Artificial neural network (ANN) is a soft computing technique that has been used to predict with accuracy compressive strength known for its high variability of values. ANN is used to develop a model that can predict compressive strength of rubberized concrete where natural aggregate such as fine and coarse aggregate are replaced by crumb rubber and tire chips. The main idea in this study is to build a model using ANN with three parameters that are: water/cement ratio, Superplasticizer, granular squeleton. Furthermore, the data used in the model has been taken from various literatures and are arranged in a format of three input parameters: water/ cement ratio, superplasticizer, granular squeleton that gathers fine aggregates, coarse aggregates, crumb rubber, tire chips and output parameter which is compressive strength.The performance of the model has been judged by using correlation coefficient, mean square error, mean absolute error and adopted as the comparative measures against the experimental results obtained from literature. The results indicate that artificial neural network has the ability to predict compressive strength of rubberized concrete with an acceptable degree of accuracy using new parameters.

The phenomenon of swelling is one of the more complicated geotechnical problems that the engineer have to deal with. However, its quantification is essential for the design of structures and various methods can be applied to the identification of this phenomenon. Some, such as mineralogical identification and direct measurements of swelling, are more or less long and require very specific equipment. However, there are other methods that offer the advantage of being relatively fast and lesser expensive: they are based on soil mechanics parameters. Using these parameters, several authors have introduced soil swelling prediction models, mostly in the form of classifications and empirical formulas. This work concerns in the first part the identification and classification of the swelling potential of two clays located in north-western Algeria. Followed by a statistical analysis carried out to test the reliability of the observations for the estimation of the pressure and the swelling amplitude using a multiple linear regression. A second part is devoted to the development of a prediction method by artificial neural networks allowing the estimation of swelling parameters (pressure and amplitude) by minimizing the difference between the experimental measurements and the numerical results. Modeling by artificial neural networks is of great interest in the field of prediction. The application of two networks makes it possible to obtain good forecasts of the swelling parameters.

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Swelling and shrinkage of expansive soils occur mainly due to a change in the moisture regime and pose serious problems to foundations causing damage to structures founded on them. However, construction on this type of soil requires a good companion for the recognition of identification, characterization of their swelling potential and Treatment processing.In this work we are interested by two aspects:1) The first is on the tests recommended for the identification of diferants expansive soils in the region of Tlemcen in the north western Algeria.2) The second is to perform stabilization tests on remolded samples by salts (KCl Potassium Chloride, Magnesium Chloride MgCl 2 ) with deffrants concentrations and see their influence on physic-chemical parameters and swelling.The results obtained show that stabilization by the addition of salts modifies the physico-chemical characteristics of soil and the results are quite satisfactory in significantly reducing the phenomenon of swelling, as regards the effect of salt on the swelling pressure it varies from salt to another and concentration to another.

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Clay soil is the foundation for many buildings. So me families have the characteristic to be swelling or shrink. However, construction on this type of soil requires a good companion for the recognition of identification and characterizat ion of their swelling potential. In this work we are interested by two aspects:-The first is on the tests recommended for the identification o f four expansive soils in the region of Tlemcen (Algeria);-The second is to assess the influence of the addit ion of milk of lime and mineral salts (KCl Potassium Chloride, Magnesium Ch loride MgCl 2) at different percentages on the physico-chemical parameters, the swelling potential of these soils fro m co rrelations approximate estimates. The results obtained show that stabilization by the addition o f milk of lime mod ifies the physico-chemical characteristics of soil and the results are quite satisfactory in significantly reducing the phenomena of swelling, as regards stabiliztion with milk of lime we notethat the salt concentration has little influence on the potential of swelling and it differs fro m one another salt.

Reinforcement corrosion is one of the main phenomena determining the life of a structure. It can be tracked using methods based on several indicators of the probability of corrosion. These measures can be more or less lengthy and can require very specific equipment. In recent years, several non-destructive tests have been developed that are relatively fast and less costly based on the measurement of corrosion potential. In this study, a statistical analysis is performed, using a multiple linear regression, to test the reliability of the data obtained by experimental measurement of the corrosion potential. Artificial neural networks (ANN) are then used to develop a model to predict the corrosion potential of reinforcement in a concrete or mortar. The results indicate that the artificial neural network can predict corrosion potential with an acceptable degree of accuracy.

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