• Structural Engineering and Mechanics
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• v.83 no.3
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• pp.327-340
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• 2022

The Collapse Margin Ratio (CMR) is a notable index used for seismic assessment of the structures. As proposed by FEMA P695, a set of analyses including the Nonlinear Static Analysis (NSA), Incremental Dynamic Analysis (IDA), together with Fragility Analysis, which are typically time-taking and computationally unaffordable, need to be conducted, so that the CMR could be obtained. To address this issue and to achieve a quick and efficient method to estimate the CMR, the Artificial Neural Network (ANN), Response Surface Method (RSM), and Adaptive Neuro-Fuzzy Inference System (ANFIS) will be introduced in the current research. Accordingly, using the NSA results, an attempt was made to find a fast and efficient approach to derive the CMR. To this end, 5016 IDA analyses based on FEMA P695 methodology on 114 various Reinforced Concrete (RC) frames with 1 to 12 stories have been carried out. In this respect, five parameters have been used as the independent and desired inputs of the systems. On the other hand, the CMR is regarded as the output of the systems. Accordingly, a double hidden layer neural network with Levenberg-Marquardt training and learning algorithm was taken into account. Moreover, in the RSM approach, the quadratic system incorporating 20 parameters was implemented. Correspondingly, the Analysis of Variance (ANOVA) has been employed to discuss the results taken from the developed model. Additionally, the essential parameters and interactions are extracted, and input parameters are sorted according to their importance. Moreover, the ANFIS using Takagi-Sugeno fuzzy system was employed. Finally, all methods were compared, and the effective parameters and associated relationships were extracted. In contrast to the other approaches, the ANFIS provided the best efficiency and high accuracy with the minimum desired errors. Comparatively, it was obtained that the ANN method is more effective than the RSM and has a higher regression coefficient and lower statistical errors.

• Korean Journal of Audiology
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• v.25 no.4
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• pp.178-188
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• 2021

Background and Objectives: The influence of musical aptitude on cognitive test performance in musicians is a long-debated research question. Evidence points to the low performance of nonmusicians in visual and auditory cognitive tasks (working memory and attention) compared with musicians. This cannot be generalized to all nonmusicians, as a sub-group in this population can have innate musical abilities even without any formal musical training. The present study aimed to study the effect of musical aptitude on the working memory and selective attention. Subjects and Methods: Three groups of 20 individuals each (a total of 60 participants), including trained-musicians, nonmusicians with good musical aptitude, and nonmusicians with low musical aptitude, participated in the present study. Cognitive-based visual (Flanker's selective attention test) and auditory (working memory tests: backward digit span and operation span) tests were administered. Results: MANOVA (followed by ANOVA) revealed a benefit of musicianship and musical aptitude on backward digit span and Flanker's reaction time (p<0.05). Discriminant function analyses showed that the groups could be effectively (accuracy, 80%) segregated based on the backward digit span and Flanker's selective attention test. Trained musicians and nonmusicians with good musical aptitude were distinguished as one cluster and nonmusicians with low musical aptitude formed another cluster, hinting the role of musical aptitude in working memory and selective attention. Conclusions: Nonmusicians with good musical aptitude can have enhanced working memory and selective attention skills like musicians. Hence, caution is required when these individuals are included as controls in cognitive-based visual and auditory experiments.

• Nuclear Engineering and Technology
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• v.52 no.12
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• pp.2789-2802
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• 2020

Small Modular Reactors (SMRs) are attracting wide attention due to their outstanding performance, extensive studies have been carried out for lead-based fast reactors (LFRs) that cooled with Lead or Lead-bismuth (LBE), and small modular natural circulation LFR is one of the promising candidates for SMRs and LFRs development. One of the challenges for the design small modular natural circulation LFR is to master the natural circulation thermal-hydraulic performance in the reactor primary circuit, while the natural circulation characteristics is a coupled thermal-hydraulic problem of the core thermal power, the primary loop layout and the operating state of secondary cooling system etc. Thus, accurate predicting the natural circulation LFRs thermal-hydraulic features are highly required for conducting reactor operating condition evaluate and Thermal hydraulic design optimization. In this study, a thermal-hydraulic analysis code is developed for small modular natural circulation LFRs, which is based on several mathematical models for natural circulation originally. A small modular natural circulation LBE cooled fast reactor named URANUS developed by Korea is chosen to assess the code's capability. Comparisons are performed to demonstrate the accuracy of the code by the calculation results of MARS, and the key thermal-hydraulic parameters agree fairly well with the MARS ones. As a typical application case, steady-state analyses were conducted to have an assessment of thermal-hydraulic behavior under nominal condition, and several parameters affecting natural circulation were evaluated. What's more, two characteristics parameters that used to analyze natural circulation LFRs natural circulation capacity were established. The analyses show that the core thermal power, thermal center difference and flow resistance is the main factors affecting the reactor natural circulation. Improving the core thermal power, increasing the thermal center difference and decreasing the flow resistance can significantly increase the reactor mass flow rate. Characteristics parameters can be used to quickly evaluate the natural circulation capacity of natural circulation LFR under normal operating conditions.

