Classification of Earthquake-Induced Damage for<i>R/C</i>Slab Column Frames Using Multiclass SVM and Its Combination with MLP Neural Network

Kia, Ali Habibi; Şensoy, Serhan

doi:10.1155/2014/734072

Cited by 11 publications

(5 citation statements)

References 23 publications

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“…In a broader context, ANN models have been developed to predict the seismic response for a variety of structures so as to infer their damage conditions. Related studies in this area include (1) quick earthquake damage estimation on ordinary wooden framed houses in Japan (Molas and Yamazaki, 1995); (2) seismic vulnerability assessment of chemical industrial plants with various topologies (Aoki et al, 2002); (3) damage index prediction of RC frames (De Lautour and Omenzetter, 2009; Morfidis and Kostinakis, 2017, 2018); (4) seismic damage evaluation of concrete shear walls (Vafaei et al, 2013) and cantilever structures (Vafaei et al, 2014); and (5) global damage classification of RC slab-column frames by combining ANN with SVM (Kia and Sensoy, 2014). ML has also been utilized by Burton and his coworkers (Burton et al, 2017; Zhang and Burton, 2019; Zhang et al, 2018) to link the seismic damage patterns of buildings to the residual structural capacity indices (i.e.…”

Section: System Identification and Damage Detectionmentioning

confidence: 99%

The promise of implementing machine learning in earthquake engineering: A state-of-the-art review

et al. 2020

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Machine learning (ML) has evolved rapidly over recent years with the promise to substantially alter and enhance the role of data science in a variety of disciplines. Compared with traditional approaches, ML offers advantages to handle complex problems, provide computational efficiency, propagate and treat uncertainties, and facilitate decision making. Also, the maturing of ML has led to significant advances in not only the main-stream artificial intelligence (AI) research but also other science and engineering fields, such as material science, bioengineering, construction management, and transportation engineering. This study conducts a comprehensive review of the progress and challenges of implementing ML in the earthquake engineering domain. A hierarchical attribute matrix is adopted to categorize the existing literature based on four traits identified in the field, such as ML method, topic area, data resource, and scale of analysis. The state-of-the-art review indicates to what extent ML has been applied in four topic areas of earthquake engineering, including seismic hazard analysis, system identification and damage detection, seismic fragility assessment, and structural control for earthquake mitigation. Moreover, research challenges and the associated future research needs are discussed, which include embracing the next generation of data sharing and sensor technologies, implementing more advanced ML techniques, and developing physics-guided ML models.

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Section: System Identification and Damage Detectionmentioning

confidence: 99%

The promise of implementing machine learning in earthquake engineering: A state-of-the-art review

et al. 2020

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“…Therefore, the Slab SVM method was proposed by Ref. [17] to solve this problem by constraining the sample points between two parallel hyperplanes. The samples can be better classified in the stripe form.…”

Section: Abnormal Traffic Detection Based On Traditional Machine Learmentioning

confidence: 99%

Abnormal Traffic Detection Based on Generative Adversarial Network and Feature Optimization Selection

Ma¹,

Zhang²,

Guo³

et al. 2021

IJCIS

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Complex and multidimensional network traffic features have potential redundancy. When traditional detection methods are used for training samples, the detection accuracy of the supervised classification model is affected due to small data samples. Therefore, a method based on generative adversarial networks (GANs) and feature optimization is proposed. First, the feature correlation and redundancy are analyzed by the potential redundancy of network traffic. The feature optimization selection method of collaborative learning automata is proposed. Second, the confrontation interactive training principle of the generative confrontation network is adapted, in which a model of the generative confrontation network is proposed to solve the problem that small training label samples. Third, the interdomain distance is minimized by using GAN and the multiple kernel variant of maximum mean discrepancy (MK-MMD). The shared features between the source domain and target domain distribution are learned by applying the information between GAN confrontation training and classification network supervision training, improving the detection accuracy. Forth, random noise data and original training label samples are mixed to form a new training set. The accuracy is further improved by adopting generative models to continuously generate samples. The final classification results are output by the 16-dimensional Softmax classifier. The method has a small loss rate when the datasets are used to train by the experimental analysis of algorithm parameters and simulation data. The model optimized by MK-MMD has strong generalization ability. The average detection accuracy rates are 91.673% (two-classification) and 91.480% (multiclassification) by comparing machine learning and other shallow neural networks, and are the highest values among the compared methods. Moreover, the effectiveness and superiority of the proposed method are verified to be the best by comparing the recall rate, false positive rate (FPR), F-measure, AUC. When the interference of other samples are mixed, the proposed method is also robust.

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“…Therefore, for N-dimensional area, the classifier is a hyperplane. Assuming that the distance between each class data point and the classifier equals to 1 [47].…”

Section: Model Selectionmentioning

confidence: 99%

Application of Support Vector Machine Modeling for the Rapid Seismic Hazard Safety Evaluation of Existing Buildings

et al. 2020

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The economic losses from earthquakes tend to hit the national economy considerably; therefore, models that are capable of estimating the vulnerability and losses of future earthquakes are highly consequential for emergency planners with the purpose of risk mitigation. This demands a mass prioritization filtering of structures to identify vulnerable buildings for retrofitting purposes. The application of advanced structural analysis on each building to study the earthquake response is impractical due to complex calculations, long computational time, and exorbitant cost. This exhibits the need for a fast, reliable, and rapid method, commonly known as Rapid Visual Screening (RVS). The method serves as a preliminary screening platform, using an optimum number of seismic parameters of the structure and predefined output damage states. In this study, the efficacy of the Machine Learning (ML) application in damage prediction through a Support Vector Machine (SVM) model as the damage classification technique has been investigated. The developed model was trained and examined based on damage data from the 1999 Düzce Earthquake in Turkey, where the building’s data consists of 22 performance modifiers that have been implemented with supervised machine learning.

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Classification of Earthquake-Induced Damage forR/CSlab Column Frames Using Multiclass SVM and Its Combination with MLP Neural Network

Cited by 11 publications

References 23 publications

The promise of implementing machine learning in earthquake engineering: A state-of-the-art review

The promise of implementing machine learning in earthquake engineering: A state-of-the-art review

Abnormal Traffic Detection Based on Generative Adversarial Network and Feature Optimization Selection

Application of Support Vector Machine Modeling for the Rapid Seismic Hazard Safety Evaluation of Existing Buildings

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