The artificial neural network (ANN) is a machine learning (ML) methodology that evolved and developed from the scheme of imitating the human brain. Artificial intelligence (AI) pyramid illustrates the evolution of ML approach to ANN and leading to deep learning (DL). Nowadays, researchers are very much attracted to DL processes due to its ability to overcome the selectivity-invariance problem. In this chapter, ANN has been explained by discussing the network topology and development parameters (number of nodes, number of hidden layers, learning rules and activated function). The basic concept of node and neutron has been explained, with the help of diagrams, leading to the ANN model and its operation. All the topics have been discussed in such a scheme to give the reader the basic concept and clarity in a sequential way from ANN perceptron model to deep learning models and underlying types.
Part of the book: Dynamic Data Assimilation
In an era with major developments in the energy sector, along with many benefits of energy consumption, it is also showing adverse effects on the end-users and the environment due to emission of various harmful gases mainly carbon dioxide (CO2). To deal with these issues, the zero energy building emerges to bring constructive developments through the construction industry. The concept of zero energy building is to develop a structural building which can generate its own required energy and have zero negative effects. The energy will be enough to fulfill all the requirements of the building operations and can save natural quarries. By increasing the numbers of zero energy buildings, major reforms can be brought in the construction industry and thus stabilizing the economy and the climate.
Part of the book: Sustainable Sewage Sludge Management and Resource Efficiency
Nowadays, the construction industry is on a fast track to adopting digital processes under the Industrial Revolution (IR) 4.0. The desire to automate maximum construction processes with less human interference has led the industry and research community to inclined towards artificial intelligence. This chapter has been themed on automated construction monitoring practices by adopting material classification via machine learning (ML) techniques. The study has been conducted by following the structure review approach to gain an understanding of the applications of ML techniques for construction progress assessment. Data were collected from the Web of Science (WoS) and Scopus databases, concluding 14 relevant studies. The literature review depicted the support vector machine (SVM) and artificial neural network (ANN) techniques as more effective than other ML techniques for material classification. The last section of this chapter includes a python-based ANN model for material classification. This ANN model has been tested for construction items (brick, wood, concrete block, and asphalt) for training and prediction. Moreover, the predictive ANN model results have been shared for the readers, along with the resources and open-source web links.
Part of the book: Deep Learning Applications