In this paper an efficient classification system in the area of earthquake engineering is reported. The proposed method uses a set of artificial accelerograms to examine several types of damages in specific structures. With the use of seismic accelerograms, a set of twenty seismic parameters have been extracted to describe earthquakes. Previous studies based on artificial neural networks and ne...

We board the problem of creating a seismic alert system, based upon artificial neural networks, trained by using the well-known back-propagation and genetic algorithms, in order to emit the alarm for the population located into a specific city, about an eminent earthquake greater than 4.5 Richter degrees, and avoiding disasters and human loses. In lieu of using the propagation wave, we employed...

During an earthquake, the motion of the ground spatially changes, in both amplitude and phase. The spatial variation of seismic ground motions has an important effect on the response of large structures such as bridges and dams. To be able to simulate seismic ground motions which vary in space, a representing spatial variability model is required. Data collected from closely spaced arrays of se...

Structural vibration control is one of the most important features in structural engineering. Real-time information about seismic resultant forces is required for deciding module of intelligent control systems. Evaluation of lateral forces during an earthquake is a complicated problem considering uncertainties of gravity loads amount and distribution and earthquake characteristics. An artificia...

This research intends to develop a method based on the Artificial Neural Network (ANN) to predict permanent earthquake-induced deformation of the earth dams and embankments. For this purpose, data sets of observations from 152 published case histories on the performance of the earth dams and embankments, during the past earthquakes, was used. In order to predict earthquake-induced deformation o...

A recently developed nonstationary stochastic model of strong earthquake ground motion is described. The model employs filtering of a white-noise process, where nonstationarity is achieved by modulating the intensity and varying the filter parameters in time. The model is fitted to a target accelerogram by matching a set of statistical measures that characterize the evolution of the intensity, ...

A new earthquake prediction system is presented in this work. This method, based on the application of artificial neural networks, has been used to predict earthquakes in Chile, one of the countries with larger seismic activity. The input values are related to the b-value, the Bath’s law, and the Omori–Utsu’s law, parameters that are strongly correlated with seismicity, as shown in solid previo...

This paper presents the preliminary results of a joint research project about Smart Cities. This project is adopting a multidisciplinary approach that combines artificial intelligence techniques with psychology research to monitor the current state of the city of L’Aquila after the dreadful earthquake of April 2009. This work focuses on the description of a semantic content analysis module. Thi...

We present a virtual reality simulation of an evacuation caused by a trembling earthquake in a secondary school. The simulation is based on artificial intelligence for which a mathematical model is implemented that controls the behavior of agents that represent the school students in the virtual stage. The simulation allows the interaction with the agents on stage by using the gyroscope of a sm...