Petros-Fotios Alvanitopoulos

Interdependence between damage indices and ground motion parameters based on Hilbert-Huang transform

Feature extraction from seismic accelerograms is a key issue in characterization of earthquake damage in structures. Until today, a number of effective classical parameters such as peak ground acceleration (PGA) and Arias intensity have been proposed for analyzing the earthquake motion records. The aim of this paper is to search for new crucial characteristic seismic parameters which provide information pertinent to the damage indicators of the structures. The first proposed parameter is the maximum amplitude (AHHT max) and the second is the mean amplitude (AHHT mean). Emphasis of our work has been placed on the use of the Hilbert‐Huang transform (HHT). A set of 13 natural accelerograms from worldwide well-known sites with strong seismic activity have been used. The HHT has been applied to the nonlinear and non-stationary data (earthquake recordings). Each complex seismic accelerogram is decomposed into several simple components called intrinsic mode functions (IMFs). Using the IMFs a three-dimensional time‐frequency distribution of earthquake excitation is computed and two new seismic parameters are proposed and evaluated. After the numerical computation of all the seismic parameters (classical and proposed), nonlinear dynamic analysis is carried out to provide the post-seismic damage status of the structure under study. Two structural damage indices are utilized and the degree of interrelation among them and the seismic parameters is provided by correlation coefficients. Furthermore, two different reinforced concrete structures are examined. Results indicate the high correlation of the new seismic parameters (AHHT max ,A HHT mean) with the damage indices and confirm that HHT is a promising tool for extracting information to characterize damage in structures.

Seismic Intensity Feature Construction Based on the Hilbert–Huang Transform

An increasing number of seismic parameters for the representation of the severity of earthquake signals have been proposed in recent years. Considering the complex nature of seismic accelerograms, there is an obvious need for a more effective representation. The destructiveness of a seismic wave cannot be always described using a single feature of the examined ground motion. Moreover, the level of structural damage caused by a severe earthquake depends on the characteristics of the oscillated structure. The aim of this study is to propose new crucial ground-motion parameters, which reflect the damage potential of the seismic excitations. The Hilbert-Huang transform (HHT) is applied to a set of 70 natural accelerograms. Through the HHT, the complex earthquake signals are decomposed into several simple components called intrinsic mode functions. After a thorough analysis, eight new seismic parameters are extracted. The key novelty of this paper is that the frequency-based new seismic parameters are associated with the eigenfrequency of the oscillated structures. By studying the correlation between the new seismic parameters and well-known damage indices (DIs), their interdependence is confirmed. The achieved high levels of correlation indicate that the proposed parameters are directly related to structural damage. Furthermore, the application of the HHT improves the correlation coefficients between the widely used classical earthquake parameters and DIs. The quality of the proposed HHT-based parameters is consolidated by the use of minimal-redundancy-maximal-relevance feature selection. A subset of six seismic parameters is tested by the use of a support vector machine classifier, providing a 91.4% classification performance. Results indicate that the new seismic features along with the proposed optimized classical parameters can be considered highly competitive descriptors of the seismic damage potential.

Solar radiation prediction model based on Empirical Mode Decomposition

Accurate solar radiation data are a key factor in Photovoltaic system design and installation. Efficient solar radiation time series prediction is regarded as a challenging task for researchers both in the past and at present. This paper deals with solar radiation time series prediction. To date an essential research effort has been made and various methods are proposed that have different mathematical backgrounds, such as artificial neural networks, fuzzy predictors, evolutionary and genetic algorithms. In the present study the solar radiation time series prediction combines the Empirical Mode Decomposition (EMD) and Support Vector Regression (SVR) models. The EMD is an adaptive signal processing technique that decomposes the nonstationary and nonlinear signals into a set of components with a different spatial frequency content. It results in a small set of new time series that are easier to model and predict. The SVR is applied to the new solar radiation time series. Since support Vector Machines provide great generalization ability and guarantee global minima for given training data, the performance of SVR is investigated. Simulation results demonstrate the feasibility of applying SVR in solar radiation time series prediction and prove that SVR is applicable and performs well for solar radiation data prediction.

Synthesis of artificial spectrum-compatible seismic accelerograms

The Hilbert–Huang transform is used to generate artificial seismic signals compatible with the acceleration spectra of natural seismic records. Artificial spectrum-compatible accelerograms are utilized instead of natural earthquake records for the dynamic response analysis of many critical structures such as hospitals, bridges, and power plants. The realistic estimation of the seismic response of structures involves nonlinear dynamic analysis. Moreover, it requires seismic accelerograms representative of the actual ground acceleration time histories expected at the site of interest. Unfortunately, not many actual records of different seismic intensities are available for many regions. In addition, a large number of seismic accelerograms are required to perform a series of nonlinear dynamic analyses for a reliable statistical investigation of the structural behavior under earthquake excitation. These are the main motivations for generating artificial spectrum-compatible seismic accelerograms and could be useful in earthquake engineering for dynamic analysis and design of buildings.According to the proposed method, a single natural earthquake record is deconstructed into amplitude and frequency components using the Hilbert–Huang transform. The proposed method is illustrated by studying 20 natural seismic records with different characteristics such as different frequency content, amplitude, and duration. Experimental results reveal the efficiency of the proposed method in comparison with well-established and industrial methods in the literature.

