• Structural Engineering and Mechanics
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• v.77 no.1
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• pp.47-56
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• 2021

This paper deals with damage detection using a Gapped Smoothing Method (GSM) combined with deep learning. Convolutional Neural Network (CNN) is a model of deep learning. CNN has an input layer, an output layer, and a number of hidden layers that consist of convolutional layers. The input layer is a tensor with shape (number of images) × (image width) × (image height) × (image depth). An activation function is applied each time to this tensor passing through a hidden layer and the last layer is the fully connected layer. After the fully connected layer, the output layer, which is the final layer, is predicted by CNN. In this paper, a complete machine learning system is introduced. The training data was taken from a Finite Element (FE) model. The input images are the contour plots of curvature gapped smooth damage index. A free-free beam is used as a case study. In the first step, the FE model of the beam was used to generate data. The collected data were then divided into two parts, i.e. 70% for training and 30% for validation. In the second step, the proposed CNN was trained using training data and then validated using available data. Furthermore, a vibration experiment on steel damaged beam in free-free support condition was carried out in the laboratory to test the method. A total number of 15 accelerometers were set up to measure the mode shapes and calculate the curvature gapped smooth of the damaged beam. Two scenarios were introduced with different severities of the damage. The results showed that the trained CNN was successful in detecting the location as well as the severity of the damage in the experimental damaged beam.

• Archives of Plastic Surgery
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• v.49 no.3
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• pp.296-303
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• 2022

Background In recent years thread lift has become widespread; however, existing methods need to improve their long-term outcome, which requires considering topographic anatomy of face and neck, especially the ligamentous apparatus. This study aims to assess the effectiveness and safety of an innovative method of one-time three-step thread facelift, which provides an additional support to the ligamentous structures of the upper, middle, and lower thirds of the face and neck. Methods The study included 357 patients aged 32 to 67 years with various morphotypes of aging. The original method of thread lift was applied, and its effectiveness was followed up for to 2 years. The Wrinkle Severity Rating Score (WSRS) and Global Aesthetic Improvement Scale (GAIS) scores were used for assessment by investigators, independent observers, and patients. Statistical significance was determined using paired t-test and chi-square test. Results The mean WSRS score was 3.88 ± 0.88 before the thread lift, 1.93 ± 0.81 one month after the procedure, and 2.36 ± 0.85 after 2 years of follow-up. The mean GAIS was 4.80 ± 0.04 one month after thread lift, and 4.01 ± 0.04 after 2 years, while in the patients' assessment Global Satisfaction Scale was 4.86 ± 0.02 and 4.10 ± 0.02, respectively. There were no clinically significant complications throughout the observation period. Conclusion The new method of one-time three-step thread fixation of the soft tissues of the face and neck demonstrated a high degree of satisfaction by both experts and patients after 2 years of follow-up. It showed high efficacy and safety, including in the group of patients with pronounced age-related changes of the skin of face and neck.

• Smart Structures and Systems
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• v.30 no.3
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• pp.333-338
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• 2022

Planning activity is complex process assuming the term "complexity" as a group of elements interconnected each other. The common knowledge about city planning underlines its main aim as: figuring the present, imaging the future, governing every day the territory and the way people use and live it at different scales. When considering the strength of technological opportunities and the spreading of ICT and IoT devices within everyday life, that mean within the life of cities, the complex nature of the urban system increases with the intensification of information and their connections. Recent orientations about urban and regional planning try to carry the discipline to a more flexible approach in respect to the hyperdeterminant role of direct technical applications. This passage is a fundamental aspect considering the faster and faster modifications of social and economic assets at the global and local scale. At the same time, the "environment question" became more and more relevant at the worldwide scale within the 2015 UN 2030 Agenda for Sustainable Development. Another relevant aspect about the recent urban planning orientations regards the role of the different subjects that are part of the planning process. Approaching the government of smart cities means to define how every subject, with different roles (public or private), could enrich the knowledge of the functioning of the "urban machine" and the awareness of participation of people and city users in the quality of urban life. In the paper author starts defining recent approaches in urban planning, then the nature of the city as a complex system is analyzed from the point of view of planners and of the different subjects that act in the city. Then the smart city is introduced as a further level of complexity and finally author propose the basic element of a Planning Support System.

