BIM based automatic construction process monitoring

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, work space analysis and quality assurance. However, a notable downside of image processing is the light condition, particularly for noisy environments such as construction sites. Much research strives to reduce the level of errors in image-based monitoring methods but it still has remained a challenging technique. This project proposes an innovative approach based on thermal image analysis to overcome problems related to the image quality. Construction materials with various emissivity resulting in measurable temperature differences can be a crucial evidence to identify different material. In addition, the convolutional neural network (CNN) based semantic segmentation is used to semantically segment different components, such as column, beam, etc. Together with a camera-view image extracted from BIM model, a cross matching is applied to confirm the presence of different building elements with specific materials, which is the key step to estimate the construction process qualitatively.

Faculty Supervisor:

Amin Hammad

Student:

Partner:

Xi'an Jiaotong-Liverpool University

Discipline:

Engineering

Sector:

Education

University:

Concordia University

Program: