Modelling of settlement induced building damage

This thesis focuses on the modelling of settlement induced damage to masonry buildings. In densely populated areas, the need for new space is nowadays producing a rapid increment of underground excavations. Due to the construction of new metro lines, tunnelling activity in urban areas is growing. One of the consequences is a greater attention to the risk of damage on existing structures. Thus, the assessment of potential damage of surface buildings has become an essential stage in the excavation projects in urban areas (Chapter 1). The current damage risk assessment procedure is based on strong simplifications, which not always lead to conservative results. Object of this thesis is the development of an improved damage classification system, which takes into account the parameters influencing the structural response to settlement, like the non-linear behaviour of masonry and the soil-structure interaction. The methodology used in this research is based on experimental and numerical modelling. The design and execution of an experimental benchmark test representative of the problem allows to identify the principal factors and mechanisms involved. The numerical simulations enable to generalize the results to a broader range of physical scenarios. The methodological choice is based on a critical review of the currently available procedures for the assessment of settlement-induced building damage (Chapter 2). A new experimental test on a 1/10th masonry façade with a rubber base interface is specifically designed to investigate the effect of soil-structure interaction on the tunnelling-induced damage (Chapter 3). The experimental results are used to validate a 2D semi-coupled finite element model for the simulation of the structural response (Chapter 4). The numerical approach, which includes a continuum cracking model for the masonry and a non-linear interface to simulate the soil-structure interaction, is then used to perform a sensitivity study on the effect of openings, material properties, initial damage, initial conditions, normal and shear behaviour of the base interface and applied settlement profile (Chapter 5). The results assess quantitatively the major role played by the normal stiffness of the soil-structure interaction and by the material parameters defining the quasi-brittle masonry behaviour. The limitation of the 2D modelling approach in simulating the progressive 3D displacement field induced by the excavation and the consequent torsional response of the building are overcome by the development of a 3D coupled model of building, foundation, soil and tunnel (Chapter 6). Following the same method applied to the 2D semi-coupled approach, the 3D model is validated through comparison with the monitoring data of a literature case study. The model is then used to carry out a series of parametric analyses on geometrical factors: the aspect ratio of horizontal building dimensions with respect to the tunnel axis direction, the presence of adjacent structures and the position and alignment of the building with respect to the excavation (Chapter 7). The results show the governing effect of the 3D building response, proving the relevance of 3D modelling. Finally, the results from the 2D and 3D parametric analyses are used to set the framework of an overall damage model which correlates the analysed structural features with the risk for the building of being damaged by a certain settlement (Chapter 8). This research therefore provides an increased experimental and numerical understanding of the building response to excavation-induced settlements, and sets the basis for an operational tool for the risk assessment of structural damage (Chapter 9).

Fixed-rank matrix factorizations and Riemannian low-rank optimization

Motivated by the problem of learning a linear regression model whose parameter is a large fixed-rank non-symmetric matrix, we consider the optimization of a smooth cost function defined on the set of fixed-rank matrices. We adopt the geometric framework of optimization on Riemannian quotient manifolds. We study the underlying geometries of several well-known fixed-rank matrix factorizations and then exploit the Riemannian quotient geometry of the search space in the design of a class of gradient descent and trust-region algorithms. The proposed algorithms generalize our previous results on fixed-rank symmetric positive semidefinite matrices, apply to a broad range of applications, scale to high-dimensional problems, and confer a geometric basis to recent contributions on the learning of fixed-rank non-symmetric matrices. We make connections with existing algorithms in the context of low-rank matrix completion and discuss the usefulness of the proposed framework. Numerical experiments suggest that the proposed algorithms compete with state-of-the-art algorithms and that manifold optimization offers an effective and versatile framework for the design of machine learning algorithms that learn a fixed-rank matrix. © 2013 Springer-Verlag Berlin Heidelberg.