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.

中文翻译：

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砌体抗压强度估算的人工神经网络实现

提出了一种用于估计砌体抗压强度的人工神经网络（ANN）。考虑使用水泥或非水泥砂浆的非均质样品，包括砖或石元素。设计了一个多层网络，其中的乙状神经元使用反向传播算法进行训练。开发了一个面向对象的Java软件程序，以便使用真实的测试数据来执行网络的训练和测试过程。平方误差的平均和（SSE）被用作网络的整体性能指标。将使用ANN获得的结果与实际测试数据和经验公式化结果进行了数值比较。比较结果表明，人工神经网络方法产生的SSE低于考虑的公式，在异质砖石样品和不同砖石系统上均具有良好的性能。当几乎没有信息可用时，所提出的方法可能特别有用，从而避免了对侵入式现场测试的需要，并且仅对砖（或石头）和灰浆进行了实验室测试。尽管所考虑的样本具有异质性，但人工神经网络能够以很小的误差预测抗压砌体强度。