other words, the higher the flowability value, the less rough the sample surface. Hypothetically, the higher the percentage of SP, the higher the workability and the lower the skid resistance.

A comprehensive GEP and MEP analysis of a cement-based concrete containing metakaolin

Due to the detrimental environmental consequence of cement formation, studies has concentrated on decreasing the environmental influence and expense of cement containing products. It is crucial to conduct research on the mechanical characteristics of cement additive/replacement products such as metakaolin (MK). To serve the said purpose, the importance of developing predictive machine learning (ML) models is growing as ecological concerns intensify. Since there are few ML approaches for them, it is imperative to develop techniques to improve the mechanical properties of such products. In order to overcome these issues, this research examines ML methods for forecasting the compressive strength (fc') of concrete containing MK. Gene expression programming (GEP) and multigene expression programming (MEP), two distinct ML predictive models, are provided in detail along with the most useful variables, enabling effective analysis and forecasting of the fc' of concrete containing MK. An important component of any prediction or simulation endeavor is model generalization, and the researchers of this study used a data set of MK concrete mechanical properties for this purpose. The database has 405 data points with pertinent model parameters to calculate the fc' of concrete containing MK. Cement, percentage of metakaolin by binder, coarse and fine aggregate by binder, water by binder, coarse aggregates by fine aggregates, percentage of superplasticizer by binder, and age are among the factors in the database that influence concrete's fc' yet still previously infrequently regarded as crucial input attributes. The effectiveness of the models is then examined in order to choose and implement the best predicted model for the mechanical properties of MK-based concrete. According to the findings of this research, the optimal ML algorithms for forecasting MK-based concrete fc' is MEP with R2 value of 0.96. Additionally, the sensitivity analysis shows that water-by-binder, percentage of superplasticizer-by-binder and age have the significant effect on the development of compressive strength of metakaolin based concrete. GEP and MEP models develop empirical expression for the outcome to forecast future databases' features to promote the usage of sustainable MK concrete. Moreover, detrimental effects caused by intensive and costly labor work can be overcome by utilizing these environmental-friendly techniques.

A Study on Self-Consolidate Concrete in Experimental Reinforced-Concrete Beam Column Structures with Alccofine and Steel Fiber

This paper discusses the concept of self-consolidating concrete mix with Alccofine-1203, superplasticizer, viscosity modification agent, crimped steel fiber on the RC beam column joint. Totally, eight reinforced-concrete (RC) column joints with M25 grade concrete were considered in this study. Out of eight RC beam column joints, two beam column joints served as Alccofine, two beam column joint specimens served as Alccofine 5%, two beam column joint specimens served as Alccofine-10%, and two beam column joint specimens served as Alccofine-15%. All the RC beam column joint specimens were tested under the compression until failure in the loading frame of 2000 kN capacity. The test results of Alccofine-10% showed the higher loading capacity than that of Alccofine-5% and Alccofine-15% in self-consolidating concrete RC beam column joints. The percentage of superplasticizer, viscosity modification agent, and crimped steel fiber is maintained. Nonlinear finite element analysis (FEA) to analyse the beam column joint through nonlinear finite element modelling (NLFEM) and the modelling results were compared from the experimental results. The results obtained through ANSYS modelling show good agreement with the experimented results. The deflection ductility of experimental results shows 1.57, and the predicted deflection ductility shows 1.59 in beam column joint with Alccofine-10%. ANSYS software is validated as appropriate software to predict the study parameters of self-consolidation concrete in beam column joints.

Study of water intrusion in admixture concrete using neutron backscattering technique

This work aims to study the water infiltration in ordinary concrete with different percentages of super-plasticizer (SP) varied from 0.25% to 1.5% of cement weight. It was performed by measuring thermal neutrons backscattered from the investigated samples. Dynamic measurements were carried out by fixing the sample on a transfer table in front of the neutron beam exit. A slit collimated beam of neutrons emitted from 252Cf was hit on which surface of the sample water did not infiltrate through it and along the flow direction.Further, static measurements were made using collimated neutron beams of thermal and fast neutrons emitted from sources of 252Cf and Pu-Be with strength of 1.3 × 107 and 5 × 106 n/s respectively. These beams of neutron incident on the other side of sample which water allowed infiltrating for 7, 14, 21, 28, and 35 days. The backscattered thermal neutrons were measured by BF3 detector which fixed on the same side of the incident neutron beam. The obtained results show clearly the dependence of water infiltration on the percentages of SP and the submerged time. As well as, the measuring sensitivity depends on the main energy of the incident neutrons, submerged time, and SP% was given and discussed. The results indicated that samples with SP below 1.5% show an improvement in resistivity to water infiltration and therefore prolong the concrete durability for marine constructions. The best percentage of SP is 1%, for more percentages, there is a negative effect on the permeability and diffusivity. As well as, the obtained results indicated that, NBS is a powerful, nondestructive, and fast method to observe water infiltration in porous materials. In addition, these results lead to improve concrete properties to prevent fluid intrusion. Further investigation should be done with different recycled additives to achieving the optimization between waste reuse and engineering properties of porous concrete.

