Simulation using artificial neural networks in geotechnical engineering

With the rapid technological and industrial development that the world has seen, particularly in construction technology with these huge oil platforms in the depths of the ocean or desert. This requires an adequate load-bearing structural system, capable of distributing forces from one level to another until they reach the foot of the structure, known as the foundation. The important role of deep foundations in transmitting service loads from the superstructure to the deep soil bearing layers has prompted the use of empirical and semi-empirical methods for the axial bearing capacity design of a pile. Alternatively, artificial neural networks (ANNs) have recently been used to predict the ultimate capacity of piles based on in situ tests. Very recently, several researchers have successfully used the RNAs artificial neural network approach for the development of integrated models in conjunction with other probabilistic and evolutionary methods.

Autorentext

Amal Benali, dottore di ricerca in ingegneria civile presso l'Università Houari Boumediene di Algeri, Algeria. Ha conseguito il Magister e l'Ingéniorat in ingegneria civile rispettivamente nel 2000 e nel 1998. È anche docente presso l'Università di Khemis Milana. In qualità di direttrice del dipartimento di ingegneria civile, ha contribuito all'apertura di diverse specializzazioni.