Evaluation of Adaptive Boosting and Neural Network in Earthquake Damage Levels Detection

PeykHerfeh, Mona; Shahbahrami, Asadollah

doi:10.5120/17507-8058

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…This damage detection procedure was further incorporated into a comprehensive framework that links the column damage with the residual drift capacity and post-earthquake fragility curves of RC structures (German et al, 2013; Paal et al, 2014). Besides, ML methods have been implemented in dealing with satellite imageries and digital maps to detect and classify building damage (Gong et al, 2016; Peyk-Herfeh and Shahbahrami, 2014). Gao and Mosalam (2018) have also constructed an image database called “Structural ImageNet,” from which two DL technologies such as transfer learning (TL) and visual geometry group (VGGNet) were applied to recognize structural damage caused by earthquakes and other natural hazards.…”

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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“…This damage detection procedure was further incorporated into a comprehensive framework that links the column damage with the residual drift capacity and post-earthquake fragility curves of RC structures [23,24]. Besides, ML methods have been implemented in dealing with satellite imagery and digital maps to detect and classify building damage [25,26]. Gao and Mosalam [27] have also constructed an image database called ''Structural ImageNet,'' from which two DL technologies such as transfer learning (TL) and visual geometry group (VGGNet) were applied to recognize structural damage caused by earthquakes and other natural hazards.…”

Section: Introductionmentioning

confidence: 99%

Implementation of AI for The Prediction of Failures of Reinforced Concrete Frames

motaghed,

Shahid zadeh,

khooshecharkh

et al. 2022

IJRRS

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Reinforced concrete tall building failure, in residual areas, can cause catastrophic disaster if they can't survive during the destructive earthquakes. Hence, determining the damage of these buildings in the earthquake and detecting the probable mechanism formation are necessary for insurance purposes in urban areas. This paper aims to determine the failure modes of the moment resisting concrete frames (MRFs) according to the damage of the beam and column. To achieve this goal, a 15-storey moment resisting reinforced concrete frame is modeled via IDARC software, and nonlinear dynamic time history analysis is performed through 60 seismic accelerograms. Then the collapse and non-collapse vectors are constructed obtaining the results of dynamic analysis in both modes. The artificial neural network is used for the classification of the obtained modes. The results show good agreement in failures classes. Hence it is possible to introduce the simple weight factor for frame status identification.

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A method of using geomagnetic anomaly to recognize objects based on HOG and 2D-AVMD

et al. 2019

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In order to identify the shape of underground small magnetic anomaly objects, we use Support Vector Machines (SVM) to identify the underground magnetic anomaly targets. Firstly, as the SVM needs a lot of training data, and we also need to make full use of the magnetic field signal, nine component signals including total magnetic intensity (TMI) and five independent components of tensor are calculated from the original detected magnetic signal. Secondly, the nine component signals are subjected respectively to two-dimensional adaptively variational mode decomposition (2D-AVMD), which is advanced based on the two indicators, namely Mutual information (MI) and empirical entropy (EE), and we can get the nine primary signals from the decomposition results of nine component signals called the Intrinsic Mode Function (IMF). Then, the Histogram of Oriented Gradients (HOG) of the nine primary signals is extracted, and the feature data would be constructed into feature vectors. In the end, Support Vector Machines (SVM) are adopted to process these feature vectors. The output of the SVM can indicate the result of small objects’ shape recognition under the ground. Experiments prove that the shape recognition accuracy of underground small magnetic anomaly object recognition reaches 90%.

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Evaluation of Adaptive Boosting and Neural Network in Earthquake Damage Levels Detection

Cited by 3 publications

References 19 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

Implementation of AI for The Prediction of Failures of Reinforced Concrete Frames

A method of using geomagnetic anomaly to recognize objects based on HOG and 2D-AVMD

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