Optimal Design of Earthquake-Resistant Buildings Based on Neural Network Inversion

Abstract

An effective seismic design entails many issues related to the capacity-based assessment of the non-linear structural response under strong earthquakes. While very powerful structural calculation programs are available to assist the designer in the code-based seismic analysis, an optimal choice of the design parameters leading to the best performance at the lowest cost is not always assured. The present paper proposes a procedure to cost-effectively design earthquake-resistant buildings, which is based on the inversion of an artificial neural network and on an optimization algorithm for the minimum total cost under building code constraints. An exemplificative application of the method to a reinforced-concrete multi-story building, with seismic demands corresponding to a medium-seismicity Italian zone, is shown. Three design-governing parameters are assumed to build the input matrix, while eight capacity-design target requirements are assigned for the output dataset. A non-linear three-dimensional concentrated plasticity model of the structure is implemented, and time-history dynamic analyses are carried out with spectrum-consistent ground motions. The results show the promising ability of the proposed approach for the optimal design of earthquake-resistant structures.

A Satellite Data Mining Approach Based on Self-Organized Maps for the Early Warning of Ground Settlements in Urban Areas

“…More recently, neuralnetwork-based algorithms were used to analyze geological data for different purposes, including detection of hydrocarbon resources, pollution, and oil spills [30], classification of complex natural ecosystems [31,32], creating hazard survey maps in earthquake-prone areas [33], and assessment of some properties of soils [34,35]. In the civil engineering field, neural-network-based methods are currently used, for instance, to predict blast-induced ground vibrations [36], to control the behavior of buildings subjected to ground motion [37], to improve the features extraction under ultrasonic tests [38], or to optimize the structural design [39].…”