Influence of superplasticizer on workability and strength of ambient cured alkali activated mortar

Alkali activated materials contain aluminosilicates, which can be used for building with a low environmental impact by repurposing waste resulting in cost-effective construction. The impact of nature and superplasticizer dosage on fly ash slag-based ambient cured mortar activated with sodium silicate and sodium hydroxide is not yet definitive. This paper studies the influence of various superplasticizers on fly ash-based ambient cured mortar activated with sodium silicate and sodium hydroxide. The effect of superplasticizers was evaluated by adding various superplasticizers including melamine, polycarboxylate ether and naphthalene-based superplasticizers in different dosages from 0 to 3% at 0.5% intervals. Fresh properties of alkali-activated mortar with various percentages of superplasticizer were tested using flow table apparatus and compared with mortar without superplasticizer. The hardened property of the alkali-activated mortar was tested by checking the compressive strength of the mortar cube. From the test results, naphthalene and polycarboxylate ether-based superplasticizers are compliant with alkali-activated mortar. Melamine-based superplasticizer shows a slight reduction on strength of alkali-activated mortar. In the ambient cured mortar, the optimum concentration for naphthalene and polycarboxylate ether-based superplasticizers is 2.0 percent, whereas melamine-based superplasticizer exhibits maximum strength for 2.5 percent. Even though naphthalene and polycarboxylate-based superplasticizers are effective for alkali-activated mortar, the most efficient type is the polycarboxylate ether-based superplasticizer.

Mechanical Performance of Commercially Available Premix UHPC-Based 3D Printable Concrete.

Several recent studies have attempted to formulate printable cementitious materials to meet the printing requirements, but these materials are designed to work with specific printing equipment and printing configurations. This paper aims to systematically develop and perform characterization of a commercially available ultra-high-performance concrete-class material (UHPC) modified to be printable. Four percentages of superplasticizer were used (100%, 94%, 88%, 82%) to adjust the UHPC mixture for 3D-printing requirements. A superplasticizer amount of 88% was considered adequate to meet the requirements. Several fresh and hardened properties of UHPC were measured experimentally: shape-retention ability and green strength were investigated in fresh state, and compressive and flexural strength were evaluated in three loading directions to evaluate the anisotropic effects. Furthermore, the strength of the interlayer bond was investigated. The UHPC developed in this study met the criteria for extrudability, buildability, and shape retention to ensure printability. In comparison with mold-cast UHPC, printed UHPC exhibited superior flexural performance (15–18%), but reduced compressive strength (32–56%). Finally, the results demonstrated that a commercially available UHPC-class material can be used for 3DCP, which possesses all necessary properties, both fresh and hardened.

Revealing the nature of metakaolin-based concrete materials using artificial intelligence techniques

In this study, a model for the estimation of the compressive strength of concretes incorporating metakaolin is developed and parametrically evaluated, using soft computing techniques. Metakaolin is a component extensively employed in recent decades as a means to reduce the requirement for cement in concrete. For the proposed models, six parameters are accounted for as input data. These are the age at testing, the metakaolin percentage in relation to the total binder, the water-to-binder ratio, the percentage of superplasticizer, the binder to sand ratio and the coarse to fine aggregate ratio. For training and verification of the developed models a database of 867 experimental specimens has been compiled, following a broad survey of the relevant published literature. A robust evaluation process has been utilized for the selection of the optimum model, which manages to estimate the concrete compressive strength, accounting for metakaolin usage, with remarkable accuracy. Using the developed model, a number of diagrams is produced that reveal the highly non-linear influence of mix components to the resulting concrete compressive strength.

