Masonry is a construction material that has been used throughout the years as a structural or non-structural component in buildings. Masonry can be described as a composite material made up of different units and diverse types of arrangements, with or without mortar, that is used in many ancient public buildings, as well as with the latest technologies being applied in construction. Research in multiple relevant fields, as well as crossing structural with non-structural needs, is crucial for understanding the qualities of existent buildings and to develop new products and construction technologies. This book addresses and promotes the discussion related to the different topics addressing the use of masonry in the construction sciences and in practice, including theory and research, numerical approaches and technical applications in new works, and repair actions and interventions in the built environment, connecting theory and application across topics from academia to industry.

This title provides a thorough theoretical and practical introduction to the application of neural networks to pattern recognition and intelligent signal processing. It has been tested on students, unfamiliar with neural networks, who were able to pick up enough details to successfully complete their masters or final year undergraduate projects. The text also presents a comprehensive treatment of a class of neural networks called common bandwidth spherical basis function NNs, including the probabilistic NN, the modified probabilistic NN, and the general regression NN.

This book deals with all the tasks related to brick and stone masonry structures, from the initial identification of defects and their diagnosis to their treatment and monitoring of its cost-effectiveness. It is written in the context of bridges and their associated retaining walls in the U.K.