Using Artificial Neural Networks Approach to Estimate Compressive Strength for Rubberized Concrete

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

Swelling and Geotechnical Cartography of Saida Soils

The urban expansion and the increase in population led the urbanization to use the spaces called “at risk”.The integration and the treatment of ground’s movements constitute an important characteristic of the equilibrium established by nature. In the Saïda town (Algeria), many projects built on grounds with problems showed signs of degradations such as cracks in structures. These degradations led to the total destruction of the buildings. The principal causes of these disasters are: the expansive nature of soils and landslides, and the disaster phenomenon not considered in the first study of these constructions. The damage also touched road embankments, highways and foundations. In order to solve these problems, it was necessary to propose a geotechnical and risk map for the ZHUN EAST (ESSALAM city, Saïda), a city which includes several yards of the soil with problems, using the Geographical Information Systems (GIS/MapInfo). These tools enable us to express the perception of space and data processing, and consequently the cartography is carried out in an optimal way. These geotechnical and risk maps have a great part of importance in all levels of a study as information, working paper, alert, and especially is a tool for the decision-making aid, by expressing tendencies and orientations. They enabled us to give a field representing the active and potential movements with a hierarchy of risks to guide the developer and the engineer

Prediction of Corrosion Potential by Generalized Artificial Neural Networks Method

Reinforcement corrosion is one of the main phenomena determining the life of the structure. It can be followed by methods based on several indicators of the probability of corrosion. Some of these measures are more or less long and require very specific equipment. In recent years, several non-destructive tests have been developed to be 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) is 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.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author