The proliferation of data collected by modern tunnel-boring machines (TBMs) presents a
substantial opportunity for the application of machine learning (ML) to support the decision …

Recently, a class of machine learning methods called physics-informed neural networks
(PINNs) has been proposed and gained prevalence in solving various scientific computing …

Abstract Artificial Neural Networks (NNWs) are appealing functions to substitute high
dimensional and non-linear history-dependent problems in computational mechanics since …

Because of its nonlinearity and path-dependency, analysis of the elasto-plastic behavior of
the finite element (FE) model is computationally expensive. By directly learning sequential …

This contribution discusses surrogate models that emulate the solution field (s) in the entire
simulation domain. The surrogate uses the most characteristic modes of the solution field (s) …

Tunnelling induced surface settlements can cause damage in buildings located in the
vicinity of the tunnel. Currently, surface settlements and associated building damage risks …

This paper reports on planning and construction related results from research performed at
the Collaborative Research Center “Interaction Modeling in Mechanized Tunneling” at Ruhr …

Additively manufactured structures can be tailor-made to optimally distribute mechanical
loads while remaining light-weight. To efficiently analyze the locally unique mechanical …

Abstract The Cut Finite Element Method (CutFEM) is recently shown to be a versatile
approach for tunnel construction modeling and settlement analysis. The CutFEM can …

Ground settlement prediction during mechanized tunneling is of paramount importance and
remains a challenging research topic. Typically, two paradigms are existing: a physics …