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Waste rubber tires have been used in building materials to support the environment and green construction. There is a growing demand for rubberized materials as they are cost-effective and useful from a sustainable standpoint. There are several properties of the rubber tire that could be applied usefully such as low density, and waterproofing properties. Rubberized concrete has composed of waste rubber as natural aggregate and is an alternative solution to the use of tire rubber particles in the production of concrete. It has been proven that the addition of waste rubber tires to concrete starts to low strength and this restriction its application in structural elements but benefits to enhance the ductility, impact resistance, thermal conductivity, and acoustic properties. This paper presents a review of the recent studies on the application and development of artificial neural networks (ANNs) to predict the compressive strength of rubberized concrete. From this review, predicting the compressive strength of rubberized concrete by ANNs is generally more accurate, and their development is inexpensive and time-saving. In this review, the advantages, and limitations of ANNs to predict the compressive strength of rubberized concrete, as well as some new research trends have been addressed. [ABSTRACT FROM AUTHOR]

The soil under any foundation should be capable enough to support the transmitted loads so that the structure– foundation–soil system is stable and safe against any settlement. Any change in the unconfined compressive strength is one of the principal causes of settlement. In this paper, an approach to forecast the unconfined compressive strength of gypseous soils based on basic soil properties was developed using the Artificial Neural Networks technique. An equation was developed to estimate the unconfined compressive strength using a backpropagation algorithm to train multi-layer perceptron, in which good agreements were achieved. The model inputs were; the depth, gypsum content, liquid limit, plastic limit, plasticity index, passing sieve #200, dry unit weight, water content and initial voids ratio. The output was the forecasted unconfined compressive strength. A parametric study was conducted to investigate the generalization and robustness of the model. The findings indicated that the model was reliable within the range of utilized data. The sensitivity analysis of the model revealed that the water content had the highest relative effect, followed by passing sieve #200, dry unit weight, liquid limit, plastic limit and gypsum content, while depth, initial voids ratio and plasticity index have lower relative effects. [ABSTRACT FROM AUTHOR]