Guest Speaker: Prof. Haiyan Xie (Illinois State University)

Time/Date: 10 am Monday 12^th, June 2023

Classroom: Room 402, Tongji Building A

ABSTRACT | 摘要

Machine learning plays a vital role in industrial projects. Its importance stems from its ability to analyze and interpret large amounts of data, identify patterns, make predictions, and automate complex tasks. With the advancement and development of Industry 4.0-oriented Digital Twin (DT) technology, especially the widespread application of machine learning, people have seen the advantages of DT implementation.

This presentation starts with the key to the implementation of machine learning in industry projects and uses innovative examples to discuss current practices, mainly including data-driven decision-making, process automation, predictive analytics, optimization and efficiency, and product development and quality control. This presentation also uses scientometric analysis and two knowledge mapping tools to visualize and analyze literature in related scientific fields. Then, we combine methods such as clustering, knowledge mapping, and network analysis to derive the current focus and future direction of digital twins in industrial projects from the data of time distribution, journal domain, and topic distribution. We will further explore the potential of machine learning and digital twins in the construction industry. These innovative examples and current situation analysis can provide new content and methods for the teaching and research of engineering management.

机器学习在工业项目中发挥着至关重要的作用。它的重要性源于其分析和解释大量数据、识别模式、进行预测和自动执行复杂任务的能力。随着以工业 4.0 为导向的数字孪生 (DT)技术的推进和发展，尤其是机器学习的广泛应用，人们已经看到了数字孪生 (DT) 实施的优势。

本专题从机器学习在行业项目中的实施关键出发，并使用创新示例来讨论当前的实践，内容主要包括：数据驱动决策，过程自动化，预测分析，优化和效率，以及产品开发和质量控制。本次讲座还通过使用科学计量分析，以及两种知识图谱工具来进行可视化和分析相关科学领域的文献。然后，我们结合聚类、知识图谱和网络分析等方法，从时间分布、期刊领域和主题分布的数据中得出数字孪生在工业项目中的当前焦点和未来方向，并进一步探索机器学习和数字孪生在建筑行业中的潜力。这些创新示例以及现状分析，为工程管理的教学与研究提供了新的内容与方法。

GUEST BIO | 嘉宾简介

Dr. Haiyan Xie is Tenured Professor of Illinois State University, Postdoctoral Fellow of University of Cambridge. Her currently researching on machine learning and digital twin for industrial projects, including generating and updating building and infrastructure digital twin models; intelligent automation of design and construction tasks; infrastructure condition assessment, modeling and sensing, etc.

谢海燕博士是伊利诺伊州立大学项目管理系教授 剑桥大学博士后。目前从事工业项目的机器学习和数字孪生技术研究 ，包括生成和更新建筑和基础设施数字孪生模型；设计和施工任务的智能自动化；基础设施状况评估、建模和传感等。