Automatic Quality Inspection and Intelligent Prevention of Prefabricated Building Construction Based on BIM and Fuzzy Logic




BIM, Computer vision, Fuzzy logic, Construction quality, Deep learning, Convolutional neural network


The construction industry plays an important role in China's economy. However, traditional construction quality inspection methods have problems such as low efficiency, high leakage rate, and poor real-time performance. Therefore, taking the appearance quality defects of reinforced concrete engineering as an example, this study combines building information models and computer vision technology. By this way, it can achieve automatic quality inspection and intelligent quality problem prevention, to improve the quality management level during the construction process. In this study, ResNet50 network was pre-trained and classified using a self-made defect image database. After transfer learning, the accuracy values of the training and testing sets remained stable at around 0.95 and the loss value remained stable at around 0.10 after 10 epochs, indicating a significant improvement in learning performance. In the defect area quantification experiment, four corner coordinates of the defect image were calculated. These corner coordinates are simultaneously obtained and saved during on-site image acquisition. According to the calculation results, the defect area of honeycomb is approximately 67.67 square centimetres. These results confirm that this method improves the efficiency and accuracy of quality inspection and has potential application in quality inspection of prefabricated building construction.


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How to Cite

Ma, W. (2024). Automatic Quality Inspection and Intelligent Prevention of Prefabricated Building Construction Based on BIM and Fuzzy Logic. Decision Making: Applications in Management and Engineering, 7(2), 65–80.