AI-enabled Smart Pile Driving System integrated with MEMS Sensors
Project Description
The majority of existing pile driving systems used in the construction industry are based on open-loop control without any feedback mechanism. As a result, the pile driving process lacks precision and efficiency. Additionally, traditional pile driving technology has not seen much improvement for many decades.
Smart manufacturing is one of the key technologies that the Hong Kong government is pushing to reduce the cost of building construction and address the lack of manpower/construction workers. Developing intelligent construction equipment such as a smart pile driving system can help accelerate the adoption of smart manufacturing in the industry.
This project aims to develop an intelligent pile driving system that incorporates MEMS sensors and artificial intelligence (AI) techniques for monitoring and control of the pile driving process. As part of the ongoing collaboration between HKUST and UC Berkeley, undergraduate students will work with faculty researchers to advance various aspects of the smart pile driving system.
Smart manufacturing is one of the key technologies that the Hong Kong government is pushing to reduce the cost of building construction and address the lack of manpower/construction workers. Developing intelligent construction equipment such as a smart pile driving system can help accelerate the adoption of smart manufacturing in the industry.
This project aims to develop an intelligent pile driving system that incorporates MEMS sensors and artificial intelligence (AI) techniques for monitoring and control of the pile driving process. As part of the ongoing collaboration between HKUST and UC Berkeley, undergraduate students will work with faculty researchers to advance various aspects of the smart pile driving system.
Supervisor
LEE Yi-Kuen
Quota
4
Course type
UROP1000
UROP1100
UROP2100
UROP3100
UROP3200
UROP4100
Applicant's Roles
Develop algorithms to analyze sensor data and model the pile driving process using open-source AI model
Design and prototype an AI-based controller to optimize the pile driving operation based on sensor feedback
Conduct laboratory experiments to test and validate the smart pile driving system
Work with faculty mentors, PhD and postdoc on selected tasks like sensor integration, data analysis algorithm development
Learn and apply machine learning tools to process sensor data
Participate in executing laboratory experiments and system validation
Maintain documentation of technical work in reports and presentations
Design and prototype an AI-based controller to optimize the pile driving operation based on sensor feedback
Conduct laboratory experiments to test and validate the smart pile driving system
Work with faculty mentors, PhD and postdoc on selected tasks like sensor integration, data analysis algorithm development
Learn and apply machine learning tools to process sensor data
Participate in executing laboratory experiments and system validation
Maintain documentation of technical work in reports and presentations
Applicant's Learning Objectives
Hands-on experience with MEMS sensors, embedded systems and AI techniques
Opportunity to get published through joint work with the UC Berkeley researchers
Training in multidisciplinary skills at the intersection of sensors, systems engineering and data science
Potential for submission to conferences/workshops on intelligent infrastructure
Students from various backgrounds like Engineering, Computer Science and Data Science are welcome to apply.
Prior coursework in related topics is preferred but not required. This project will provide excellent training for students interested in research careers involving smart systems and AI technology.
Opportunity to get published through joint work with the UC Berkeley researchers
Training in multidisciplinary skills at the intersection of sensors, systems engineering and data science
Potential for submission to conferences/workshops on intelligent infrastructure
Students from various backgrounds like Engineering, Computer Science and Data Science are welcome to apply.
Prior coursework in related topics is preferred but not required. This project will provide excellent training for students interested in research careers involving smart systems and AI technology.
Complexity of the project
Challenging