Active Learning for Accurate Settlement Prediction Using Numerical Simulations in Mechanized Tunneling

Surrogate modeling for mechanized tunneling simulations with uncertain data

Computational reliability assessment in mechanized tunneling requires realistic numerical models accounting for the uncertainty of geotechnical and tunneling process parameters. For real-time prognoses, surrogate models are in general inevitable to substitute expensive complex numerical simulations. In this paper, different strategies for surrogate modeling in mechanized tunneling simulations a...

Prediction of maximum surface settlement caused by earth pressure balance shield tunneling using random forest

Due to urbanization and population increase, need for metro tunnels, has been considerably increased in urban areas. Estimating the surface settlement caused by tunnel excavation is an important task especially where the tunnels are excavated in urban areas or beneath important structures. Many models have been established for this purpose by extracting the relationship between the settlement a...

Interaction Modeling in Mechanized Tunneling

prediction of maximum surface settlement caused by earth pressure balance shield tunneling using random forest

due to urbanization and population increase, need for metro tunnels, has been considerably increased in urban areas. estimating the surface settlement caused by tunnel excavation is an important task especially where the tunnels are excavated in urban areas or beneath important structures. many models have been established for this purpose by extracting the relationship between the settlement a...

Learning Accurate Active Contours

Focus of research in Active contour models (ACM) area is mainly on development of various energy functions based on physical intuition. In this work, instead of designing a new energy function, we generate a multitude of contour candidates using various values of ACM parameters, assess their quality, and select the most suitable one for an object at hand. A random forest is trained to make cont...