Uncertainties and Optimization in Geotechnical Engineering

The needs of modern times for more economical, more efficient construction lead to a move away from the one-dimensional concept of "security" and are now turning towards the search for the "optimum" in search of the best solution through a set of secure options. Recognition of this need led to the creation of original mathematical programming techniques that focus on solving optimization problems. They include finding a set of variables that optimize the objective function and satisfy some predefined design constraints. Their capabilities have now been widely recognized, resulting in applications of genetic and other evolutionary algorithms, as well as neural networks extending not only to nonlinear problems but also to all aspects of geotechnical engineering. Studies of metaheuristic optimization algorithms have shown that they can provide appreciable results in geotechnical applications, such as the example of generating random fields of soil properties using the following L.A.S. method. It is estimated that their wider application to practical and theoretical geotechnical problems can bring beneficial results and become a particularly useful tool in the hands of civil engineers.