Analysis of masonry arch bridges

In January 1999, the maximum axle weight increased from IN to 11.5t for the assessment of highway bridges and structures. At the same time, 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.