Abstract

Benyamin Sadeghi, PhD candidate, bsadeghi@uwm.edu

                                           Hamid Seifoddini, Faculty member, seifoddi@uwm.edu

 University of Wisconsin-Milwaukee

Biography:

Benyamin Sadeghi is a PhD candidate in the Industrial and manufacturing Engineering Department at the University of Wisconsin-Milwaukee-UWM. He is exploring the potential applications of the technologies of Industry 4.0 in construction activities. He has his MS and BS degrees in Civil Engineering from Sharif University Technology in 2015 and Isfahan University of Technology in 2012, respectively.

Hamid Seifoddini is a faculty member in the Industrial and Manufacturing Engineering Department at UWM. He works on the applications of clustering techniques in manufacturing. His focus is on lean manufacturing, prescriptive maintenance, and Industry 4.0.

Abstract 

Construction 4.0, the application of Industry 4.0 technologies in construction activities, promises to revolutionize the construction industry by increasing productivity, enhancing safety, improving sustainability, and achieving the highest standard of quality. Industry 4.0 technologies, such as Artificial Intelligence (AI), robotics, and the Internet of Things (IoT), offer an unprecedented potential for the transformation of the construction activities in the 21st century. Construction activities constitute approximately 4.3% of the US GDP and employ 7.5 million workers. Although productivity has significantly increased in other industries, progress has been slow in the construction sector. One contributing factor is the relatively low investment in IT compared to other industries. The diversity and novelty of Industry 4.0 technologies, along with their wide range of applications in the construction sector, presents a considerable challenge in selecting the most appropriate technologies for various construction applications. This paper is based on our research which examines and clusters 33 Industry 4.0 technologies and assesses their potential for different construction applications. In this study, text mining techniques combined with clustering analysis are used to gain insights into various construction 4.0 technologies and their potential in improving construction activities.