Research on the Prediction of the Service Life of Concrete Building based on the Crack Recognition of UAV

To address challenges in traditional building crack detection such as low efficiency and subjective lifespan estimation, this study proposes an integrated UAV-based method for structural aging prediction. The system employs high-resolution imaging equipment to capture concrete building surfaces, with image processing algorithms extracting critical parameters including crack length, width, and density. By combining these measurements with concrete aging mechanisms and crack propagation patterns, a predictive model for structural lifespan is developed. Using three concrete residential buildings of varying service ages as test cases, comparative analysis between UAV detection and manual inspection demonstrated the method's feasibility and advantages. Experimental results showed 89.7% crack recognition accuracy with estimated lifespan errors consistently below 5%. Compared to conventional manual methods, the UAV approach achieves 3-5 times higher detection efficiency while providing quantified data support for more objective predictions. This research delivers efficient and precise technical support for building safety assessment and lifespan prediction, particularly suitable for application in building operation and maintenance management to enhance scientific rigor and intelligent management capabilities.