Abstract
This work aims at proposing a novel procedure for the seismic assessment of historic masonry structures which is computationally efficient and does not rely on destructive material tests. Digital datasets describing the geometric configuration of historic masonry structures are employed to automatically generate a non-linear Finite Element (FE) model and investigate on possible collapse modes. A configuration of failure surfaces is therefore detected through the Control Surface Method (CSM), which is here proposed for the first time. In a following step of the analysis, structural macroblocks are identified, whereas an upper bound limit analysis approach is employed to estimate the structural capacity of the structure. Genetic Algorithms are also employed to detect the actual failure mode for the structure. The procedure is implemented into a visual coding environment, which allows one to parametrically explore all possible failure surfaces and immediately visualize the effects of the user assumptions. This is particularly suited to support a decisions-making process which strongly relay on engineering judgement. The procedure is validated by the analysis of two benchmark cases, whose results are presented and discussed.