Artificial neural network

Cost prediction, Professional Statement, 1st edition, published in November 2020 by the Royal Institution of Chartered Surveyors (RICS), defines an artificial neural network (ANN) as: ‘A computer system that is able to simulate the learning effect of the human brain, performing tasks involving incomplete data sets, fuzzy/incomplete information and for highly complex and ill-defined problems. For example, historical data can be used to train the ANN so that it ‘learns’ to predict costs.’

The 'CIOB Artificial Intelligence (AI) Playbook 2024', glossary of terms, published by the Chartered Institute of Building (CIOB) describes artificial neural network) as 'A computer structure inspired by the biological brain, consisting of a large set of interconnected computational units (‘neurons’) that are connected in layers. Data passes between these units as between neurons in a brain. Outputs of a previous layer are used as inputs for the next, and there can be hundreds of layers of units. An artificial neural network with more than three layers is considered a deep learning algorithm. Examples of artificial neural networks include Transformers or Generative adversarial networks.'

In June 2021, BSRIA published the white paper, 'Artificial Intelligence in Buildings (WP 14/2021)' written by Henry Lawson. It states: ‘In the context of AI, neural networks represent an attempt to emulate some of the processes of a biological neural network such as that found in a human brain. While artificial neural networks are nowhere near as complex as a human brain, they are particularly useful in identifying common traits between different ‘experiences’. For example, in building security, a human will normally fairly easily recognise a familiar person even with a different hairstyle, with or without glasses and with or without a suntan or in different lighting conditions. A neural network can help an AI system learn what the common points are that make it possible to identify all these as belonging to the same person. This is just one practical example of a neural network.’

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