**Abstract**

Quality performance of building construction is frequently assessed throughout the construction life cycle. In Hong Kong, quality management system must be established before commencing new building works. Regular building inspections are conducted in accordance with the code of practice of new building works. Quality managers are deployed in construction sites to inspect and record any building defects. The concrete cracks must be identified, which is usually followed by proposed rectifications, in order to protect the public and occupants from dangers. This chapter is structured as follows: Background information of concrete cracks is firstly given. Traditional technique of conducting regular manual inspection is introduced, in accordance with Hong Kong's code of practice "Building Performance Assessment Scoring System (PASS)". Then, an advanced technique of conducting crack inspection intelligently based on computer vision is introduced. The procedures of defining, training, and benchmarking the architecture of convolutional neural network models are presented. The calculation steps are detailed and illustrated using a simple textbook example. An experiment case study is used to compare the time, cost of inspecting concrete cracks using both manual and advanced technique. The study concludes with a presentation of the future vision of robot-human collaboration for inspecting concrete cracks in building construction.

**Keywords:** building quality control, concrete crack, quality inspection, computer vision, artificial intelligence
