Soft Schemes for Earthquake-Geotechnical Dilemmas

Abstract

Models of real systems are of fundamental importance in virtually all disciplines because they can be useful for gaining a better understanding of the organism. Models make it possible to predict or simulate a system's behavior; in earthquake geotechnical engineering, they are required for the design of new constructions and for the analysis of those that exist. Since the quality of the model typically determines an upper bound on the quality of the final problem solution, modeling is often the bottleneck in the development of the whole system. As a consequence, a strong demand for advanced modeling and identification schemes arises. During the past years, soft computing techniques have been used for developing unconventional procedures to study earthquake geotechnical problems. Considering the strengths and weaknesses of the algorithms, in this work a criterion to leverage the best features to develop efficient hybrid models is presented. Via the development of schemes for integrating data-driven and theoretical procedures, the soft computing tools are presented as reliable earthquake geotechnical models. This assertion is buttressed using a broad history of seismic events and monitored responses in complicated soils systems. Combining the versatility of fuzzy logic to represent qualitative knowledge, the data-driven efficiency of neural networks to provide fine-tuned adjustments via local search, and the ability of genetic algorithms to perform efficient coarse-granule global search, the earthquake geotechnical problems are observed, analyzed, and solved under a holistic approach.