Robust Earthquake Cluster Analysis Based on K-Nearest Neighbor Search

Samadi, Hamid Reza; Kimiaefar, Roohollah; Hajian, Alireza

doi:10.1007/s00024-020-02618-6

Cited by 5 publications

(3 citation statements)

References 27 publications

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“…For classification problems, the KNN algorithm assigns the new sample to the class that is most common among its KNN. In contrast, for regression problems, the KNN algorithm estimates the value of the new sample as the average or median of the output values of its KNN (Jiang et al ., 2007; Samadi et al. , 2020).…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Prediction of storey drift for reinforced concrete structures subjected to pulse-like ground motions using machine learning classification models

Wani,

Vemuri,

Chenna

2023

IJSI

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PurposeNear-fault pulse-like ground motions have distinct and very severe effects on reinforced concrete (RC) structures. However, there is a paucity of recorded data from Near-Fault Ground Motions (NFGMs), and thus forecasting the dynamic seismic response of structures, using conventional techniques, under such intense ground motions has remained a challenge.Design/methodology/approachThe present study utilizes a 2D finite element model of an RC structure subjected to near-fault pulse-like ground motions with a focus on the storey drift ratio (SDR) as the key demand parameter. Five machine learning classifiers (MLCs), namely decision tree, k-nearest neighbor, random forest, support vector machine and Naïve Bayes classifier , were evaluated to classify the damage states of the RC structure.FindingsThe results such as confusion matrix, accuracy and mean square error indicate that the Naïve Bayes classifier model outperforms other MLCs with 80.0% accuracy. Furthermore, three MLC models with accuracy greater than 75% were trained using a voting classifier to enhance the performance score of the models. Finally, a sensitivity analysis was performed to evaluate the model's resilience and dependability.Originality/valueThe objective of the current study is to predict the nonlinear storey drift demand for low-rise RC structures using machine learning techniques, instead of labor-intensive nonlinear dynamic analysis.

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Section: Machine Learning Algorithmsmentioning

confidence: 99%

Prediction of storey drift for reinforced concrete structures subjected to pulse-like ground motions using machine learning classification models

Wani,

Vemuri,

Chenna

2023

IJSI

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“…[ 26 ]. Cluster analysis was performed based on K-nearest neighbor search for improving earthquake mechanism identification and more reliable and stable pattern recognition for active seismicity regions [ 27 ]. Integrated petrophysical approaches were suggested in Refs.…”

Section: Introductionmentioning

confidence: 99%

Robust reservoir identification by multi-well cluster analysis of wireline logging data

Szabó

Kilik

Dobróka

2023

Heliyon

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“…Such a complex pattern of interactions leads to both long- and short-term clustering of seismicity over several spatial scales, e.g., [ 14 ]. For this reason, clustering features of seismic activity have been extensively studied using different approaches, ranging from classical statistical analysis to artificial intelligence, both in the laboratory and in real fault systems [ 15 , 16 , 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Clustering Analysis of Seismicity in the Anatolian Region with Implications for Seismic Hazard

Zaccagnino,

Telesca,

Tan

et al. 2023

Entropy

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The Anatolian region is one of the most seismically active tectonic settings in the world. Here, we perform a clustering analysis of Turkish seismicity using an updated version of the Turkish Homogenized Earthquake Catalogue (TURHEC), which contains the recent developments of the still ongoing Kahramanmaraş seismic sequence. We show that some statistical properties of seismic activity are related to the regional seismogenic potential. Mapping the local and global coefficients of variation of inter-event times of crustal seismicity which occurred during the last three decades, we find that territories prone to major seismic events during the last century usually host globally clustered and locally Poissonian seismic activity. We suggest that regions with seismicity associated with higher values of the global coefficient of variation of inter-event times, CV, are likely to be more prone to hosting large earthquakes in the near future than other regions characterized by lower values, if their largest seismic events have the same magnitude. If our hypothesis is confirmed, clustering properties should be considered as a possible additional information source for the assessment of seismic hazard. We also find positive correlations between global clustering properties, the maximum magnitude and the seismic rate, while the b-value of the Gutenberg–Richter law is weakly correlated with them. Finally, we identify possible changes in such parameters before and during the 2023 Kahramanmaraş seismic sequence.

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Robust Earthquake Cluster Analysis Based on K-Nearest Neighbor Search

Cited by 5 publications

References 27 publications

Prediction of storey drift for reinforced concrete structures subjected to pulse-like ground motions using machine learning classification models

Prediction of storey drift for reinforced concrete structures subjected to pulse-like ground motions using machine learning classification models

Robust reservoir identification by multi-well cluster analysis of wireline logging data

Clustering Analysis of Seismicity in the Anatolian Region with Implications for Seismic Hazard

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