Damage assessment automation for single storey detached masonry houses: a probabilistic approach

Assessing the existing condition of aging masonry houses are of high interest as the cost of retrofitting and repairing becomes significantly higher. Conventional condition assessment tools and methods for single storey detached masonry houses (SSDMH) are time-consuming, subjective, tedious, and sparse. This study aims to formulate a novel framework for assessing the condition of those houses by proposing a user-friendly, effective, and impartial model, for existing structures considering cracks in the masonry walls and the age of the house. This study adopted the bayesian belief network (BBN) method since the existing data on building assessment are subjective and consider multiple parameters. The application of the proposed model was formulated using wall cracks observed in a sample of thirty SSDMH. The Expectation Maximization (EM) algorithm was used to compute the conditional probabilities from the data set. The model was tested on ten houses for which the results were positive and validated with the Receiver Operating Characteristic (ROC) curve. However, the scope of the model is limited to SSDMH. Further development of this model may benefit the Surveyors, Engineers, and Architects to make informed decisions quickly by placing the structure at the correct severity level to decide on the renovation strategies.