Damage Detection in Structural Health Monitoring using Hybrid Convolution Neural Network and Recurrent Neural Network

The process of damage identification in Structural Health Monitoring (SHM) gives us a lot of practical information about the current status of the inspected structure. The target of the process is to detect damage status by processing data collected from sensors, followed by identifying the difference between the damaged and the undamaged states. Different machine learning techniques have been applied to attempt to extract features or knowledge from vibration data, however, they need to learn prior knowledge about the factors affecting the structure. In this paper, a novel method of structural damage detection is proposed using convolution neural network and recurrent neural network. A convolution neural network is used to extract deep features while recurrent neural network is trained to learn the long-term historical dependency in time series data. This method with combining two types of features increases discrimination ability when compares with it to deep features only. Finally, the neural network is applied to categorize the time series into two states - undamaged and damaged. The accuracy of the proposed method was tested on a benchmark dataset of Z24-bridge (Switzerland). The result shows that the hybrid method provides a high level of accuracy in damage identification of the tested structure

ATMOSPHERIC PARTICLES IN HOCHIMINH SIZE DISTRIBUTION OF WATER-SOLUBLE INORGANIC IONS

Joint Research on Environmental Science and Technology for the Eart

Multi-level damage detection using a combination of deep neural networks

In recent years, bridge damage identification using a convolutional neural network (CNN) has become a hot research topic and received much attention in the field of civil engineering. Although CNN is capable of categorizing damaged and undamaged states from the measured data, the level of accuracy for damage diagnosis is still insufficient due to the tendency of CNN to ignore the temporal dependency between data points. To address this problem, this paper introduces a novel hybrid damage detection method based on the combination of CNN and Long Short-Term Memory (LSTM) to classify and quantify different levels of damage in the bridge structure. In this method, the CNN model will be used to extract the spatial damage features, which will be combined with the temporal features obtained from Long Short-Term Memory (LSTM) model to create the enhanced damage features. The combination successfully strengthened the damage detection capability of the neural network. Moreover, deep learning is also improved in this paper to process the acceleration-time data, which has a different amplitude at short intervals and the same amplitude at long intervals. The empirical result on the Vang bridge shows that our hybrid CNN-LSTM can detect structural damage with a high level of accuracy

Consumer ethnocentrism, cosmopolitanism, product judgment, and foreign product purchase intention: An empirical study in Vietnam

This study aims to determine the relationship between consumer ethnocentrism, consumer cosmopolitanism, imported product judgment, and foreign product purchase intention in Vietnam. This paper tries to present its results empirically, which might be helpful in preparing a strategy for Vietnamese customers’ international purchasing behavior to increase competition at retail companies in Vietnam. It uses a questionnaire with a purposive random sampling of 311 customers in Vietnam. Analysis was conducted through a quantitative descriptive analysis, measurement of variable dimensions on the questionnaire using a seven-point Likert scale, and partial least squares structural equation modeling (PLS-SEM) to test the hypotheses. This study found that imported product judgment, consumer cosmopolitanism, social influence, and perceived behavioral control positively influence foreign product purchase intention, whereas customer ethnocentrism has a negative impact on that intention. The association between consumer cosmopolitanism and foreign product purchase intention is mediated by imported product evaluation and consumer ethnocentrism. At the same time, national identity does not affect consumer ethnocentrism and foreign product purchase intentions. Besides, this study offers some managerial implications for marketers in decisions linked to Vietnamese customers’ international purchasing behavior to increase competition in the domestic market. AcknowledgmentThe authors express a sincere gratitude to all the participants who generously took part in this research study

AIR POLLUTION REDUCE PRODUCTION AND LEAVE AREA OF RED RADISH (Rapahanus sativus cv. Red Chime) AND CHINESE VEGETABLE (Brassica campestris var. rosularis cv. ATU171)

Joint Research on Environmental Science and Technology for the Eart

Effects of Internal Factors on Financial Performance of Listed Construction-Material Companies: The Case of Vietnam

