• Title/Summary/Keyword: Feature modeling

Search Result 639, Processing Time 0.026 seconds

A novel coupled finite element method for hydroelastic analysis of FG-CNTRC floating plates under moving loads

  • Nguyen, Vu X.;Lieu, Qui X.;Le, Tuan A.;Nguyen, Thao D.;Suzuki, Takayuki;Luong, Van Hai
    • Steel and Composite Structures
    • /
    • v.42 no.2
    • /
    • pp.243-256
    • /
    • 2022
  • A coupled finite element method (FEM)-boundary element method (BEM) for analyzing the hydroelastic response of functionally graded carbon nanotube-reinforced composite (FG-CNTRC) floating plates under moving loads is firstly introduced in this article. For that aim, the plate displacement field is described utilizing a generalized shear deformation theory (GSDT)-based FEM, meanwhile the linear water-wave theory (LWWT)-relied BEM is employed for the fluid hydrodynamic modeling. Both computational domains of the plate and fluid are coincidentally discretized into 4-node Hermite elements. Accordingly, the C1-continuous plate element model can be simply captured owing to the inherent feature of third-order Hermite polynomials. In addition, this model is also completely free from shear correction factors, although the shear deformation effects are still taken into account. While the fluid BEM can easily handle the free surface with a lower computational effort due to its boundary integral performance. Material properties through the plate thickness follow four specific CNT distributions. Outcomes gained by the present FEM-BEM are compared with those of previously released papers including analytical solutions and experimental data to validate its reliability. In addition, the influences of CNT volume fraction, different CNT configurations, water depth, and load speed on the hydroelastic behavior of FG-CNTRC plates are also examined.

Cable damage identification of cable-stayed bridge using multi-layer perceptron and graph neural network

  • Pham, Van-Thanh;Jang, Yun;Park, Jong-Woong;Kim, Dong-Joo;Kim, Seung-Eock
    • Steel and Composite Structures
    • /
    • v.44 no.2
    • /
    • pp.241-254
    • /
    • 2022
  • The cables in a cable-stayed bridge are critical load-carrying parts. The potential damage to cables should be identified early to prevent disasters. In this study, an efficient deep learning model is proposed for the damage identification of cables using both a multi-layer perceptron (MLP) and a graph neural network (GNN). Datasets are first generated using the practical advanced analysis program (PAAP), which is a robust program for modeling and analyzing bridge structures with low computational costs. The model based on the MLP and GNN can capture complex nonlinear correlations between the vibration characteristics in the input data and the cable system damage in the output data. Multiple hidden layers with an activation function are used in the MLP to expand the original input vector of the limited measurement data to obtain a complete output data vector that preserves sufficient information for constructing the graph in the GNN. Using the gated recurrent unit and set2set model, the GNN maps the formed graph feature to the output cable damage through several updating times and provides the damage results to both the classification and regression outputs. The model is fine-tuned with the original input data using Adam optimization for the final objective function. A case study of an actual cable-stayed bridge was considered to evaluate the model performance. The results demonstrate that the proposed model provides high accuracy (over 90%) in classification and satisfactory correlation coefficients (over 0.98) in regression and is a robust approach to obtain effective identification results with a limited quantity of input data.

A computational investigation on flexural response of laminated composite plates using a simple quasi-3D HSDT

  • Draiche, Kada;Selim, Mahmoud M.;Bousahla, Abdelmoumen Anis;Tounsi, Abdelouahed;Bourada, Fouad;Tounsi, Abdeldjebbar;Mahmoud, S.R.
    • Steel and Composite Structures
    • /
    • v.41 no.5
    • /
    • pp.697-711
    • /
    • 2021
  • In this work, a simple quasi 3-D parabolic shear deformation theory is developed to examine the bending response of antisymmetric cross-ply laminated composite plates under different types of mechanical loading. The main feature of this theory is that, in addition to including the transverse shear deformation and thickness stretching effects, it has only five-unknown variables in the displacement field modeling like Mindlin's theory (FSDT), yet satisfies the zero shear stress conditions on the top and bottom surfaces of the plate without requiring a shear correction factor. The static version of principle of virtual work was employed to derive the governing equations, while the bending problem for simply supported antisymmetric cross-ply laminated plates was solved by a Navier-type closed-form solution procedure. The adequacy of the proposed model is handled by considering the impact of side-to-thickness ratio on bending response of plate through several illustrative examples. Comparison of the obtained numerical results with the other shear deformation theories leads to the conclusion that the present model is more accurate and efficient in predicting the displacements and stresses of laminated composite plates.

