Artificial neural network implementation for masonry compressive strength estimation

An artificial neural network (ANN) implementation for the estimation of masonry compressive strength is presented. A heterogeneous sample is considered, including brick or stone elements, with cementitious or non-cementitious mortar. A multi-layer network was designed with sigmoidal neurons trained using a back-propagation algorithm. An object-oriented Java software program was developed in order to perform the training and the testing processes of the network, using real test data. The mean sum of square errors (SSE) was used as a global performance indicator of the network. The results obtained using the ANN were numerically compared with both real test data and with the results of empirical formulations. The comparisons showed that the ANN approach produced lower SSE than the considered formulations, with good performance on both heterogeneous masonry samples and different masonry systems. The presented approach could be particularly useful when little information is available, avoiding the need for invasive on-site tests and performing only laboratory tests on the brick (or stone) and the mortar. The ANN was able to predict the compressive masonry strength with a very small error, despite the heterogeneity of the considered sample.