• Wind and Structures
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• v.33 no.6
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• pp.463-470
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• 2021

This study investigates the aerodynamic characteristics of NACA series airfoil by altering the trailing edge in the form of extended and serrated sections. This contemporary advent examined NACA 0020 airfoil experimentally at the angle of attack ranging from 0° to 45° and for the Reynolds number of 2.46 × 105. To figure out the flow behaviour, the standard average pressure distribution over the airfoil surface is estimated with 50 pressure taps. The time series surface pressure is recorded for 700 Hz of sampling frequency. The extended trailing edge of 0.1 c, 0.2 c and 0.3 c are attached to the base airfoil. Further, the triangular serration is introduced with the base length of 2 cm, 4 cm and 6 cm. Each base length with three different amplitudes of 0.1 c, 0.2 c and 0.3 c were designed and equipped with the baseline case at the trailing edge and tested. The aerodynamic force coefficient, as well as pressure coefficient are presented. The obtained data advises that modification in the trailing edge will reflect the aerodynamic characteristics and the flow behaviour over the section of a wing. Resultantly, the extended trailing edge as a thin elongated surface attached to a base airfoil without revising the main airfoil favors good lift increment. The serrated trailing edge acts as a flow control device by altering the flow pattern results to delay the stall phenomenon. Besides it, improves lift co-efficient with less amount of additional drag. This extended and serrated trailing edge approach can support for designing the future smart airfoil.

• The Journal of the Korea Contents Association
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• v.21 no.11
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• pp.268-280
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• 2021

This study analysed the narrative strategies in TV drama utilized in order to support the recent feminism movements. The analysis revealed that this TV drama breaks away from the clichéd patriarchal drama series. It portrays the main characters are not the sons but the two daughters-in-law, and represents the women challenging the order of the patriarchy, and resolving the issues. In this drama, men's power was removed and female agents were held up to ridicule. In addition, it eradicates the traditional female conflict structures and creates a strong bond between the females. With this storyline, TV series concludes with two achievements. One, the stepmother and the mother co-parent the child instead of the father, suggests that a non-blood related matriarchal family is possible. Two, the heir to the chaebol family, which is traditionally a patrilineal structure, is not the oldest son or the immoral son, but the lesbian daughter-in-law, overturning the idea of heteronormativity that is dominant in the patriarchal system.

• Advances in concrete construction
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• v.13 no.4
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• pp.315-325
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• 2022

Reinforced concrete structures are exposed throughout their lifetime to the phenomenon of carbonation, which considerably influences their durability by causing corrosion of the reinforcements. The fight against this phenomenon is usually ensured by anti-carbonation coatings which have the possibility of limiting the permeability to carbon dioxide or with coatings which absorb the CO₂ present in the air. A coating with good crack-bridging (sealing) capacity will prevent water from entering through existing cracks in concrete. Despite the beneficial effect of these coatings, their durability decreases considerably over time with temperature and humidity. In order to use coatings made from local materials, not presenting any danger, available in abundance in our country, very economical and easy to operate is the main objective of this work. This paper aim is to contribute to the formulation of a corrected dune sand-based mortar as an anti-carbonation coating for concrete. The results obtained show that the cement mortar based on dune sand formulated has a very satisfactory compressive strength, a very low water porosity compared to ordinary cement mortar and that this mortar allows an improvement in the protection of the concrete against the carbonation of 60% compared to ordinary cement mortar based on alluvial sand. Moreover, the formulated cement mortars based on dune sand have good adhesion to the concrete support, their adhesion strengths are greater than 1.5MPa recommended by the standards.

• Smart Structures and Systems
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• v.29 no.1
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• pp.251-266
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• 2022

Structural Health Monitoring (SHM) of critical infrastructure comprises a major pillar of maintenance management, shielding public safety and economic sustainability. Although SHM is usually associated with data-driven metrics and thresholds, expert judgement is essential, especially in cases where erroneous predictions can bear casualties or substantial economic loss. Considering that visual inspections are time consuming and potentially subjective, artificial-intelligence tools may be leveraged in order to minimize the inspection effort and provide objective outcomes. In this context, timely detection of sensor malfunctioning is crucial in preventing inaccurate assessment and false alarms. The present work introduces a sensor-fault detection and interpretation framework, based on the well-established support-vector machine scheme for anomaly detection, combined with a coalitional game-theory approach. The proposed framework is implemented in two datasets, provided along the 1st International Project Competition for Structural Health Monitoring (IPC-SHM 2020), comprising acceleration and cable-load measurements from two real cable-stayed bridges. The results demonstrate good predictive performance and highlight the potential for seamless adaption of the algorithm to intrinsically different data domains. For the first time, the term "decision trajectories", originating from the field of cognitive sciences, is introduced and applied in the context of SHM. This provides an intuitive and comprehensive illustration of the impact of individual features, along with an elaboration on feature dependencies that drive individual model predictions. Overall, the proposed framework provides an easy-to-train, application-agnostic and interpretable anomaly detector, which can be integrated into the preprocessing part of various SHM and condition-monitoring applications, offering a first screening of the sensor health prior to further analysis.

