Fast-track construction with slag cement concrete: Adiabatic strength development and strength prediction

The early-age strength development of concrete containing slag cement has been investigated to give guidance for its use in fast-track construction. Measurements of temperature rise under adiabatic conditions have shown that high levels of slag cement-for example, 70% of the total binder-are required to obtain a significant reduction in the peak temperature rise. Despite these temperature rises being lower than those for portland cement mixtures, however the early-age strength under adiabatic conditions of slag cement concrete can be as high as 250% of the strength of companion cubes cured at 20 degrees C (68 degrees F). The maturity and, hence, strength development were calculated from the adiabatic temperature histories based on several Maturity functions available in the literature. The predicted strength development with age was compared with the experimental results. Maturity functions that take into account the lower ultimate strengths obtained at elevated curing temperatures were found to be better at predicting the strength development.

Factorial Design of Cement Slurries Containing Limestone Powder for Self-Consolidating Slurry-Infiltrated Fiber Concrete

Slurries with high penetrability for production of Self-consolidating Slurry Infiltrated Fiber Concrete (SIFCON) were investigated in this study. Factorial experimental design was adopted in this investigation to assess the combined effects of five independent variables on mini-slump test, plate cohesion meter, induced bleeding test, J-fiber penetration test and compressive strength at 7 and 28 days. The independent variables investigated were the proportions of limestone powder (LSP) and sand, the dosages of superplasticiser (SP) and viscosity agent (VA), and water-to-binder ratio (w/b). A two-level fractional factorial statistical method was used to model the influence of key parameters on properties affecting the behaviour of fresh cement slurry and compressive strength. The models are valid for mixes with 10 to 50% LSP as replacement of cement, 0.02 to 0.06% VA by mass of cement, 0.6 to 1.2% SP and 50 to 150% sand (% mass of binder) and 0.42 to 0.48 w/b. The influences of LSP, SP, VA, sand and W/B were characterised and analysed using polynomial regression which identifies the primary factors and their interactions on the measured properties. Mathematical polynomials were developed for mini-slump, plate cohesion meter, J-fiber penetration test, induced bleeding and compressive strength as functions of LSP, SP, VA, sand and w/b. The estimated results of mini-slump, induced bleeding test and compressive strength from the derived models are compared with results obtained from previously proposed models that were developed for cement paste. The proposed response models of the self-consolidating SIFCON offer useful information regarding the mix optimization to secure a highly penetration of slurry with low compressive strength

A geotechnical description of fresh cement grout - filtration and consolidation behaviour

The removal of water from three Portland cement grouts by pressure filtration is examined, and the consolidation behaviour of the filtered material clarified. The filtration takes place by the laying down of a very stiff filter cake through the removal of excess water. The behaviour due to further loading resembles that of a re-constituted silt. For stress levels above the filtration pressure the calculated permeability values are similar to those from the filtration phase only if the data sampling rate was sufficiently rapid to discriminate the first portion of the observed primary consolidation curve. The change in void ratio for incremental loading is roughly linear with the change in the logarithm of the vertical effective stress. The characterisation of fresh cement paste using standard soil mechanics models is both appropriate and useful, at least during the first few hours after mixing.

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.

Artificial Intelligence Model for Flowable Concrete Mixtures Used in Underwater Construction and Repair

This study explores using artificial neural networks to predict the rheological and mechanical properties of underwater concrete (UWC) mixtures and to evaluate the sensitivity of such properties to variations in mixture ingredients. Artificial neural networks (ANN) mimic the structure and operation of biological neurons and have the unique ability of self-learning, mapping, and functional approximation. Details of the development of the proposed neural network model, its architecture, training, and validation are presented in this study. A database incorporating 175 UWC mixtures from nine different studies was developed to train and test the ANN model. The data are arranged in a patterned format. Each pattern contains an input vector that includes quantity values of the mixture variables influencing the behavior of UWC mixtures (that is, cement, silica fume, fly ash, slag, water, coarse and fine aggregates, and chemical admixtures) and a corresponding output vector that includes the rheological or mechanical property to be modeled. Results show that the ANN model thus developed is not only capable of accurately predicting the slump, slump-flow, washout resistance, and compressive strength of underwater concrete mixtures used in the training process, but it can also effectively predict the aforementioned properties for new mixtures designed within the practical range of the input parameters used in the training process with an absolute error of 4.6, 10.6, 10.6, and 4.4%, respectively.

Strength and permeation properties as an indicator of durability of concrete

Durability of concrete is a great concern to all designers, owners and users of reinforced concrete structures. As a result, more restrictive regulations are being introduced in various Codes of Practice dealing with the design of these structures. Attempts are being made by various researchers to develop performance based specification. For this to be successful standard non destructive tests are required which will be used to assess the durability of concretes. In parallel with this approach, a research team in Queen’s University Belfast, U. K., investigated the effect of different mix parameters on workability, strength and various permeation properties. Furthermore, durability parameters such as freeze-thaw salt scaling resistance and carbonation depth were also investigated. The research was part funded by the Department of Environment, Transport and the Regions (DETR). This paper reports of the findings from this study. The results from this investigation showed that some of the non destructive tests used were reasonably well correlated with carbonation and freeze-thaw salt scaling resistance of CEM I concrete. If the mix parameters such as aggregate-cement ratio or water-cement ratio are known, better correlation can be obtained. Further investigation is required varying other mix parameters including various aggregates, admixtures and air entrainments before the result can be used for developing mix design methods for durable concretes. Also long term site tests are required to validate the results obtained from the accelerated laboratory tests used to study the carbonation resistance and freeze-thaw salt scaling resistance.