Seismic risk assessment of storage tanks in Turkish industrial facilities

Korkmaz, Kasım Armağan; Sarı, Ali; Çarhoğlu, Asuman Işıl

doi:10.1016/j.jlp.2011.01.003

Cited by 43 publications

(14 citation statements)

References 20 publications

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“…True_Positive + True _Negetive Total_number_of _samples (5) To further ensure cross-validation of the result analysis, and to determine the final classification accuracy, we analyzed the results of 10 experiments using the proposed method with an optimal value of k = 5. The classification results for the 10 experiments are given in Figure 11.…”

Section: Avg_classification_accuracy =mentioning

confidence: 99%

“…To avoid such accidents, improved safety precautions and maintenance of the spherical tank is necessary [3]. Qualitative and quantitative evaluations to reduce the risks associated with spherical tanks are needed [3,5]. A reliable crack detection method for a spherical tank is comprised of the following: extraction of the features associated with the health states of the spherical tank, optimal features subset selection, and fault detection.…”

Section: Introductionmentioning

confidence: 99%

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Fault Detection of a Spherical Tank Using a Genetic Algorithm-Based Hybrid Feature Pool and k-Nearest Neighbor Algorithm

Hasan

Kim

2019

Energies

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Fault detection in metallic structures requires a detailed and discriminative feature pool creation mechanism to develop an effective condition monitoring system. Traditional fault detection methods incorporate handcrafted features either from the time, frequency or time-frequency domains. To explore the salient information provided by the acoustic emission (AE) signals, a hybrid of feature pool creation and an optimal features subset selection mechanism is proposed for crack detection in a spherical tank. The optimal hybrid feature pool creation process is composed of two major parts:(1) extraction of statistical features from time and frequency domains, as well as extraction of traditional features associated with the AE signals; and (2) genetic algorithm (GA)-based optimal features subset selection. The optimal features subset is then provided to the k-nearest neighbor (k-NN) classifier to distinguish between normal (NC) and crack conditions (CC). Experimental results show that the proposed approach yields an average 99.8% accuracy for heath state classification. To validate the effectiveness of the proposed approach, it is compared to conventional non-linear dimensionality reduction techniques, as well as those without feature selection schemes. Experimental results show that the proposed approach outperforms conventional non-linear dimensionality reduction techniques, achieving at least 2.55% higher classification accuracy.Energies 2019, 12, 991 2 of 14 acoustic emission signals. Detection of cracks in their early stages enables necessary measures to be undertaken in a timely fashion, thereby reducing accident occurrence. Acoustic emission (AE) signals are a promising nondestructive technology, capable of providing the information required for crack classification in the incipient stages. Compared to other nondestructive methods, AE is an economical and efficient alternative for recording the data associated with the health state of an object [6]. Additionally, the low energy signals found in AE signals can provide underlying information for substantial data-driven fault identification approaches [7,8]. Due to these benefits, AE signals are used to record data and develop a data-driven crack classification model for spherical tanks.Traditional data-driven fault identification methodologies rely on two important procedures: handcrafted feature extraction utilizing domain expertise; and detection of the health types using the extracted features. Signal-based health state diagnosis approaches rely primarily on the spectral analysis of the signals [9]. The choice of a signal analysis technique to extract discriminant information from the signals also has an impact in the performance of the fault classification process [10]. Therefore, in this study, the AE signals are analyzed in different domains to explore the detailed intrinsic information contained in the signals. The advantage of analyzing the signals in different domains is the acquisition of multi-domain knowledge of the signals, which would otherwise not...

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Section: Avg_classification_accuracy =mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fault Detection of a Spherical Tank Using a Genetic Algorithm-Based Hybrid Feature Pool and k-Nearest Neighbor Algorithm

Hasan

Kim

2019

Energies

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“…With the increasing use of these types of spherical tanks for different industries, the number of accidents related to leakage from the bottoms of these tanks is also increasing [2]. As a result, improved safety precautions and maintenance are required [3,4]. Moreover, the finding of the crack in its initial stages facilitates required actions to be commenced in a timely manner and reduces the incidence of industrial accidents.…”

Section: Introductionmentioning

confidence: 99%

Health State Classification of a Spherical Tank Using a Hybrid Bag of Features and K-Nearest Neighbor

