Application of computational limit analysis to soil-structure interaction in masonry arch bridges.

For the assessment of Masonry Arch Bridges (MAB), many structural and material
models have been applied, ranging from sophisticated non-linear finite element
analysis models to much simpler rigid-block limit analysis models. i.e. elastic and
plastic methods respectively. The application of elastic analysis to MAB suffers
many drawbacks since it requires full mechanical characterization of ancient masonry
structures. The mechanical characterization of ancient masonry is difficult
since these structures have typically undergone a century or more of environmental
deterioration and in many cases have been already subjected to extensive modification.
Also, sophisticated material models generally require specialized parameters
that are hard to assess, particularly if non-destructive tests are used. In these cases
practicing engineers typically favour simpler material models, involving fewer parameters.
Thus non-linear finite element methods or other sophisticated models
may not be a good choice for the assessment of MAB, while simplified approaches
for example based on limit analysis principles are likely to be more appropriate. In
this research. a holistic computational limit analysis procedure is presented which
involves modelling both soil and masonry components explicitly. Masonry bridge
parts are discretized using rigid blocks whilst the soil fill is discretized using deformable
triangular elements and modelled a.'i a Mohr-Coulomb material with a
tension cut-off. Lower and upper bound estimates of the collapse load are obtained.
Results are compared with those from recently performed bridge tests carried out
in collaboration with the University of Salford. A key project finding is that the
use of peak soil strength parameters in limit analysis models is inappropriate when
the soil is modelled explicitly. However, use of mobilized strengths appears to be a
promising way forward, yielding much closer correlation with experimental data.