Deep-learning Networks for Construction Quality Control

点击翻译按钮获取中文摘要

The focus of this project is to design and implement an effective deep learning network to identify and predict quality problems in construction projects. This will involve deep learning-based analysis of text to automatically identify quality defects from data generated by the building information management (BIM) technology during the construction process. This analysis will be done by extracting keywords and the context of surrounding words to obtain an accurate text classification model. The partner company has developed an information-based management model to reduce costs and maximize profits over the construction process. It uses traditional data analysis methods built on project intelligence to facilitate cost optimization. It processes internal historical data (e.g. planned start and end dates) as well as external factors (e.g. project size and contract) to provide cost estimates. Although this model can be used to reduce project costs, performing an analysis over all stages of a construction project is unmanageable due to the complexity of tasks that must be performed and the massive amounts of data collected by the BIM technology. This reduces the platform utility and impacts construction productivity. The BIM technology collects data from all project phases, so this data can be used as a basis to answer numerous task-specific questions. The first goal of this project will be to enable the platform to process project data from the entire construction project life cycle to answer task-specific questions through data-driven insights. A predictive model will be developed to examine current and past data to detect quality defects. This model will be applicable to a wide range of construction projects to predict quality issues in areas such as framing, drywall, electric, plumbing, and flooring. The predictive model will be based on deep learning algorithms to ensure minimal defects as well as project execution according to required standards. It will also be used to classify quality defects into categories such as minor, major and critical. This hierarchical classification can have a significant impact on improving areas such as schedule management, subcontractor management, construction site environment monitoring, and safety.