A Neural-Genetic Technique for Coastal Engineering: Determining Wave-induced Seabed Liquefaction Depth

Abstract

In the past decade, computational intelligence (CI) technologies have been widely adopted in various fields such as business, science and engineering, as well as information technology. Specifically, hybrid techniques using artificial neural networks (ANNs) and genetic algorithms (GAs) are becoming an important alternative for solving problems in the field of engineering in comparison to traditional solutions, which ordinarily use complicated mathematical theories. The wave-induced seabed liquefaction problem is one of most critical issues for analysing and designing marine structures such as caissons, oil platforms and harbours. In the past, various investigations into wave-induced seabed liquefaction have been carried out including numerical models, analytical solutions and some laboratory experiments. However, most previous studies are based on complicated mathematical theories. In this study, the proposed a neural-genetic model will be applied to wave-induced liquefaction, provide a better prediction of liquefaction potential. The ANN+GA simulation results illustrate the applicability of a hybrid, neural-genetic technique for the accurate prediction of wave-induced liquefaction depth, which can also provide coastal engineers with alternative tools to analyse the stability of marine sediments.

An intelligent approach to handle False-Positive Radio Frequency Identification Anomalies

“…The child chromosomes can be created with different means such as one-point, two-point or uniform crossover [25]. Mutation will then be applied to a certain small percent of the population to assure that the network avoids problems such as network paralysis or local minima [6]. A general flow diagram interpretation of the Evolutionary Neural Network training process may viewed in Figure 6.…”