Hasan

Kim

et al. 2020

Applied Sciences

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Feature analysis puts a great impact in determining the various health conditions of mechanical vessels. To achieve balance between traditional feature extraction and the automated feature selection process, a hybrid bag of features (HBoF) is designed for multiclass health state classification of spherical tanks in this paper. The proposed HBoF is composed of (a) the acoustic emission (AE) features and (b) the time and frequency based statistical features. A wrapper-based feature chooser algorithm, Boruta, is utilized to extract the most intrinsic feature set from HBoF. The selective feature matrix is passed to the multi-class k-nearest neighbor (k-NN) algorithm to differentiate among normal condition (NC) and two faulty conditions (FC1 and FC2). Experimental results demonstrate that the proposed methodology generates an average 99.7% accuracy for all working conditions. Moreover, it outperforms the existing state-of-art works by achieving at least 19.4%.

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“…e models mentioned above have been implemented by the seismic design codes and guidelines on liquid storage tanks worldwide, such as API 650 [6] and Eurocode 8 [7]. In recent years, the rapid development of computer technology and availability of powerful software allow numerical simulation become an important technique to investigate seismic behavior of liquid storage tanks in a more sophisticated manner, such as Cho and Lee [8], Virella et al [9], Livaoglu [10], Liu and Lin [11], Attati and Rofooei [12], Firouz-Abadi et al [13], Ozdemir et al [14], Korkmaz et al [15], Moslemi and Kianoush [16], Matsui and Nagaya [17], and Buratti and Tavano [18]. e shaking table test is the main tool to investigate the dynamic responses of liquid storage tanks under earthquake excitations experimentally.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Response of a 100,000 m³ Cylindrical Oil‐Storage Tank under Seismic Excitations: Experimental Tests and Numerical Simulations

Zhao

Wang

2018

Shock and Vibration

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Considering the disastrous consequences of the oil tank failure, it is of great importance to ensure the safety of the large-scale oil tank under earthquakes. This study sheds light on investigating the dynamic response of a prototype 100,000 m3 cylindrical oil-storage tank under various seismic excitations. The foundation of the tank is also considered in this study so that the obtained results are closer to the reality. Shaking table tests are conducted using a 1/20 scale liquid-tank-foundation system under various seismic excitations. The test results reveal that the dynamic responses such as accelerations and the deformation of the test specimen in the major and minor vibration directions do not differ significantly. Finite element models are constructed for the test specimen and the prototype tank and are validated through comparing the simulation results with the test data. The simulation results suggest that it might be necessary to stiffen the locations on the tank wall where the thickness of the tank wall changes because the stresses at such locations may be close or even exceed the yield strength of the structural steel under severe earthquakes.

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Seismic risk assessment of storage tanks in Turkish industrial facilities

Cited by 43 publications

References 20 publications

Fault Detection of a Spherical Tank Using a Genetic Algorithm-Based Hybrid Feature Pool and k-Nearest Neighbor Algorithm

Fault Detection of a Spherical Tank Using a Genetic Algorithm-Based Hybrid Feature Pool and k-Nearest Neighbor Algorithm

Health State Classification of a Spherical Tank Using a Hybrid Bag of Features and K-Nearest Neighbor

Dynamic Response of a 100,000 m³ Cylindrical Oil‐Storage Tank under Seismic Excitations: Experimental Tests and Numerical Simulations

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Seismic risk assessment of storage tanks in Turkish industrial facilities

Cited by 43 publications

References 20 publications

Fault Detection of a Spherical Tank Using a Genetic Algorithm-Based Hybrid Feature Pool and k-Nearest Neighbor Algorithm

Fault Detection of a Spherical Tank Using a Genetic Algorithm-Based Hybrid Feature Pool and k-Nearest Neighbor Algorithm

Health State Classification of a Spherical Tank Using a Hybrid Bag of Features and K-Nearest Neighbor

Dynamic Response of a 100,000 m3 Cylindrical Oil‐Storage Tank under Seismic Excitations: Experimental Tests and Numerical Simulations

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Dynamic Response of a 100,000 m³ Cylindrical Oil‐Storage Tank under Seismic Excitations: Experimental Tests and Numerical Simulations