• Smart Structures and Systems
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• v.30 no.1
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• pp.75-88
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• 2022

Engineering structures in operation essentially belong to time-varying or nonlinear structures and the resultant response signals are usually non-stationary. For such time-varying structures, it is of great importance to extract time-dependent dynamic parameters from non-stationary response signals, which benefits structural health monitoring, safety assessment and vibration control. However, various traditional signal processing methods are unable to extract the embedded meaningful information. As a newly developed technique, variational mode decomposition (VMD) shows its superiority on signal decomposition, however, it still suffers two main problems. The foremost problem is that the number of modal components is required to be defined in advance. Another problem needs to be addressed is that VMD cannot effectively separate non-stationary signals composed of closely spaced or overlapped modes. As such, a new method named generalized adaptive variational modal decomposition (GAVMD) is proposed. In this new method, the number of component signals is adaptively estimated by an index of mean frequency, while the generalized demodulation algorithm is introduced to yield a generalized VMD that can decompose mode overlapped signals successfully. After that, synchrosqueezing wavelet transform (SWT) is applied to extract instantaneous frequencies (IFs) of the decomposed mono-component signals. To verify the validity and accuracy of the proposed method, three numerical examples and a steel cable with time-varying tension force are investigated. The results demonstrate that the proposed GAVMD method can decompose the multi-component signal with overlapped modes well and its combination with SWT enables a successful IF extraction of each individual component.

• Steel and Composite Structures
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• v.43 no.6
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• pp.785-796
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• 2022

Concrete-encased steel (CES) beam, in which structural steel is encased in a reinforced concrete (RC) section, is widely applied in high-rise buildings as transfer beams due to its high load-carrying capacity, great stiffness, and good durability. However, these CES beams are prone to shear failure because of the low shear span-to-depth ratio and the heavy load. Due to the high load-carrying capacity and the brittle failure process of the shear failure, the accurate strength prediction of CES beams significantly influences the assessment of structural safety. In current design codes, design formulas for predicting the shear strength of CES beams are based on the so-called "superposition method". This method indicates that the shear strength of CES beams can be obtained by superposing the shear strengths of the RC part and the steel shape. Nevertheless, in some cases, this method yields errors on the unsafe side because the shear strengths of these two parts cannot be achieved simultaneously. This paper clarifies the conditions at which the superposition method does not hold true, and the shear strength of CES beams is investigated using a compatible truss-arch model. Considering the deformation compatibility between the steel shape and the RC part, the method to obtain the shear strength of CES beams is proposed. Finally, the proposed model is compared with other calculation methods from codes AISC 360 (USA, North America), Eurocode 4 (Europe), YB 9082 (China, Asia), JGJ 138 (China, Asia), and AS/NZS 2327 (Australia/New Zealand, Oceania) using the available test data consisting of 45 CES beams. The results indicate that the proposed model can predict the shear strength of CES beams with sufficient accuracy and safety. Without considering the deformation compatibility, the calculation methods from the codes AISC 360, Eurocode 4, YB 9082, JGJ 138, and AS/NZS 2327 lead to excessively conservative or unsafe predictions.

• Smart Structures and Systems
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• v.30 no.6
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• pp.613-626
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• 2022

Guaranteeing the quality and integrity of structural health monitoring (SHM) data is very important for an effective assessment of structural condition. However, sensory system may malfunction due to sensor fault or harsh operational environment, resulting in multiple types of data anomaly existing in the measured data. Efficiently and automatically identifying anomalies from the vast amounts of measured data is significant for assessing the structural conditions and early warning for structural failure in SHM. The major challenges of current automated data anomaly detection methods are the imbalance of dataset categories. In terms of the feature of actual anomalous data, this paper proposes a data anomaly detection method based on data-level and deep learning technique for SHM of civil engineering structures. The proposed method consists of a data balancing phase to prepare a comprehensive training dataset based on data-level technique, and an anomaly detection phase based on a sophisticatedly designed network. The advanced densely connected convolutional network (DenseNet) and Transformer encoder are embedded in the specific network to facilitate extraction of both detail and global features of response data, and to establish the mapping between the highest level of abstractive features and data anomaly class. Numerical studies on a steel frame model are conducted to evaluate the performance and noise immunity of using the proposed network for data anomaly detection. The applicability of the proposed method for data anomaly classification is validated with the measured data of a practical supertall structure. The proposed method presents a remarkable performance on data anomaly detection, which reaches a 95.7% overall accuracy with practical engineering structural monitoring data, which demonstrates the effectiveness of data balancing and the robust classification capability of the proposed network.

