It is very important to observe the health monitoring (SHM) of mass concrete structure by atomic force microscope (AFM) for the safety and sustainability of the structure. SHM model is proposed, in which Monte Carlo sampling can be used to quantify the uncertainty. Using Bayesian inference, three independent case studies are studied: crack, local damage identification and building main component detection. In addition to uncertainty indicators show that the mean softmax variance and entropy have a good correlation with miss classification. Although uncertainty indicators can be used to trigger which can further improve performance. The results show that this method is superior to the latest one. In the actual scene test, the crack segmentation accuracy is 91%, and IOU is 78%. The accuracy of damage segmentation is 83% and IOU is 73%, which shows that this study can be fully used in the actual production process. It can be seen from these that the method proposed.

Neha Jain. Common Cracks of Mass Concrete in Construction Quality by AFM. International Journal of Engineering Technology and Construction (2020), Vol. 1, Issue 3: 54-63. https://doi.org/10.38007/IJETC.2020.010305.

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