Deep learning based water leakage detection for shield tunnel lining

Shield tunnel lining is prone to water leakage,which may further bring about corrosion and structural damage to the walls,potentially leading to dangerous accidents.To avoid tedious and inefficient manual inspection,many projects use artificial intelligence(Al)to detect cracks and water leakage.A novel method for water leakage inspection in shield tunnel lining that utilizes deep learning is introduced in this paper.Our proposal includes a ConvNeXt-S backbone,deconvolutional-feature pyramid network(D-FPN),spatial attention module(SPAM).and a detection head.It can extract representative features of leaking areas to aid inspection processes.To further improve the model's robustness,we innovatively use an inversed low-light enhancement method to convert normally illuminated images to low light ones and introduce them into the training samples.Validation experiments are performed,achieving the average precision(AP)score of 56.8%,which outperforms previous work by a margin of 5.7%.Visualization illustrations also support our method's practical effectiveness.

Artificial intelligence technology in rock mechanics and rock engineering

The scope and scale of rock engineering activities have witnessed continuous expansion,which makes the geological conditions of rock engineering increasingly complex,and there are more and more types of disasters occurring during the construction and operation processes.The uncertainty of engineering geological information and the unclear nature of rock mass failure and disaster mechanisms pose increasingly prominent challenges to the study of rock mechanics and engineering problems.The artificial intelligence technology develops driven by data and knowledge,especially the proposal of digital-twin technology and metaverse ideas.This has injected new innovative impetus for the development of rock mechanics and engineering intelligence,where data and knowledge have been greatly enriched and updated in recent years.This article proposes the construction idea of a rock mechanics and engineering artificial intelligence system based on the metaverse,including intelligent recognition of three-dimensional(3D)geological structures,intelligent recognition of 3D geostress,intelligent recognition of rock mechanical behavior,intelligent evaluation,monitoring and early warning of rock engineering disaster,intelligent design of rock engineering,and intelligent construction of rock engineering.Two typical engineering applications are used as case studies to illustrate the integrated method of applying this system to solve engineering problems with multiple tasks.