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Clayey soils are known as problematic soils for geotechnical engineering since several years. The effect of mineral additives on geotechnical properties of clayey soils has been many times investigated. However, there are a few investigations about the use of artificial neural networks (ANNs) for predicting the geotechnical properties of stabilised soils, all the same, the ANNs can be successfully used in this field. The accurate prediction of plasticity index (PI), maximum dry density (MDD) and optimum moisture content (OMC) is beneficial for the construction engineering in order to avoid the cumbersome tests in the laboratory. The aim of this research is to develop three models with good performances based on ANNs, and to predict all the PI, OMC and MDD values of subgrade soil stabilised with the addition of lime, using basic soil parameters which are always available for engineers. Three different models are developed which each one corresponding to the best architecture for the three properties where these models can be used as a reliable tool to predict the PI, OMC and MDD of clayey soils stabilised with lime. [ABSTRACT FROM AUTHOR]

This paper investigates the feasibility of using artificial neural networks (ANNs) modeling to predict the properties of self-compacting concrete (SCC) containing fly ash as cement replacement. For the purpose of constructing this model, a database of experimental data was gathered from the literature and used for training and testing the model. The data used in the artificial neural network model are arranged in a format of six input parameters that cover the total binder content, fly ash replacement percentage, water-binder ratio, fine aggregates, coarse aggregates and superplasticizer. Four outputs parameters are predicted based on the ANN technique as the slump flow, the L-box ratio, the V-funnel time and the compressive strength at 28 days of SCC. To demonstrate the utility of the proposed model and improve its performance, a comparison of the ANN-based prediction model with other researcher's experimental results was carried out, and a good agreement was found. A sensitivity analysis was also conducted using the trained and tested ANN model to investigate the effect of fly ash on SCC properties. This study shows that artificial neural network has strong potential as a feasible tool for predicting accurately the properties of SCC containing fly ash. [ABSTRACT FROM AUTHOR]

The relative performance of various supplementary cementitious materials (SCMs) can be compared with that of portland cement using the practical concept of the efficiency factor (or χ value). This study describes the use of artificial neural networks (ANNs) for the prediction of the efficiency factor of ground-granulated blast-furnace slag (GGBFS) in concrete based on published test results. Feed-forward back-propagation neural networks have been used. The ANN model was established by the incorporation of a large experimental database and by appropriately choosing the architecture and training process. The introduced ANN model provided a more accurate tool to calculate χ and capture the effects of five main parameters: concrete composition (the waterbinder ratio [w/b]; cement dosage; and the GGBFS replacement level); age; and curing temperature, confirming the reported experimental data. This study shows that the use of this model for numerical investigations on the parameters affecting the efficiency of SCMs in concrete is successful. A mathematical model was also developed based on the ANN model's results for predicting the χ value of GGBFS in terms of percentage replacement (from 0 to 80%) and concrete age (from 2 to 90 days), as these are considered the most important factors affecting concrete strength. This evaluation makes it possible to design GGBFS concretes for a desired strength at any given age and replacement level. [ABSTRACT FROM AUTHOR]