The effect of natural fiber (banana fiber) on the mechanical properties of self-compacting concrete

Self-compacting concrete is an innovative concrete technology with higher flowability properties by adding the mineral admixtures such as superplasticizers and not necessary to vibrate concrete. The banana fibers were used in this investigation because banana fiber is an environmentally friendly material with good properties compared to synthetic fiber. The experimental test consisted of two phases of research, i.e., preliminary research, which had the aim to investigate the appropriate percentage of superplasticizer in the mixed composition of self-compacting concrete. The percentages of superplasticizers were 0.7%, 1%, and 1.4%. The second phase of research on the effect of additional banana fiber on self-compacting concrete was carried out, examining workability, compressive strength, and the splitting tensile strength of concrete. In this research, the percentages of fiber were 0.12%, 0.3%, and 0.5% of the cement weight, and evaluated the effect of fiber treatment. The treated fibers mean that the fiber is immersed in NaOH solution or named as a delignification process. The hardened concrete specimen was used for determining the mechanical strengths, such as the compressive strength and split tensile strength tests. The test results found that the optimum value for superplasticizer dosage was 0.7% of cement weight, which can fulfill the whole criteria of fresh concrete and hardened SCC concrete. Furthermore, the specimen with the adding banana fiber of 0.12% by cement weight and treated fiber indicated a decrease in workability. Compared to the control concrete, it inversely increased compressive strength up to 44.36% and tensile strength up to 17.78%.

Effect of critical parameters on the fresh properties of Self Compacting geopolymer concrete

The aim of this paper is to study the effect of critical parameters with their acceptable wide range of variations in the production of a Ground Granulated Blast Furnace Slag (GGBFS) based Self-Compacting Geopolymer Concrete (SCGC) with acceptable passing and filling ability as per British Standard. In the absence of specific design code, various critical parameters with their ranges of variations are: molarity of alkaline solution (10 M, 12 M and 14 M), alkaline activated solution (AAS) ratio (2, 2.5 and 3), percentage of superplasticizer (4%, 6% and 8%) and extra water (18%, 21% and 24%). The raw materials involved in the manufacturing of SCGC i.e., fine aggregate and coarse aggregate were characterized as per existing Indian codes and supplier properties for GGBFS (except specific gravity), superplasticizer, sodium hydroxide and sodium silicate solution were used. The SCGC thus produced out of the design mix prepared, based on all the variables, were examined for the workability criteria. It is further perceived that the wide range of four critical parameters are narrowed down and specific values of the affecting parameters can only be obtained with the help of hardened property study.

Comparison of Machine Learning Approaches with Traditional Methods for Predicting the Compressive Strength of Rice Husk Ash Concrete

Efforts are being devoted to reducing the harmful effect of the construction industry around the globe, including the use of rice husk ash as a partial replacement of cement. However, no method is available to date to predict the compressive strength (CS) of rice husk ash blended concrete (RHAC). In this study, advanced machine learning techniques (artificial neural network, artificial neuro-fuzzy inference system) were used to predict the CS of RHAC. Based on the published literature, six inputs, i.e., age of specimen, percentage of rice husk ash, percentage of superplasticizer, aggregates, water, and amount of cement, were selected. Results obtained from machine learning methods were compared with traditional methods such as linear and non-linear regressions. It was observed that the performance of machine learning methods was superior to traditional methods for determining the CS of RHAC. This study will prove beneficial in minimizing the cost and time of executing laboratory experiments for designing the optimum content portions of RHAC.

Parametric Effects on Slump and Compressive Strength Properties of Geopolymer Concrete using Taguchi Method

This paper represents the parametric effects on slump and compressive strength of aluminosilicate based Geopolymer concrete using by Taguchi method. A total of nine mix proportions were considered to evaluate the effect of sodium hydroxide (NaOH) solution, Solution/Binder (SB) ratio and the percentage of superplasticizer. Results indicated that the highest slump of 165 mm and 28 days compressive strength of 68.37 MPa was obtained for aluminosilicate based Geopolymer concrete with the superplasticizer, Solution to Binder (SB) ratio and extra water) parameters. By using the selected (Signal-to-Noise (SN) ratio graphs, the best combination of parameters for slump and compressive strength properties was also obtained. The mix with the best combination of parameters was considered and partially replaced with silica fume and rice husk ash. The inclusion of additional silica (in form of silica fume and rice husk ash as Ground Granulated Blast Furnace Slag (GGBFS) replacement), most significantly influenced the slump and compressive strength properties.

Eco-friendly Hybrid Concrete Using Pozzolanic Binder and Glass Fibers

Hybrid Concrete focused on development of buildings, highways, and other structures of civil engineering. In the current study, various mix combinations have been prepared and tested with different percentages of super-plasticizer at different levels of water reduction for obtaining the optimum mix. Further, study on different properties of hybrid concrete and replacement of ordinary portland cement (OPC) with ground granulated blast furnace slag (GGBFS), silica fume (SF) and glass fibers (GF) for obtaining highly cement replaced concrete (HCRC) and glass fiber concrete (GFC). The concrete performance was evaluated based on slump cone test, compressive strength test, split tensile strength test, flexural strength test, water absorption test and ultrasonic pulse velocity test. It was observed from the results that, the best performance of HCRC achieved at 50% GGBFS and 3% silica fume replacement. Further, in the case of GFC, 0.2% of glass fibers showed high performance in terms of split tensile and flexural strength at all ages. The optimized concrete mixtures like HCRC and GFC performed better than the control concrete (CC).

