TY - JOUR
T1 - A BIM-enabled robot control system for automated integration between rebar reinforcement and 3D concrete printing
AU - Du, Song
AU - Teng, Fei
AU - Zhuang, Zicheng
AU - Zhang, Dong
AU - Li, Mingyang
AU - Li, Heng
AU - Weng, Yiwei
N1 - Publisher Copyright:
© 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
PY - 2024
Y1 - 2024
N2 - 3D concrete printing (3DCP) has attracted significant attention due to the benefits of enhanced productivity and sustainability. However, existing 3DCP techniques face challenges of automation and practicality in integrating conventional rebar reinforcement with printed concrete structures. This study presents an automated robot system coupled with Building Information Modeling (BIM) to address the challenge. Dynamo scripts in BIM were used to generate printing paths, further optimised by a proposed algorithm for incorporating rebar reinforcement. A deep learning model was adopted to identify rebars with large aspect ratios. The average accuracy of rebar recognition is 92.5%, with a position error within 2 mm. A centralised control system was developed for multiple-device communication, including a camera, a robot arm, and a gripper. Finally, a real-time demonstration was conducted, representing the first practical demonstration of an automated robotic system to integrate rebar reinforcement with printed structures using BIM-generated data in the physical world.
AB - 3D concrete printing (3DCP) has attracted significant attention due to the benefits of enhanced productivity and sustainability. However, existing 3DCP techniques face challenges of automation and practicality in integrating conventional rebar reinforcement with printed concrete structures. This study presents an automated robot system coupled with Building Information Modeling (BIM) to address the challenge. Dynamo scripts in BIM were used to generate printing paths, further optimised by a proposed algorithm for incorporating rebar reinforcement. A deep learning model was adopted to identify rebars with large aspect ratios. The average accuracy of rebar recognition is 92.5%, with a position error within 2 mm. A centralised control system was developed for multiple-device communication, including a camera, a robot arm, and a gripper. Finally, a real-time demonstration was conducted, representing the first practical demonstration of an automated robotic system to integrate rebar reinforcement with printed structures using BIM-generated data in the physical world.
KW - 3D concrete printing
KW - Building Information Modeling
KW - deep learning
KW - rebar reinforcement
KW - robot control system
UR - https://www.scopus.com/pages/publications/85188606568
U2 - 10.1080/17452759.2024.2332423
DO - 10.1080/17452759.2024.2332423
M3 - 文章
AN - SCOPUS:85188606568
SN - 1745-2759
VL - 19
JO - Virtual and Physical Prototyping
JF - Virtual and Physical Prototyping
IS - 1
M1 - e2332423
ER -