• 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.

• Atmosphere
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• v.31 no.5
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• pp.511-523
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• 2021

This study explores the optimal ensemble size to improve the prediction performance of the Korea Meteorological Administration's operational climate prediction system, global seasonal forecast system version 5 (GloSea5). The GloSea5 produces an ensemble of hindcast data using the stochastic kinetic energy backscattering version2 (SKEB2) and timelagged ensemble. An experiment to increase the hindcast ensemble from 3 to 14 members for four initial dates was performed and the improvement and effect of the prediction performance considering Root Mean Square Error (RMSE), Anomaly Correlation Coefficient (ACC), ensemble spread, and Ratio of Predictable Components (RPC) were evaluated. As the ensemble size increased, the RMSE and ACC prediction performance improved and more significantly in the high variability area. In spread and RPC analysis, the prediction accuracy of the system improved as the ensemble size increased. The closer the initial date, the better the predictive performance. Results show that increasing the ensemble to an appropriate number considering the combination of initial times is efficient.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.26 no.1
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• pp.43-49
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• 2023

Purpose: The cow's milk-related-symptom-score (CoMiSS) tool was developed as an awareness tool for the assessment of cow's milk-related symptoms in infants or children. Fecal calprotectin (FC) is a noninvasive biomarker of gut inflammation that can be measured in serum and stool. This study aimed to investigate the relationship between FC levels and CoMiSS scores in infants with cow's milk protein allergy. Methods: Infants (aged 6-12 months) who were allergic to cow's milk protein were enrolled prospectively. Following completion of the CoMiSS scoring, the infants were divided into group 1 (positive CoMiSS scores ≥12) and group 2 (negative CoMiSS scores <12). FC was measured using immunoassay. Results: Of the 120 infants enrolled in this study, 60 (50.0%) had positive CoMiSS scores (group 1), while 60 (50.0%) had negative scores (group 2). The mean FC level was higher in the infants in group 1 than those in group 2 (2,934.57 ㎍/g vs. 955.13 ㎍/g; p<0.001). In addition, there was a positive correlation between FC and CoMiSS scores (R=0.168, p<0.0001). A FC level of 1,700 ㎍/g provided a sensitivity of 98.3%, specificity of 93.3%, and accuracy of 95.8% for the diagnosis of cow's milk protein allergy (CMPA). Conclusion: FC measurement may have a role in the assessing infants with CMPA.

• Geomechanics and Engineering
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• v.33 no.1
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• pp.1-9
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• 2023

The unconfined compression strength (UCS) of soils is commonly used either before or during the construction of geo-structures. In the pre-design stage, UCS as a mechanical property is obtained through a laboratory test that requires cumbersome procedures and high costs from in-situ sampling and sample preparation. As an alternative way, the empirical model established from limited testing cases is used to economically estimate the UCS. However, many parameters affecting the 1D soil compression response hinder employing the traditional statistical analysis. In this study, gene expression programming (GEP) is adopted to develop a prediction model of UCS with common affecting soil properties. A total of 79 undisturbed soil samples are collected, of which 54 samples are utilized for the generation of a predictive model and 25 samples are used to validate the proposed model. Experimental studies are conducted to measure the unconfined compression strength and basic soil index properties. A performance assessment of the prediction model is carried out using statistical checks including the correlation coefficient (R), the root mean square error (RMSE), the mean absolute error (MAE), the relatively squared error (RSE), and external criteria checks. The prediction model has achieved excellent accuracy with values of R, RMSE, MAE, and RSE of 0.98, 10.01, 7.94, and 0.03, respectively for the training data and 0.92, 19.82, 14.56, and 0.15, respectively for the testing data. From the sensitivity analysis and parametric study, the liquid limit and fine content are found to be the most sensitive parameters whereas the sand content is the least critical parameter.

• Imaging Science in Dentistry
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• v.53 no.3
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• pp.199-207
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• 2023

Purpose: The objective of this study was to evaluate the accuracy and effectiveness of an artificial intelligence (AI) program in identifying dental conditions using panoramic radiographs(PRs), as well as to assess the appropriateness of its treatment recommendations. Materials and Methods: PRs from 100 patients(representing 4497 teeth) with known clinical examination findings were randomly selected from a university database. Three dentomaxillofacial radiologists and the Diagnocat AI software evaluated these PRs. The evaluations were focused on various dental conditions and treatments, including canal filling, caries, cast post and core, dental calculus, fillings, furcation lesions, implants, lack of interproximal tooth contact, open margins, overhangs, periapical lesions, periodontal bone loss, short fillings, voids in root fillings, overfillings, pontics, root fragments, impacted teeth, artificial crowns, missing teeth, and healthy teeth. Results: The AI demonstrated almost perfect agreement (exceeding 0.81) in most of the assessments when compared to the ground truth. The sensitivity was very high (above 0.8) for the evaluation of healthy teeth, artificial crowns, dental calculus, missing teeth, fillings, lack of interproximal contact, periodontal bone loss, and implants. However, the sensitivity was low for the assessment of caries, periapical lesions, pontic voids in the root canal, and overhangs. Conclusion: Despite the limitations of this study, the synthesized data suggest that AI-based decision support systems can serve as a valuable tool in detecting dental conditions, when used with PR for clinical dental applications.