Optimizing fresh properties and compressive strength of self-consolidating concrete

Compared to conventional concrete self-consolidating concrete (SCC) has a higher cost due to the large content of cement, use of mineral fillers, and use of various chemical admixtures, resulting in relatively high material cost. The proper selection of material and mixture proportioning can enable reduction in cement and admixture contents, leading to savings in cost. This study investigates the potential of optimizing fresh properties and 28-day compressive strength of SCC incorporating fly ash (FA), silica fume (SF), and limestone powder (LP) as part of cement by mass in total powder content. The input parameters of mixtures were total powder content (P), percentage of FA, percentage of SF, percentage of LP, water to powder ratio (W/P), and percentage of superplasticizer (SP) whereas slump flow, L-box (H2/H1), segregation index, and 28-day compressive strength were the desirable responses. A total of ninety concrete mixes were designed using central composite design (CCD) concept and checked experimentally using artificial neural network (ANN) in MATLAB. Analysis of variance test (ANOVA) used to establish the mathematical models. Results indicated that fresh characteristics of SCC enhance with the increase in FA percentage while the 28-day strength decreases. Slump flow diameter and L-box (H2/H1) ratio decrease with the increase in SF percentage. Nevertheless, the increase in silica fume improves the resistance of SCC to segregation and increases the 28-day strength. Using limestone powder as a replacement of cement content resulting in negative impact on L-box (H2/H1) ratio and 28-day compressive strength while the resistance to segregation enhances. High relations between the responses and the constituent materials of SCC can be developed using response surface methodology (RSM) and the optimum values of variables can be estimated to achieve the desirable properties of responses.

Mechanical characterization of concretes produced with construction and demolition waste

The construction industry is responsible for the generation of large volumes of waste, known as construction and demolition waste (CDW). Around the world, millions of tons of these wastes are generated annually, which often become important environmental liabilities. The situation gets worse as the sector develops. In Europe, only 15 of the 27 countries in the European Union annually produce around 180 million tonnes of CDW, in Brazil, the data about this indicates that in 2014 the municipalities collected about 45 million tons of CDW, quantity 4.1% higher than in 2013. In this scenario, the present study aims to evaluate the partial replacement of natural aggregates by CDW (aggregates of concrete waste) in the production of concretes. In this sense, the effects of this substitution on the workability and mechanical characteristics of the concretes produced, as well as the influence of the mixing method and the percentage of superplasticizer additive on the same characteristics were evaluated. The methodology basically consists in the production of seven different traits, which are references and different combinations of mixing method, aggregate substitutions and superplasticizer percentages. Specimens of these traces were molded for mechanical characterization. Workability parameters were also evaluated. It is concluded that the workability is strongly affected by the addition of CDW, but a good workability can be obtained with the use of superplasticizer additives. The mixing method did not change the results obtained for this property. The results also indicate that the compressive and tensile strengths are not negatively affected by the substitution of aggregates, as well as being not significantly affected by the presence of the percentages of superplasticizer used, nor by the mixing methods.

Optimal proportioning of grout constituents using mathematical programming for semi flexible pavement

This study proposes a mathematical approach to obtain the optimal proportion of the grout constituents for a semi-flexible pavement. Grout mixes were prepared using three different water-cement ratio, three sand sizes, one sand-cement ratio, four/five different percentages of super plasticizer, and two different types of super plasticizer. The grout mixes were tested for flowability and strength using Marsh cone test and cube compressive strength test, respectively. The laboratory results were utilized to validate the proposed mathematical programming approach while obtaining the optimal proportion of grout constituents. Experimental results indicated that a minimum of 2% naphthalene super plasticizer and 1% polycarboxylate super plasticizer are required, irrespective of any combination, to achieve the desired flow time of the grout. The flow time and compressive strength were found to be associated with type of super plasticizer used. The proposed approach was found to be statistically satisfactory, with 95% confidence level, for determining the optimum grout constituents. The validation of the grout penetrability, using the proposed approach, has also been provided. Researchers and field engineers may consider to employ this analytical approach for grout constituent proportioning by partial or total replacement of cement with silica fume or fly ash.