Student Group Division Algorithm based on Multi-view Attribute Heterogeneous Information Network

  • Jia, Xibin;Lu, Zijia;Mi, Qing;An, Zhefeng;Li, Xiaoyong;Hong, Min
    • KSII Transactions on Internet and Information Systems (TIIS)
    • /
    • v.16 no.12
    • /
    • pp.3836-3854
    • /
    • 2022
  • The student group division is benefit for universities to do the student management based on the group profile. With the widespread use of student smart cards on campus, especially where students living in campus residence halls, students' daily activities on campus are recorded with information such as smart card swiping time and location. Therefore, it is feasible to depict the students with the daily activity data and accordingly group students based on objective measuring from their campus behavior with some regular student attributions collected in the management system. However, it is challenge in feature representation due to diverse forms of the student data. To effectively and comprehensively represent students' behaviors for further student group division, we proposed to adopt activity data from student smart cards and student attributes as input data with taking account of activity and attribution relationship types from different perspective. Specially, we propose a novel student group division method based on a multi-view student attribute heterogeneous information network (MSA-HIN). The network nodes in our proposed MSA-HIN represent students with their multi-dimensional attribute information. Meanwhile, the edges are constructed to characterize student different relationships, such as co-major, co-occurrence, and co-borrowing books. Based on the MSA-HIN, embedded representations of students are learned and a deep graph cluster algorithm is applied to divide students into groups. Comparative experiments have been done on a real-life campus dataset collected from a university. The experimental results demonstrate that our method can effectively reveal the variability of student attributes and relationships and accordingly achieves the best clustering results for group division.

Classifying and Characterizing the Types of Gentrified Commercial Districts Based on Sense of Place Using Big Data: Focusing on 14 Districts in Seoul (빅데이터를 활용한 젠트리피케이션 상권의 장소성 분류와 특성 분석 -서울시 14개 주요상권을 중심으로-)

  • Young-Jae Kim;In Kwon Park
    • Journal of the Korean Regional Science Association
    • /
    • v.39 no.1
    • /
    • pp.3-20
    • /
    • 2023
  • This study aims to categorize the 14 major gentrified commercial areas of Seoul and analyze their characteristics based on their sense of place. To achieve this, we conducted hierarchical cluster analysis using text data collected from Naver Blog. We divided the districts into two dimensions: "experience" and "feature" and analyzed their characteristics using LDA (Latent Dirichlet Allocation) of the text data and statistical data collected from Seoul Open Data Square. As a result, we classified the commercial districts of Seoul into 5 categories: 'theater district,' 'traditional cultural district,' 'female-beauty district,' 'exclusive restaurant and medical district,' and 'trend-leading district.' The findings of this study are expected to provide valuable insights for policy-makers to develop more efficient and suitable commercial policies.

Research on Selecting Influential Climatic Factors and Optimal Timing Exploration for a Rice Production Forecast Model Using Weather Data

  • Jin-Kyeong Seo;Da-Jeong Choi;Juryon Paik
    • Journal of the Korea Society of Computer and Information
    • /
    • v.28 no.7
    • /
    • pp.57-65
    • /
    • 2023
  • Various studies to enhance the accuracy of rice production forecasting are focused on improving the accuracy of the models. In contrast, there is a relative lack of research regarding the data itself, which the prediction models are applied to. When applying the same dependent variable and prediction model to two different sets of rice production data composed of distinct features, discrepancies in results can occur. It is challenging to determine which dataset yields superior results under such circumstances. To address this issue, by identifying potential influential features within the data before applying the prediction model and centering the modeling around these, it is possible to achieve stable prediction results regardless of the composition of the data. In this study, we propose a method to adjust the composition of the data's features in order to select optimal base variables, aiding in achieving stable and consistent predictions for rice production. This method makes use of the Korea Meteorological Administration's ASOS data. The findings of this study are expected to make a substantial contribution towards enhancing the utility of performance evaluations in future research endeavors.

The development of training platform for CiADS using cave automatic virtual environment

  • Jin-Yang Li ;Jun-Liang Du ;Long Gu ;You-Peng Zhang;Xin Sheng ;Cong Lin ;Yongquan Wang
    • Nuclear Engineering and Technology
    • /
    • v.55 no.7
    • /
    • pp.2656-2661
    • /
    • 2023
  • The project of China initiative Accelerator Driven Subcritical (CiADS) system has been started to construct in southeast China's Guangdong province since 2019, which is expected to be checked and accepted in the year 2025. In order to make the students in University of Chinese Academy of Sciences (UCAS) better understand the main characteristic and the operation condition in the subcritical nuclear facility, the training platform for CiADS has been developed based on the Cave Automatic Virtual Environment (CAVE) in the Institute of Modern Physics Chinese Academy of Sciences (IMPCAS). The CAVE platform is a kind of non-head mounted virtual reality display system, which can provide the immersive experience and the alternative training platform to substitute the dangerous operation experiments with strong radioactivity. In this paper, the CAVE platform for the training scenarios in CiADS system has been presented with real-time simulation feature, where the required devices to generate the auditory and visual senses with the interactive mode have been detailed. Moreover, the three dimensional modeling database has been created for the different operation conditions, which can bring more freedom for the teachers to generate the appropriate training courses for the students. All the user-friendly features will offer a deep realistic impression to the students for the purpose of getting the required knowledge and experience without the large costs in the traditional experimental nuclear reactor.

