TY - GEN
T1 - As-Built BIM Updating Based on Image Processing and Artificial Intelligence
AU - Zhang, Cheng
AU - Huang, Hong
N1 - Publisher Copyright:
© 2019 American Society of Civil Engineers.
PY - 2019
Y1 - 2019
N2 - Image-based technology is effective for recording on-site data geospatially and chronologically. It has gained increasing attention in the construction field for progress monitoring. However, a notable downside of image processing is the light condition, particularly for noisy environments such as construction sites. Poor or undesirable ambient light conditions produce low quality images which significantly affect the accuracy of data extracted from related images. This paper proposes an innovative approach based on thermal image analysis to mitigate problems related to the image quality. Construction materials with various emissivity resulting in measurable temperature differences can be a crucial evidence to identify different materials. In addition, a convolutional neural network was used to segment different components semantically. Together with a camera-view image extracted from BIM model, a cross matching can be applied to confirm the presence of different building elements with specific materials, which is the key step to estimate the construction process qualitatively.
AB - Image-based technology is effective for recording on-site data geospatially and chronologically. It has gained increasing attention in the construction field for progress monitoring. However, a notable downside of image processing is the light condition, particularly for noisy environments such as construction sites. Poor or undesirable ambient light conditions produce low quality images which significantly affect the accuracy of data extracted from related images. This paper proposes an innovative approach based on thermal image analysis to mitigate problems related to the image quality. Construction materials with various emissivity resulting in measurable temperature differences can be a crucial evidence to identify different materials. In addition, a convolutional neural network was used to segment different components semantically. Together with a camera-view image extracted from BIM model, a cross matching can be applied to confirm the presence of different building elements with specific materials, which is the key step to estimate the construction process qualitatively.
UR - http://www.scopus.com/inward/record.url?scp=85068742537&partnerID=8YFLogxK
U2 - 10.1061/9780784482421.002
DO - 10.1061/9780784482421.002
M3 - Conference Proceeding
AN - SCOPUS:85068742537
T3 - Computing in Civil Engineering 2019: Visualization, Information Modeling, and Simulation - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019
SP - 9
EP - 16
BT - Computing in Civil Engineering 2019
A2 - Cho, Yong K.
A2 - Leite, Fernanda
A2 - Behzadan, Amir
A2 - Wang, Chao
PB - American Society of Civil Engineers (ASCE)
T2 - ASCE International Conference on Computing in Civil Engineering 2019: Visualization, Information Modeling, and Simulation, i3CE 2019
Y2 - 17 June 2019 through 19 June 2019
ER -