Influences of Parameters on Slump Flow and Compressive Strength Properties of Aluminosilicate Based Flowable Geopolymer Concrete Using Taguchi Method

In this study, the influences of factors and their levels on flowable geopolymer concrete had been examined using Taguchi method. The factors like molarity of sodium silicate (Na2SiO3) solution, percentage of coarse aggregate (CA %), Solution to binder (S/B) ratio and percentage of superplasticizer and each factor varies with three levels as 1.5 M, 1.75 M, 2 M; 40%, 50%, 60%; 0.5, 0.55, 0.6; 0%, 1%, 2% was considered. A total of 9 mixes has been considered as per Taguchi method (L9). It can be observed from the test results that, among the nine Taguchi mixes, the mix T4 is containing 1.75 M of Na2SiO3 solution; 60% of coarse aggregate; solution to binder ratio of 0.6; superplasticizer of 2% had achieved highest slump flow of 650 mm; compressive strength of 29.8 MPa at 28 days and other side the mix T5 is containing 1.75 M of Na2SiO3 solution; 50% of coarse aggregate; solution to binder ratio of 0.5; superplasticizer of 0% had achieved slump flow of 340 mm; compressive strength 47.9 MPa at 28 days. The molarity of the solution is same in the both mixes as 1.75 M. It can be seen that; the mixes are highly influenced by the solution to binder ratio and superplasticizer. Increase in solution to binder ratio and superplasticizer results in increase in slump flow and decrease in strength properties. Therefore, Taguchi analysis is carried out to understand the influences of factors and their levels using Minitab17 software. The optimum level of each factor had been considered in terms of flow and strength properties. Hence, the optimum mix T10 proposed by Taguchi is 1.75 M of sodium silicate solution, 50% of coarse aggregate, S/B ratio of 0.55 and superplasticizer dosage of 1% had achieved 575 mm flow and compressive strength of 52.81 MPa. However, the mix T10 had achieved 11.38% less flow as compared to mix T4 and increase in compressive strength of 10% as compared to T5. Therefore, the comparative study had been carried out between T4, T5 and T10 to determine the optimum mix.

Rheological and Mechanical Response Modifications for a Self-Leveling Mortar

In many cases cement based materials demand a higher flowability and workability and this conventionally can't be done without loss of its strength, due to the fact that the common practice to increase the workability is the addition of water. But, nowadays using a third generation superplasticizer (SP) we can achieve the desire flowability without loss of its strength. The action of superplastisizers is to spread efficiently the cement grains and so to wetting better the cement grains giving a more homogeneous mixture with higher strength. Nine different mixtures were prepared adding a small percentage of SP (1%). The conditions to get a self levelling mortar, have to do not only with rheological but also with mechanical demands. The bending and compression test gave the achieving mechanical strength whereas their rheological response came through slump flow and v-funnel flow tests. With the help of a small amount of stabilizer we obtain a robust mixture that deserves the desire response at the field too.

Prediction of Fresh and Hardened Properties of Self-Consolidating Concrete Using Neurofuzzy Approach

Self-consolidating concrete (SCC) developed in Japan in the late 80s has enabled the construction industry to reduce demand on the resources, improve the work conditions and also reduce the impact on the environment by elimination of the need for compaction. This investigation aimed at exploring the potential use of the neurofuzzy (NF) approach to model the fresh and hardened properties of SCC containing pulverised fuel ash (PFA) as based on experimental data investigated in this paper. Twenty six mixes were made with water-to-binder ratio ranging from 0.38 to 0.72, cement content ranging from 183 to 317 kg/ m3 , dosage of PFA ranging from 29 to 261 kg/ m3 , and percentage of superplasticizer, by mass of powder, ranging from 0 to 1%. Nine properties of SCC mixes modeled by NF were the slump flow, JRing combined to the Orimet, JRing combined to cone, V-funnel, L-box blocking ratio, segregation ratio, and the compressive strength at 7, 28, and 90 days. These properties characterized the filling ability, the...

Superplasticizer effects on setting and structuration mechanisms of ultrahigh-performance concrete

The effect of superplasticizer addition on chemical and mechanical kinetics of ultrahigh-performance concrete realization is studied by ultrasonic spectroscopy, calorimetry, and autogenous shrinkage determination. Different percentages of superplasticizer have been used. We have firstly been able to identify the role of the superplasticizer on the early mechanisms of settlement of the solid particles in the mix, and the influence of entrapped air. The delay in the chemical activity brought by the superplasticizer has been analyzed according to the results of the three experimental methods. The continuous information upon the state of organization of the concrete and the structuration of the material is given by ultrasonics for any stage of the chemical reaction. A desynchronization of the shrinkage intense period versus the peak of released power and the material structuration is analyzed in terms of fissuration healing capability and material damaging.