Smart-tracking Systems Development with QR-Code and 4D-BIM for Progress Monitoring of a Steel-plant Blast-furnace Revamping Project in Korea

  • Jung, In-Hye;Roh, Ho-Young;Lee, Eul-Bum
    • International conference on construction engineering and project management
    • /
    • 2020.12a
    • /
    • pp.149-156
    • /
    • 2020
  • Blast furnace revamping in steel industry is one of the most important work to complete the complicated equipment within a short period of time based on the interfaces of various types of work. P company has planned to build a Smart Tracking System based on the wireless tag system with the aim of complying with the construction period and reducing costs, ahead of the revamping of blast furnace scheduled for construction in February next year. It combines the detailed design data with the wireless recognition technology to grasp the stage status of design, storage and installation. Then, it graphically displays the location information of each member in relation to the plan and the actual status in connection with Building Information Modeling (BIM) 4D Simulation. QR Code is used as a wireless tag in order to check the receiving status of core equipment considering the characteristics of each item. Then, DB in server system is built, status information is input. By implementing BIM 4D Simulation data using DELMIA, the information on location and status is provided. As a feature of the S/W function, a function for confirming the items will be added to the cellular phone screen in order to improve the accuracy of tagging of the items. Accuracy also increases by simultaneous processing of storage and location tagging. The most significant effect of building this system is to minimize errors in construction by preventing erroneous operation of members. This system will be very useful for overall project management because the information about the position and progress of each critical item can be visualized in real time. It could be eventually lead to cost reduction of project management.

  • PDF

Modeling of Data References with Temporal Locality and Popularity Bias (시간 지역성과 인기 편향성을 가진 데이터 참조의 모델링)

  • Hyokyung Bahn
    • The Journal of the Institute of Internet, Broadcasting and Communication
    • /
    • v.23 no.6
    • /
    • pp.119-124
    • /
    • 2023
  • This paper proposes a new reference model that can represent data access with temporal locality and popularity bias. Among existing reference models, the LRU-stack model can express temporal locality, which is a characteristic that the more recently referenced data has, the higher the probability of being referenced again. However, it cannot take into account differences in popularity of the data. Conversely, the independent reference model can reflect the different popularity of data, but has the limitation of not being able to model changes in data reference trends over time. The reference model presented in this paper overcomes the limitations of these two models and has the feature of reflecting both the popularity bias of data and their changes over time. This paper also examines the relationship between the cache replacement algorithm and the reference model, and shows the optimality of the proposed model.

Deep learning-based AI constitutive modeling for sandstone and mudstone under cyclic loading conditions

  • Luyuan Wu;Meng Li;Jianwei Zhang;Zifa Wang;Xiaohui Yang;Hanliang Bian
    • Geomechanics and Engineering
    • /
    • v.37 no.1
    • /
    • pp.49-64
    • /
    • 2024
  • Rocks undergoing repeated loading and unloading over an extended period, such as due to earthquakes, human excavation, and blasting, may result in the gradual accumulation of stress and deformation within the rock mass, eventually reaching an unstable state. In this study, a CNN-CCM is proposed to address the mechanical behavior. The structure and hyperparameters of CNN-CCM include Conv2D layers × 5; Max pooling2D layers × 4; Dense layers × 4; learning rate=0.001; Epoch=50; Batch size=64; Dropout=0.5. Training and validation data for deep learning include 71 rock samples and 122,152 data points. The AI Rock Constitutive Model learned by CNN-CCM can predict strain values(ε1) using Mass (M), Axial stress (σ1), Density (ρ), Cyclic number (N), Confining pressure (σ3), and Young's modulus (E). Five evaluation indicators R2, MAPE, RMSE, MSE, and MAE yield respective values of 0.929, 16.44%, 0.954, 0.913, and 0.542, illustrating good predictive performance and generalization ability of model. Finally, interpreting the AI Rock Constitutive Model using the SHAP explaining method reveals that feature importance follows the order N > M > σ1 > E > ρ > σ3.Positive SHAP values indicate positive effects on predicting strain ε1 for N, M, σ1, and σ3, while negative SHAP values have negative effects. For E, a positive value has a negative effect on predicting strain ε1, consistent with the influence patterns of conventional physical rock constitutive equations. The present study offers a novel approach to the investigation of the mechanical constitutive model of rocks under cyclic loading and unloading conditions.