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 3872069
  • - Research Article

Quantifying Carbon Emissions Generated by Monorail Transits: A Life Cycle Assessment Approach

Teng Li | Eryu Zhu
  • Special Issue
  • - Volume 2022
  • - Article ID 4879942
  • - Research Article

Class-Incremental Learning on Video-Based Action Recognition by Distillation of Various Knowledge

Vali Ollah Maraghi | Karim Faez
  • Special Issue
  • - Volume 2022
  • - Article ID 8456197
  • - Research Article

Characterization of Group Behavior of Corruption in Construction Projects Based on Contagion Mechanism

Jingjing Li | Qiangqiang Shen | Wencan Gao
  • Special Issue
  • - Volume 2022
  • - Article ID 3510552
  • - Research Article

A Gray Correlation Algorithm for Analysis of Influencing Factors of Film and Television Copyright Export

Bingchao Ren | Ting Jin
  • Special Issue
  • - Volume 2022
  • - Article ID 7272219
  • - Research Article

Study on Behavior and Bearing Capacity Computation Method of Shallow Rock-Socketed Short Piles Based on the Self-Balanced Loading Test

Junxiu Liu | Xianfeng Shao | ... | Guangyong Cao
  • Special Issue
  • - Volume 2022
  • - Article ID 9818226
  • - Research Article

Research on Human Behavior Modeling of Sports Culture Communication in Industrial 4.0 Intelligent Management

Zhihui Li
  • Special Issue
  • - Volume 2022
  • - Article ID 9598781
  • - Research Article

Exploring Technical Decision-Making Risks in Construction Megaprojects Using Grounded Theory and System Dynamics

Xiaoying Tang | Mengjun Wang | ... | Juanjuan Tang
  • Special Issue
  • - Volume 2022
  • - Article ID 5115432
  • - Research Article

Construction of Relationship Model between College Students’ Psychological Status and Epidemic Situation Based on BP Neural Network

Shuguang Yao
  • Special Issue
  • - Volume 2022
  • - Article ID 1256975
  • - Research Article

System Dynamics Analysis of Construction Safety Risk considering Existing Railway Lines

Xiaoye Zeng | Naixin Huang | ... | Jianling Huang
  • Special Issue
  • - Volume 2022
  • - Article ID 6978771
  • - Research Article

A Novel Noncooperative Behavior Management Method for Multiattribute Large Group Decision-Making

Xiaoqin Dong | Ying Yang | ... | Xianbin Sun

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