The research aims to assess internal factors affecting to financial performance so that feasible suggestions could be provided for construction-material firms which are currently listing in the stock market of Vietnam. The authors applied regression for panel data which collected from 30 listed construction-material firms. The findings show that firms’ financial performance are positively affected by the firms’ size, capital structure, capitalization expenditure, and accounts receivable management. The research results also reflect positive relation between firms’ financial performance and business risk. Upon the research results we recommend that the changes should be focus on improving policies about capitalization expenditure; reasonable capital structure and management of inventories. Keywords: financial performance, construction-material, listed firms, Vietna

Model Updating for Large-Scale Railway Bridge Using Grey Wolf Algorithm and Genetic Alghorithms

This paper proposes a novel hybrid algorithm to deal with an inverse problem of a large-scale truss bridge. Grey Wolf Optimization (GWO) Algorithm is a well-known and widely applied metaheuristic algorithm. Nevertheless, GWO has two major drawbacks. First, this algorithm depends crucially on the positions of the leading Wolf. If the position of the leaderis far from the best solution, the obtained results are poor. On the other hand, GWO does not own capacities to improve the quality of new generations if elements are trapped into local minima. To remedy the shortcomings of GWO, we propose a hybrid algorithm combining GWO with Genetic Algorithm (GA), termed HGWO-GA. This proposed method contains two key features (1) based on crossover and mutation capacities, GA is first utilized to generate the high-quality elements (2) after that, the optimization capacity of GWO is employed to seek the optimal solutions. To assess the effectiveness of the proposed approach, a large-scale truss bridge is employed for model updating. The obtained results show that HGWO-GA not only provides a good agreement between numerical and experimental results but also outperforms traditional GWO in terms of accuracy

Model Updating for Large-Scale Railway Bridge Using Grey Wolf Algorithm and Genetic Alghorithms

This paper proposes a novel hybrid algorithm to deal with an inverse problem of a large-scale truss bridge. Grey Wolf Optimization (GWO) Algorithm is a well-known and widely applied metaheuristic algorithm. Nevertheless, GWO has two major drawbacks. First, this algorithm depends crucially on the positions of the leading Wolf. If the position of the leaderis far from the best solution, the obtained results are poor. On the other hand, GWO does not own capacities to improve the quality of new generations if elements are trapped into local minima. To remedy the shortcomings of GWO, we propose a hybrid algorithm combining GWO with Genetic Algorithm (GA), termed HGWO-GA. This proposed method contains two key features (1) based on crossover and mutation capacities, GA is first utilized to generate the high-quality elements (2) after that, the optimization capacity of GWO is employed to seek the optimal solutions. To assess the effectiveness of the proposed approach, a large-scale truss bridge is employed for model updating. The obtained results show that HGWO-GA not only provides a good agreement between numerical and experimental results but also outperforms traditional GWO in terms of accuracy

Determinants Influencing Quality of Finance and Accounting Education: The Case Study of Vietnam

The quality of undergraduate program in general and in the discipline of finance and accounting in particular is one of the big concerned issues in society. For a long time, Vietnam has trained a large number of bachelor students which exceeds the real needs, especially in the field of economics. In the dimension of this study, we investigate the impact levels of determinants on quality of financial and accounting education in the context of the 4th Industrial Revolution. Data were collected by receiving questionnaire feedbacks from students of Trade Union University of Vietnam. By employing the tests of Cronbach’s Alpha, exploratory factor analysis and multivariate regression, the results show that four determinants including (i) role of trainers, (ii) innovation of the training program, (iii) applying information technology; (iv) and social behavior skills of students influence positively the quality of education in the field of finance and accounting. Keywords: Education quality, finance and accounting, Vietna

ATMOSPHERIC PARTICLES IN HANOI-CONCENTRATIONS OF WATER-SOLUBLE INORGANIC IONS AND SOURCE REGIONS

Joint Research on Environmental Science and Technology for the Eart