The response of buildings to tunnelling: A case study

The response of buildings to tunnelling induced ground movements is an area of great importance for many urban tunnelling projects. This paper presents the response of two buildings to the construction of a 12 m diameter sprayed concrete lining (SCL) tunnel with face reinforcement, in Italy. Soil and structure displacements were monitored through extensive instrumentation. The settlement response of the two buildings was found to differ significantly, demonstrating both flexible and rigid response mechanisms. Comparison of the building settlement profiles with greenfield settlements enables the soil structure interaction to be quantified. Encouraging agreement between the modification to the greenfield settlement profile displayed by buildings and estimates made from existing predictive tools is observed. Potential issues for infrastructure connected to buildings, arising from the embedment of rigid buildings into the soil, are also highlighted. © 2012 Taylor & Francis Group.

Student-t processes as alternatives to Gaussian processes

We investigate the Student-t process as an alternative to the Gaussian process as a non-parametric prior over functions. We derive closed form expressions for the marginal likelihood and predictive distribution of a Student-t process, by integrating away an inverse Wishart process prior over the co-variance kernel of a Gaussian process model. We show surprising equivalences between different hierarchical Gaussian process models leading to Student-t processes, and derive a new sampling scheme for the inverse Wishart process, which helps elucidate these equivalences. Overall, we show that a Student-t process can retain the attractive properties of a Gaussian process - a nonparamet-ric representation, analytic marginal and predictive distributions, and easy model selection through covariance kernels - but has enhanced flexibility, and predictive covariances that, unlike a Gaussian process, explicitly depend on the values of training observations. We verify empirically that a Student-t process is especially useful in situations where there are changes in covariance structure, or in applications such as Bayesian optimization, where accurate predictive covariances are critical for good performance. These advantages come at no additional computational cost over Gaussian processes.