• Smart Structures and Systems
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• v.29 no.1
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• pp.93-103
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• 2022

With the wider availability of sensor technology through easily affordable sensor devices, several Structural Health Monitoring (SHM) systems are deployed to monitor vital civil infrastructure. The continuous monitoring provides valuable information about the health of the structure that can help provide a decision support system for retrofits and other structural modifications. However, when the sensors are exposed to harsh environmental conditions, the data measured by the SHM systems tend to be affected by multiple anomalies caused by faulty or broken sensors. Given a deluge of high-dimensional data collected continuously over time, research into using machine learning methods to detect anomalies are a topic of great interest to the SHM community. This paper contributes to this effort by proposing a relatively new time series representation named "Shapelet Transform" in combination with a Random Forest classifier to autonomously identify anomalies in SHM data. The shapelet transform is a unique time series representation based solely on the shape of the time series data. Considering the individual characteristics unique to every anomaly, the application of this transform yields a new shape-based feature representation that can be combined with any standard machine learning algorithm to detect anomalous data with no manual intervention. For the present study, the anomaly detection framework consists of three steps: identifying unique shapes from anomalous data, using these shapes to transform the SHM data into a local-shape space and training machine learning algorithms on this transformed data to identify anomalies. The efficacy of this method is demonstrated by the identification of anomalies in acceleration data from an SHM system installed on a long-span bridge in China. The results show that multiple data anomalies in SHM data can be automatically detected with high accuracy using the proposed method.

• Smart Structures and Systems
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• v.29 no.1
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• pp.17-28
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• 2022

The cable component of cable-stayed bridges is gradually impacted by weather conditions, vehicle loads, and material corrosion. The stayed cable is a critical load-carrying part that closely affects the operational stability of a cable-stayed bridge. Damaged cables might lead to the bridge collapse due to their tension capacity reduction. Thus, it is necessary to develop structural health monitoring (SHM) techniques that accurately identify damaged cables. In this work, a combinational identification method of three efficient techniques, including statistical analysis, clustering, and neural network models, is proposed to detect the damaged cable in a cable-stayed bridge. The measured dataset from the bridge was initially preprocessed to remove the outlier channels. Then, the theory and application of each technique for damage detection were introduced. In general, the statistical approach extracts the parameters representing the damage within time series, and the clustering approach identifies the outliers from the data signals as damaged members, while the deep learning approach uses the nonlinear data dependencies in SHM for the training model. The performance of these approaches in classifying the damaged cable was assessed, and the combinational identification method was obtained using the voting ensemble. Finally, the combination method was compared with an existing outlier detection algorithm, support vector machines (SVM). The results demonstrate that the proposed method is robust and provides higher accuracy for the damaged cable detection in the cable-stayed bridge.

• Journal of People, Plants, and Environment
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• v.23 no.5
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• pp.565-576
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• 2020

Background and objective: The elderly living in homeless living facilities for a long time suffer from various mental health problems. This study aims to determine the psychological, emotional, and social effects of a horticultural therapy program composed of gardening activities, which was designed based on the semantic structures of life for the homeless elderly living in the facilities for a long time. Methods: A total of 12 subjects (6 in the control group and 6 in the experimental group) participated in the study. The horticultural therapy program consisted mainly of gardening activities, and a total of 16 sessions were conducted once a week for 16 weeks, 60-90 minutes per session. The subjects were tested to evaluate their self-esteem, depression, and horticultural activities. The data were analyzed using the Mann-Whitney U test, Wilcoxon rank test, and Friedman test, which were nonparametric tests, conducted at a 95% significance level. Results: First, in the case of self-esteem, a significant difference was found between the groups, 20.00 points (SD = 5.69) in the control group, and 25.50 points (SD = 3.73) in the experimental group (p = .034). Second, in the case of depression, no statistically significant difference was found in the posttest. Finally, in the case of the horticultural activity evaluation, the scores of most variables gradually and significantly increased during the program [Verbal interaction during activity (p = .006), Self-concept and identity (p = .006), Need-drive adaptation (p < .001), Interpersonal and social relations (p < .001)]. Conclusion: These results support that the horticultural therapy program could help the elderly improve psychological relaxation, emotional stability, and social relationships. In order to generalize the results, it is suggested to increase the number of subjects or conduct additional repetitive experiments in further research.