Table of Content

Machine Learning based Methods for Mechanics

Submission Deadline: 31 January 2020 (closed)

Guest Editors

Prof. Xiaoying Zhuang, Leibniz Univerisity Hannover, Germany
Prof. Timon Rabczuk, Bauhaus University Weimar, Germany
Prof. Hung Nguyen Xuan, Ho Chi Minh City University of Technology (HUTECH), Vietnam

Summary

In this age of big data, machine learning techniques have been successfully applied in image processing, genomics, financial problems and even medical diagnosis. The emerging application of machine learning and big data analysis has fundamentally influenced and changed our way of how we think, plan, solve and analyze in engineering. Nevertheless, we are faced with many issues and unsolved problems when applying data drive computing and machine learning in engineering analysis.

The objective of the special issue is to invite the submissions of the works of researchers from worldwide and provide a fair overview on the state of the art on theories, methods and techniques contributed to the machine learning for mechanics and applications, and also the remaining issues and limitations, thus promoting further research interests.

Possible topics (inviting more ideas): 
Papers on topics related to new theory, methods and applications related to the machine learning for mechanics not only to theory but also to experiments are encouraged to submit to the special issue. 

We outline the following possible related topics but are not restricted to: 
• Machine learning based solutions of PDEs
• Data driven constitutive modelling
• Visualization and visual analytics of mechanical engineering
• Big data for design and optimization 
• Machine learning assisted high performance computing
• Data-driven computing in dynamics and molecular dynamics
• Machine learning for uncertainties analysis and stochastic model
• Data-driven simulation techniques
• Machine learning for discrete particle-based method



Published Papers


  • Open Access

    ARTICLE

    An Improved Algorithm for the Detection of Fastening Targets Based on Machine Vision

    Jian Yang, Lang Xin, Haihui Huang, Qiang He
    CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 779-802, 2021, DOI:10.32604/cmes.2021.014993
    (This article belongs to this Special Issue: Machine Learning based Methods for Mechanics)
    Abstract Object detection plays an important role in the sorting process of mechanical fasteners. Although object detection has been studied for many years, it has always been an industrial problem. Edge-based model matching is only suitable for a small range of illumination changes, and the matching accuracy is low. The optical flow method and the difference method are sensitive to noise and light, and camshift tracking is less effective in complex backgrounds. In this paper, an improved target detection method based on YOLOv3-tiny is proposed. The redundant regression box generated by the prediction network is filtered by soft nonmaximum suppression (NMS)… More >

  • Open Access

    ARTICLE

    Study on the Improvement of the Application of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise in Hydrology Based on RBFNN Data Extension Technology

    Jinping Zhang, Youlai Jin, Bin Sun, Yuping Han, Yang Hong
    CMES-Computer Modeling in Engineering & Sciences, Vol.126, No.2, pp. 755-770, 2021, DOI:10.32604/cmes.2021.012686
    (This article belongs to this Special Issue: Machine Learning based Methods for Mechanics)
    Abstract The complex nonlinear and non-stationary features exhibited in hydrologic sequences make hydrological analysis and forecasting difficult. Currently, some hydrologists employ the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method, a new time-frequency analysis method based on the empirical mode decomposition (EMD) algorithm, to decompose non-stationary raw data in order to obtain relatively stationary components for further study. However, the endpoint effect in CEEMDAN is often neglected, which can lead to decomposition errors that reduce the accuracy of the research results. In this study, we processed an original runoff sequence using the radial basis function neural network (RBFNN) technique… More >

  • Open Access

    ARTICLE

    Application of FCM Algorithm Combined with Articial Neural Network in TBM Operation Data

    Jingyi Fang, Xueguan Song, Nianmin Yao, Maolin Shi
    CMES-Computer Modeling in Engineering & Sciences, Vol.126, No.1, pp. 397-417, 2021, DOI:10.32604/cmes.2021.012895
    (This article belongs to this Special Issue: Machine Learning based Methods for Mechanics)
    Abstract Fuzzy clustering theory is widely used in data mining of full-face tunnel boring machine. However, the traditional fuzzy clustering algorithm based on objective function is difficult to effectively cluster functional data. We propose a new Fuzzy clustering algorithm, namely FCM–ANN algorithm. The algorithm replaces the clustering prototype of the FCM algorithm with the predicted value of the articial neural network. This makes the algorithm not only satisfy the clustering based on the traditional similarity criterion, but also can effectively cluster the functional data. In this paper, we rst use the t-test as an evaluation index and apply the FCM–ANN algorithm… More >

  • Open Access

    ARTICLE

    A Reinforcement Learning System for Fault Detection and Diagnosis in Mechatronic Systems

    Wanxin Zhang, Jihong Zhu
    CMES-Computer Modeling in Engineering & Sciences, Vol.124, No.3, pp. 1119-1130, 2020, DOI:10.32604/cmes.2020.010986
    (This article belongs to this Special Issue: Machine Learning based Methods for Mechanics)
    Abstract With the increasing demand for the automation of operations and processes in mechatronic systems, fault detection and diagnosis has become a major topic to guarantee the process performance. There exist numerous studies on the topic of applying artificial intelligence methods for fault detection and diagnosis. However, much of the focus has been given on the detection of faults. In terms of the diagnosis of faults, on one hand, assumptions are required, which restricts the diagnosis range. On the other hand, different faults with similar symptoms cannot be distinguished, especially when the model is not trained by plenty of data. In… More >

  • Open Access

    ARTICLE

    Investigation of Granite Deformation Process under Axial Load Using LSTM-Based Architectures

    Yalei Wang, Jinming Xu, Mostafa Asadizadeh, Chuanjiang Zhong, Xuejie Tao
    CMES-Computer Modeling in Engineering & Sciences, Vol.124, No.2, pp. 643-664, 2020, DOI:10.32604/cmes.2020.09866
    (This article belongs to this Special Issue: Machine Learning based Methods for Mechanics)
    Abstract Granite is generally composed of quartz, biotite, feldspar, and cracks. The changes in digital parameters of these compositions reflect the detail of the deformation process of the rock. Therefore, the estimation of the changes in digital parameters of the compositions is much helpful to understand the deformation and failure stages of the rock. In the current study, after dividing the frames in the video images photographed during the axial compression test into four parts (or, the upper left, upper right, lower left, and lower right ones), the digital parameters of various compositions in each part were then extracted. Using these… More >

  • Open Access

    ARTICLE

    Intelligent Detection Model Based on a Fully Convolutional Neural Network for Pavement Cracks

    Duo Ma, Hongyuan Fang, Binghan Xue, Fuming Wang, Mohammed A. Msekh, Chiu Ling Chan
    CMES-Computer Modeling in Engineering & Sciences, Vol.123, No.3, pp. 1267-1291, 2020, DOI:10.32604/cmes.2020.09122
    (This article belongs to this Special Issue: Machine Learning based Methods for Mechanics)
    Abstract The crack is a common pavement failure problem. A lack of periodic maintenance will result in extending the cracks and damage the pavement, which will affect the normal use of the road. Therefore, it is significant to establish an efficient intelligent identification model for pavement cracks. The neural network is a method of simulating animal nervous systems using gradient descent to predict results by learning a weight matrix. It has been widely used in geotechnical engineering, computer vision, medicine, and other fields. However, there are three major problems in the application of neural networks to crack identification. There are too… More >

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