Computational Intelligence and Neuroscience

Human Behavior Modelling in Engineering Management under Industry 4.0


Publishing date
01 Aug 2022
Status
Published
Submission deadline
08 Apr 2022

Lead Editor

1Central South University, Changsha, China

2City University of Hong Kong, Hong Kong

3Xi'an University of Architecture and Technology, Xian, China

4China University of Mining and Technology, Xuzhou, China

5London South Bank University, London, UK


Human Behavior Modelling in Engineering Management under Industry 4.0

Description

Human society is entering the era of Industry 4.0. Automatic production and intelligent manufacturing are proliferating rapidly across industries, freeing the workforce from simple but repetitive work. The roles of humans in engineering systems are continuously changing. In addition, engineering systems are becoming more intelligent and complex, which further increases the difficulties in engineering management. Engineering management is facing unprecedented opportunities and challenges. As humans are the most important and determining factor of management, human behavior can significantly affect the efficiency of engineering management.

However, it is usually not easy to understand the mechanism of decision-making process and human behavior in engineering management. This often requires interdisciplinary knowledge in engineering, management, neural engineering, mathematics, and psychology. Efficient tools for human behavior modelling and simulation are urgently needed. With the development of mathematical algorithms and computational technologies, intelligent modelling and simulation bring innovation to research on human behavior in engineering management. Specifically, advanced modelling and simulation methods, such as machine learning, deep learning, big data, system dynamics, and agent-based modelling, enable revealing the mechanism of decision-making process and predicting human behavior under various circumstances. Intelligent modelling and simulation provide valuable information and databases for managers to improve the efficiency of engineering management. There should be continuous research exploring applying intelligent techniques to model and simulate human behavior in engineering management.

The aim of this Special Issue is to solicit research and offer a timely opportunity to scholars and industry practitioners to discuss, share, and disseminate current innovations in intelligent modelling and simulation techniques for human behavior in engineering management under Industry 4.0. Authors are invited to present original research and review articles that will stimulate continuing efforts in this field. We hope that this Special Issue helps solve practical engineering problems and bring theoretical contributions.

Potential topics include but are not limited to the following:

  • Human behavior changes in engineering management under Industry 4.0
  • Intelligent modelling and simulation methods for human behavior in engineering management
  • Dynamic relationships between human behavior and the efficiency of engineering management under Industry 4.0
  • Machine learning and deep learning for human behavior in engineering management
  • Data mining for human behavior in engineering management
  • System dynamics for human behavior in engineering management
  • Agent-based modelling for human behaviour in engineering management
  • Complex employee relationship network modelling in engineering management under Industry 4.0
  • Intelligent prediction, monitoring, assessment, and management of human behavior in engineering management
  • Data-driven and intelligent decision support systems in engineering management under Industry 4.0

Articles

  • Special Issue
  • - Volume 2022
  • - Article ID 9701963
  • - Research Article

Empirical Research on the Critical Success Factors of Construction Program

Chunmei Zhou | Zheng He | ... | Hongyan Yan
  • Special Issue
  • - Volume 2022
  • - Article ID 1904158
  • - Research Article

Garment Design Models Combining Bayesian Classifier and Decision Tree Algorithm

Xiaoyu Yan | Shuo Ma
  • Special Issue
  • - Volume 2022
  • - Article ID 7012399
  • - Research Article

Identification Method of Citrus Aurantium Diseases and Pests Based on Deep Convolutional Neural Network

Yuke Lin | Jin Xu | Ying Zhang
  • Special Issue
  • - Volume 2022
  • - Article ID 3449431
  • - Research Article

Application of Mathematical Methods Based on Improved Fuzzy Computing in Building and Urban Design in the Environment of Industry 4.0

Chuan Duan
  • Special Issue
  • - Volume 2022
  • - Article ID 4410075
  • - Research Article

Multimedia Automation Access Control of Big Data Open Resources Based on Blockchain

Nan Zhao | Hui Su | ... | Yan Zhao
  • Special Issue
  • - Volume 2022
  • - Article ID 3581563
  • - Research Article

Grounded Theory and Social Psychology Approach to Investigating the Formation of Construction Workers’ Unsafe Behaviour

Yu Han | Xuezheng Li | ... | Obas John Ebohon
  • Special Issue
  • - Volume 2022
  • - Article ID 9223552
  • - Research Article

Abnormal Target Detection Method in Hyperspectral Remote Sensing Image Based on Convolution Neural Network

Yun Liu | Jia-Bao Liu
  • Special Issue
  • - Volume 2022
  • - Article ID 9306167
  • - Research Article

Differentiation in Emotional Investments in Work Groups among Different Social Status of Construction Industry Practitioners: A Perspective from the Social Exchange Theory

Wenqing Zhang | Dingzhou Fei
  • Special Issue
  • - Volume 2022
  • - Article ID 2484850
  • - Research Article

Correlation Study between Rural Human Settlement Health Factors: A Case Study of Xiangxi, China

Shuyuan Tong | Yafeng Zhu | Zhe Li
  • Special Issue
  • - Volume 2022
  • - Article ID 3384948
  • - Research Article

Research on Building Space Model Method Based on Big Data Map Visual Design

Qiang Xu | Long He

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