• Smart Structures and Systems
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• v.29 no.1
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• pp.181-193
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• 2022

Data-driven structural health monitoring (SHM) of civil infrastructure can be used to continuously assess the state of a structure, allowing preemptive safety measures to be carried out. Long-term monitoring of large-scale civil infrastructure often involves data-collection using a network of numerous sensors of various types. Malfunctioning sensors in the network are common, which can disrupt the condition assessment and even lead to false-negative indications of damage. The overwhelming size of the data collected renders manual approaches to ensure data quality intractable. The task of detecting and classifying an anomaly in the raw data is non-trivial. We propose an approach to automate this task, improving upon the previously developed technique of image-based pre-processing on one-dimensional (1D) data by enriching the features of the neural network input data with multiple channels. In particular, feature engineering is employed to convert the measured time histories into a 3-channel image comprised of (i) the time history, (ii) the spectrogram, and (iii) the probability density function representation of the signal. To demonstrate this approach, a CNN model is designed and trained on a dataset consisting of acceleration records of sensors installed on a long-span bridge, with the goal of fault detection and classification. The effect of imbalance in anomaly patterns observed is studied to better account for unseen test cases. The proposed framework achieves high overall accuracy and recall even when tested on an unseen dataset that is much larger than the samples used for training, offering a viable solution for implementation on full-scale structures where limited labeled-training data is available.

• Steel and Composite Structures
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• v.41 no.5
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• pp.761-773
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• 2021

This study presents a 3D non-linear finite element (FE) assessment of dynamic soil-structure interaction (SSI). The numerical investigation has been performed on the time domain through a Finite Element (FE) system, while considering the nonlinear behavior of soil and the multi-directional nature of genuine seismic events. Later, the FE outcomes are analyzed to the recorded in-situ free-field and structural movements, emphasizing the numerical model's great result in duplicating the observed response. In this work, the soil response is simulated using an isotropic hardening elastic-plastic hysteretic model utilizing HSsmall. It is feasible to define the non-linear cycle response from small to large strain amplitudes through this model as well as for the shift in beginning stiffness with depth that happens during cyclic loading. One of the most difficult and unexpected tasks in resolving soil-structure interaction concerns is picking an appropriate ground motion predicted across an earthquake or assessing the geometrical abnormalities in the soil waves. Furthermore, an artificial neural network (ANN) has been utilized to properly forecast the non-linear behavior of soil and its multi-directional character, which demonstrated the accuracy of the ANN based on the RMSE and R² values. The total result of this research demonstrates that complicated dynamic soil-structure interaction processes may be addressed directly by passing the significant simplifications of well-established substructure techniques.

• Journal of Audiology & Otology
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• v.25 no.4
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• pp.178-188
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• 2021

Background and Objectives: The influence of musical aptitude on cognitive test performance in musicians is a long-debated research question. Evidence points to the low performance of nonmusicians in visual and auditory cognitive tasks (working memory and attention) compared with musicians. This cannot be generalized to all nonmusicians, as a sub-group in this population can have innate musical abilities even without any formal musical training. The present study aimed to study the effect of musical aptitude on the working memory and selective attention. Subjects and Methods: Three groups of 20 individuals each (a total of 60 participants), including trained-musicians, nonmusicians with good musical aptitude, and nonmusicians with low musical aptitude, participated in the present study. Cognitive-based visual (Flanker's selective attention test) and auditory (working memory tests: backward digit span and operation span) tests were administered. Results: MANOVA (followed by ANOVA) revealed a benefit of musicianship and musical aptitude on backward digit span and Flanker's reaction time (p<0.05). Discriminant function analyses showed that the groups could be effectively (accuracy, 80%) segregated based on the backward digit span and Flanker's selective attention test. Trained musicians and nonmusicians with good musical aptitude were distinguished as one cluster and nonmusicians with low musical aptitude formed another cluster, hinting the role of musical aptitude in working memory and selective attention. Conclusions: Nonmusicians with good musical aptitude can have enhanced working memory and selective attention skills like musicians. Hence, caution is required when these individuals are included as controls in cognitive-based visual and auditory experiments.

• Journal of the Korean Society of Systems Engineering
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• v.17 no.2
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• pp.9-22
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• 2021

A study on the sustainability evaluation of the requirements of the mobile industry closely related to the human factor was needed. We conducted the sustainability evaluation of the mobile industry using a quantitative evaluation method with the AHP tool and a qualitative evaluation method reflecting the technological age flow of the philosophical thoughts to improve the reliability of the evaluation results of the model verification. And this evaluation is to evaluate the consistency of the connection between the national sustainability and the mobile industry. In order to draw the conclusion of the relevance, the team member Delphi 5-point scale was used. As a result, the priority was confirmed in the national sustainable development indicator and mobile industry indicator. Quantitative and qualitative evaluation was applied to the discontinued model of mobile to derive insufficient indicators, and it was confirmed that if the indicators were improved and reflected, it would be sustainable. And in order to secure reliability and accuracy, we made a proposal to apply the systems engineering process to the development of the model evaluation field. The systems engineering technology process meets the needs and requirements of stakeholders throughout the lifecycle and it is suitable for the development model of the industry's sustainability assessment.