Structural Engineering Applications with Augmented Neural Networks
I‐Cheng Yeh · Computer-Aided Civil and Infrastructure Engineering · 1998
This paper presents an augmented neural network (ANN), a novel neural network architecture, and examines its efficiency and accuracy for structural engineering applications. The proposed architecture is that of a standard backpropagation neural network with augmented neurons, that is, logarithm neurons and exponent neurons are added to the network's input and output layers. The principles of augmented neural networks are (1) the augmented neurons are highly sensitive in the boundary domain, thereby facilitating construction of accurate mapping in the model's boundary domain, and (2) the network denotes each input variable with multiple input neurons, thus allowing a highly interactive function on hidden neurons to be easily formed. Therefore, the hidden neurons can more easily construct an accurate network output for a highly interactive mapping model. Experimental results demonstrate that the network's logarithm and exponent neurons provide a markedly enhanced network architecture capable of improving the network's performance for structural engineering applications.