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

Vrochidou, Εleni; Alvanitopoulos, Petros-Fotios; Andreadis, Ioannis; Elenas, Anaxagoras

doi:10.1007/978-3-642-33409-2_44

Cited by 4 publications

(7 citation statements)

References 9 publications

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“…For the second category, a preliminary feature selection process has been conducted. During this process, the outcomes of several studies [36,[69][70][71][72][73][74][75][76][77] investigating the interdependence between seismic parameters and damage indicators of RC structures have been taken into account. As a result, 16 prominent Intensity Measures (IMs) have been selected as seismic damage predictors to express the severity of the second shock.…”

Section: Feature Selection and Dataset Configuration And Preprocessingmentioning

confidence: 99%

Interpretable Machine Learning for Assessing the Cumulative Damage of a Reinforced Concrete Frame Induced by Seismic Sequences

Lazaridis,

Kavvadias,

Demertzis

et al. 2023

Sustainability

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Recently developed Machine Learning (ML) interpretability techniques have the potential to explain how predictors influence the dependent variable in high-dimensional and non-linear problems. This study investigates the application of the above methods to damage prediction during a sequence of earthquakes, emphasizing the use of techniques such as SHapley Additive exPlanations (SHAP), Partial Dependence Plots (PDPs), Local Interpretable Model-agnostic Explanations (LIME), Accumulated Local Effects (ALE), permutation and impurity-based techniques. Following previous investigations that examine the interdependence between predictors and the cumulative damage caused by a seismic sequence using classic statistical methods, the present study deploy ML interpretation techniques to deal with this multi-parametric and complex problem. The research explores the cumulative damage during seismic sequences, aiming to identify critical predictors and assess their influence on the cumulative damage. Moreover, the predictors contribution with respect to the range of final damage is evaluated. Non-linear time history analyses are applied to extract the seismic response of an eight-story Reinforced Concrete (RC) frame. The regression problem’s input variables are divided into two distinct physical classes: pre-existing damage from the initial seismic event and seismic parameters representing the intensity of the subsequent earthquake, expressed by the Park and Ang damage index (DIPA) and Intensity Measures (IMs), respectively. In addition to the interpretability analysis, the study offers also a comprehensive review of ML methods, hyperparameter tuning, and ML method comparisons. A LightGBM model emerges as the most efficient, among 15 different ML methods examined. Among the 17 examined predictors, the initial damage, caused by the first shock, and the IMs of the subsequent shock—IFVF and SIH—emerged as the most important ones. The novel results of this study provide useful insights in seismic design and assessment taking into account the structural performance under multiple moderate to strong earthquake events.

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Section: Feature Selection and Dataset Configuration And Preprocessingmentioning

confidence: 99%

Interpretable Machine Learning for Assessing the Cumulative Damage of a Reinforced Concrete Frame Induced by Seismic Sequences

Lazaridis,

Kavvadias,

Demertzis

et al. 2023

Sustainability

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“…Several recently reported studies employ the HHT to generate spectrum-compatible seismic accelerograms (Ni et al, 2011, 2013; Vrochidou et al, 2012, 2013; 2014a, 2014b). In methods proposed by Ni et al (2011, 2013), a set of six natural seismic records is essential in order to generate an artificial spectrum-compatible accelerogram.…”

Section: Introductionmentioning

confidence: 99%

“…The frequency is modified properly, rather than scaled, and the same optimization problem is solved. In the method suggested by Vrochidou et al (2012), one seismic signal of any region globally is utilized as the initial seed signal. The instantaneous amplitude and frequency values of the initial signal are properly modified to form the artificial seismic signal.…”

Section: Introductionmentioning

confidence: 99%

Artificial accelerograms composition based on the CEEMD

Vrochidou

Alvanitopoulos

Andreadis

et al. 2016

Transactions of the Institute of Measurement and Control

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In this work, a new methodology for generating spectrum-compatible accelerograms is presented. The proposed methodology considers the non-stationary and non-linear characteristics of seismic signals and utilizes the Hilbert–Huang transform (HHT) to analyse them. The two reported drawbacks of HHT, i.e. the mode mixing phenomenon and the end effects issue, are resolved through the proposed methodology. More specifically, the advantages of the recently introduced complementary ensemble empirical mode decomposition (CEEMD) are exploited in order to eliminate the mode mixing phenomenon. Moreover, the application of the proposed method only to the strong motion duration of the seismic signal assists to overcome the end effects issue. Thirty natural seismic records of different characteristics, such as frequency content, amplitude and duration, are employed as initial seed signals to demonstrate the proposed method. To illustrate the effectiveness of the proposed technique, comparisons with three established methods for generating spectrum-compatible seismic accelerograms are also provided.

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“…Previously reported methods in the literature employ HHT to generate spectrumcompatible accelerograms [4,5]. In method [4] six natural records are required to describe the high and low frequency areas of the target design spectrum.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, it can be of any intensity and thus no limitations are imposed. Another method is proposed in [5].Artificial seismic signals are generated and submitted to a correlation study. The correlation study demonstrates that the artificially generated seismic signals share similar seismic parameter values to those of natural accelerograms.…”

Section: Introductionmentioning

confidence: 99%

HHT-Based Artificial Seismic Accelerograms Generation

Vrochidou

Alvanitopoulos

Andreadis

et al. 2014

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Part 11: Simulations and Fuzzy ModelingInternational audienceA 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

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

Cited by 4 publications

References 9 publications

Interpretable Machine Learning for Assessing the Cumulative Damage of a Reinforced Concrete Frame Induced by Seismic Sequences

Interpretable Machine Learning for Assessing the Cumulative Damage of a Reinforced Concrete Frame Induced by Seismic Sequences

Artificial accelerograms composition based on the CEEMD

HHT-Based Artificial Seismic Accelerograms Generation

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