Applications of statistical models in proportioning medium strength self-compacting concrete

Self-compacting concrete (SCC) is generally designed with a relatively higher content of finer, which includes cement, and dosage of superplasticizer than the conventional concrete. The design of the current SCC leads to high compressive strength, which is already used in special applications, where the high cost of materials can be tolerated. Using SCC, which eliminates the need for vibration, leads to increased speed of casting and thus reduces labour requirement, energy consumption, construction time, and cost of equipment. In order to obtain and gain maximum benefit from SCC it has to be used for wider applications. The cost of materials will be decreased by reducing the cement content and using a minimum amount of admixtures. This paper reviews statistical models obtained from a factorial design which was carried out to determine the influence of four key parameters on filling ability, passing ability, segregation and compressive strength. These parameters are important for the successful development of medium strength self-compacting concrete (MS-SCC). The parameters considered in the study were the contents of cement and pulverised fuel ash (PFA), water-to-powder ratio (W/P), and dosage of superplasticizer (SP). The responses of the derived statistical models are slump flow, fluidity loss, rheological parameters, Orimet time, V-funnel time, L-box, JRing combined to Orimet, JRing combined to cone, fresh segregation, and compressive strength at 7, 28 and 90 days. The models are valid for mixes made with 0.38 to 0.72 W/P ratio, 60 to 216 kg/m3 of cement content, 183 to 317 kg/m3 of PFA and 0 to 1% of SP, by mass of powder. The utility of such models to optimize concrete mixes to achieve good balance between filling ability, passing ability, segregation, compressive strength, and cost is discussed. Examples highlighting the usefulness of the models are presented using isoresponse surfaces to demonstrate single and coupled effects of mix parameters on slump flow, loss of fluidity, flow resistance, segregation, JRing combined to Orimet, and compressive strength at 7 and 28 days. Cost analysis is carried out to show trade-offs between cost of materials and specified consistency levels and compressive strength at 7 and 28 days that can be used to identify economic mixes. The paper establishes the usefulness of the mathematical models as a tool to facilitate the test protocol required to optimise medium strength SCC.

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 passing ability, the segregation resistance of fresh SCC, and the compressive strength. NF model is constructed by training and testing data using the experimental results obtained in this study. The results of NF models were compared with experimental results and were found to be quite accurate. The proposed NF models offers useful modeling approach of the fresh and hardened properties of SCC containing PFA.

Genetic programming based formulation for fresh and hardened properties of self-compacting concrete containing pulverised fuel ash

Self-compacting concrete (SCC) flows into place and around obstructions under its own weight to fill the formwork completely and self-compact without any segregation and blocking. Elimination of the need for compaction leads to better quality concrete and substantial improvement of working conditions. This investigation aimed to show possible applicability of genetic programming (GP) to model and formulate the fresh and hardened properties of self-compacting concrete (SCC) containing pulverised fuel ash (PFA) based on experimental data. Twenty-six mixes were made with 0.38 to 0.72 water-to-binder ratio (W/B), 183–317 kg/m³ of cement content, 29–261 kg/m³ of PFA, and 0 to 1% of superplasticizer, by mass of powder. Parameters of SCC mixes modelled by genetic programming were the slump flow, JRing combined to the Orimet, JRing combined to cone, and the compressive strength at 7, 28 and 90 days. GP is constructed of training and testing data using the experimental results obtained in this study. The results of genetic programming models are compared with experimental results and are found to be quite accurate. GP has showed a strong potential as a feasible tool for modelling the fresh properties and the compressive strength of SCC containing PFA and produced analytical prediction of these properties as a function as the mix ingredients. Results showed that the GP model thus developed is not only capable of accurately predicting the slump flow, JRing combined to the Orimet, JRing combined to cone, and the compressive strength used in the training process, but it can also effectively predict the above properties for new mixes designed within the practical range with the variation of mix ingredients.

Influence of the type of coarse lightweight aggregate on properties of Semi-Lightweight Self-Consolidating Concrete

This paper presents studies on the properties of fresh and hardened semilightweight self-consolidating concrete (SLWSCC) mixtures, produced with two types of manufactured coarse lightweight aggregates (LWA) and normal weight sand. The first type, a sintered pulverized fuel ash, was made from an industrial by-product, fly ash, whereas the second one, an expanded clay, was produced from a naturally sourced clay. For all mixtures, normal weight sand was used as a fine fraction of aggregates, and the portland cement was partially replaced with a limestone powder. The SLWSCC was produced with different water presaturation regimes of the LWAs. The desired initial slump-flow spread was set between 700 and 800 mm. The effect of three superplasticizers was evaluated by testing properties of SLWSCC, normal weight SCC, and paste mixtures. Three SCC fresh properties were measured: the slump-flow, the V-funnel flow time, and the J-ring blocking step. Moreover, the slump-flow loss was evaluated. The degree of segregation was assessed in both fresh and hardened states. Additionally, the hardened density and the compressive strengths were tested. All SLWSCC mixtures were produced with a desired range of slump-flow spread and with satisfactory passing ability assessed with the J-ring test. SLWSCCs prepared with the expanded clay LWA were less sensitive to the variation of water presaturation levels and showed lower viscosity than those made with the sintered pulverized fuel ash LWA. Only mixtures containing SP-3 superplasticizer showed acceptable workability loss resistance. The saturated surface-dry density of all of the mixtures varied in a range of 2,025–2,125??kg/m 3 . Mixtures containing 29% of coarse LWAs and 71% of sand (by mass) had 24-h and 28-day compressive strengths above 20 and 40 MPa, respectively, but the mixtures made with the expanded clay were slightly weaker.