This article, first, discusses the decision-making process, typically used by trained
engineers to assess failure modes of masonry buildings, and then, presents the
rule-based model, required to build a knowledge-based system for post-earthquake
damage assessment. The acquisition of the engineering knowledge and implementation
of the rule-based model lead to the developments of the knowledge-based system
LOG-IDEAH (Logic trees for Identification of Damage due to Earthquakes for Architectural
Heritage), a web-based tool, which assesses failure modes of masonry buildings
by interpreting both crack pattern and damage severity, recorded on site by visual
inspection. Assuming that failure modes detected by trained engineers for a sample
of buildings are the correct ones, these are used to validate the predictions made by
LOG-IDEAH. Prediction robustness of the proposed system is carried out by computing
Precision and Recall measures for failure modes, predicted for a set of buildings selected
in the city center of L’Aquila (Italy), damaged by an earthquake in 2009. To provide an
independent meaning of verification for LOG-IDEAH, random generations of outputs are
created to obtain baselines of failure modes for the same case study. For the baseline
output to be compatible and consistent with the observations on site, failure modes are
randomly generated with the same probability of occurrence as observed for the building
samples inspected in the city center of L’Aquila. The comparison between Precision and
Recall measures, calculated on the output, provided by LOG-IDEAH and predicted by
random generations, underlines that the proposed knowledge-based system has a high
ability to predict failure modes of masonry buildings, and has the potential to support
surveyors in post-earthquake assessments