Correlation between Seismic Intensity Parameters of HHT-Based Synthetic Seismic Accelerograms and Damage Indices of Buildings

In this work, a correlation study between well-known seismic parameters and damage indices is presented. The correlation analysis is first performed on a set of natural seismic signals and afterwards on a set of artificial accelerograms generated from natural records. The artificial seismic signals are generated by a combination of techniques; the Hilbert-Huang transform and an optimization algorithm. In addition, they are compatible with the design spectra of a chosen seismic area. Results reveal that the seismic parameters of the synthetic earthquake accelerograms provide the same degree of correlation with the used damage indices as the natural earthquakes. Thus, the proposed synthetic accelerograms technique appropriately represents a seismic event and, therefore, it is a useful tool in earthquake engineering.

A Genetic Algorithm for the Classification of Earthquake Damages in Buildings

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 neuro-fuzzy classification systems present satisfactory classification results in different types of earthquake damages. In this approach a genetic algorithm (GA) was used to find the optimal feature subset of the seismic parameters that minimizes the computational cost and maximizes the classification performance. Experimental results indicate that the use of the GA was able to classify the structural damages with classification rates up to 92%.

Intelligent Systems for Structural Damage Assessment

Abstract This research provides a comparative study of intelligent systems in structural damage assessment after the occurrence of an earthquake. Seismic response data of a reinforced concrete structure subjected to 100 different levels of seismic excitation are utilized to study the structural damage pattern described by a well-known damage index, the maximum inter-story drift ratio (MISDR). Through a time-frequency analysis of the accelerograms, a set of seismic features is extracted. The aim of this study is to analyze the performance of three different techniques for the set of the proposed seismic features: an artificial neural network (ANN), a Mamdani-type fuzzy inference system (FIS), and a Sugeno-type FIS. The performance of the models is evaluated in terms of the mean square error (MSE) between the actual calculated and estimated MISDR values derived from the proposed models. All models provide small MSE values. Yet, the ANN model reveals a slightly better performance.

Solar Radiation Time-Series Prediction Based on Empirical Mode Decomposition and Artificial Neural Networks

This paper presents a new model for daily solar radiation prediction. In order to capture the hidden knowledge of existing data, a time-frequency analysis on past measurements of the solar energy density is carried out. The Hilbert-Huang transform (HHT) is employed for the representation of the daily solar irradiance time series. A set of physical measurements and simulated signals are selected for the time series analysis. The empirical mode decomposition is applied and the adaptive basis of each raw signal is extracted. The decomposed narrow-band amplitude and frequency modulated signals are modelled by using dynamic artificial neural networks (ANNs). Nonlinear autoregressive networks are trained with the average daily solar irradiance as exogenous (independent) input. The instantaneous value of solar radiation density is estimated based on previous values of the time series and previous values of the independent input. The results are promising and they reveal that the proposed system can be incorporated in intelligent systems for better load management in photovoltaic systems.

HHT-Based Artificial Seismic Accelerograms Generation

A new efficient approach for generating spectrum-compatible seismic accelerograms is proposed. It is based on the Hilbert-Huang Transform (HHT); one natural seismic accelerogram is decomposed into frequency and amplitude components. The components are appropriately modified to synthesize the artificial seismic accelerogram that appears to have compatible acceleration spectrum with the natural seismic accelerogram. The HHT is an adaptive signal processing technique for analyzing nonlinear and non-stationary data such as seismic accelerograms. With HHT a seismic accelerogram is decomposed into a finite and small set of components. These components have well defined instantaneous frequencies, estimated by the first derivative of the phase of the analytic signal. The method is tested using twenty natural seismic records and a comparison with two established methodologies is provided.

Fuzzy Inference Systems for Automatic Classification of Earthquake Damages

This paper presents efficient models in the area of damage potential classification of seismic signals. After an earthquake, one of the most important actions that authorities must take is to inspect structures and estimate the degree of damages. The interest is obvious for several reasons such as public safety, economical recourses management and infrastructure. This approach provides a comparative study between the Mamdani-type and Sugeno-type fuzzy inference systems (FIS). The fuzzy models use a set of artificial accelerograms in order to classify structural damages in a specific structure. Previous studies propose a set of twenty well-known seismic parameters which are essential for description of the seismic excitation. The proposed fuzzy systems use an input vector of twenty seismic parameters instead of the earthquake accelerogram and produce classification rates up to 90%. Experimental results indicate that these systems are able to classify the structural damages in structures accurately. Both of them produce the same level of correct classification rates but the Mamdani-type has a slight superiority.