Edinburgh Napier University

  In January 1999, the maximum axle weight increased from IN to 11.5t for the
assessmenot f highway bridges and structures. At the samet ime, the maximum vehicle
weight also increased from 38t to 44t. Highway authorities are urgently searching for a
more refined assessmenmt ethod to predict the behaviour of masonry arch bridges.
LUSAS finite element analysis was used to study the behaviour of masonry arch
bridges. Load versus deflection curves and collapse loads are given for some of the full
and large scale arches previously tested to collapse. A parametric study was also
performed to determine the influence of the arch material properties and the load
dispersal angle: the arch tensile strength and the load dispersal angle were found to
have the most significant influence on the collapse load predictions.
Repeatability tests were carried out by building three nominally identical large scale
arch bridges in the laboratory and testing them to collapse. The first, second and third
arches collapsed at 2lkNm', 16kNm', and 25kNm 1 respectively. Finite element
analysis predicted a range of 18kNm' to 39kNm 1 for the same arches. This led to an
examination of a statistical, risk based, approach to bridge assessment.
Two novel risk assessment programs were developed by integrating Monte Carlo
simulation with the MEXE and the mechanism methods. Statistical information about
the predicted collapse load and allowable axle load is given. These risk assessment
tools are offered for incorporation within routine assessmenmt ethods. Their principal
benefit lies in providing engineers with a feel for the reliability of their analyses.
A modification has been made to the mechanism method by considering arch
deflection. A mechanism prediction is accurate only when all the forces and their
positions are accurately located. The modified mechanism method was used to analyse
some of the full scale arch bridges, previously tested to collapse, which revealed that
arch deflections had a significant influence on the collapse load prediction.