Theses Doctoral

A Self-localized Smart Hardhat System for Construction 4.0

Sun, Weizhuo

Human beings are at the rim of embracing Industry 4.0, which indicates the mass application of Artificial Intelligence (AI), Internet of Things (IoT), 5G, connected industrial robots, and big data may breed the next wave of the industrial revolution, and eventually increase human society’s productivity and happiness. Construction 4.0 is modeled after Industry 4.0, a confluence and convergence of emerging trends and technologies from the manufacturing industry in a dedicated framework designed for the Architecture\Engineering\Construction (AEC) industry.

Within this framework vision, industrial prefabrication, cyber-physical systems, and digital technologies will work together toward a safer and more productive future. However, the cold facts were that in the past 20 years, the productivity in AEC industries only grew less than 20%, outpaced by the manufacturing industry (50%); as for the safety aspect, the industry mortality rate in the US kept steady in the last ten years. All facts point to the current approach to improving productivity, and safety may have enormous room to improve.

Improving workplace safety and work efficiency is extremely important for the AEC industry's highly dynamic and GNSS-denial environment. However, the lack of precise indoor localization and estimation of the human sight field has impeded this process. It hinders augmented reality (AR) and prevents other emerging technologies (e.g., cyber-physical systems, digital twins). Acknowledging this void, this research customizes a novel wearable device system applying LiDAR as a key sensor and semantic 3D map as preload database to outperform current approaches in precision and range, even without any positioning infrastructures. In addition, preload map data can be generated directly from existing BIM models or future full-functional digital twin systems, reducing the cost of large-scale commercial deployment. Experimental results are presented to demonstrate the comparative efficacy of our system.


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More About This Work

Academic Units
Civil Engineering and Engineering Mechanics
Thesis Advisors
Feng, Maria Q.
Ph.D., Columbia University
Published Here
May 10, 2023