Novel Hybrid Intelligence Techniques in Engineering

doi:10.3390/books978-3-0365-7107-2

Download Url(s)

https://mdpi.com/books/pdfview/book/7091

Contributor(s)

Armaghani, Danial Jahed (editor)

Zhang, Yixia (editor)

Samui, Pijush (editor)

Elshafie, Ahmed Hussein Kamel Ahmed (editor)

Azizi, Aydin (editor)

Language

English

Show full item record

Abstract

The focus of this reprint is the development of novel intelligence techniques for solving various problems in engineering. These techniques, due to their ability to create complex relationships between dependent and independent variables, can be implemented in a faster and more reliable way. Such techniques utilise algorithms/approaches such as artificial neural networks, fuzzy logic, evolutionary theory, learning theory, and probabilistic theory, making them a suitable and useful fit for real-life complex problems. This reprint introduces the process of selecting, applying, and developing such techniques in different engineering designs and applications. In addition, the validation process of intelligence systems as an alternative is discussed in this reprint. Overall, this reprint forms an excellent introduction to these systems for engineers who are not familiar with them.

URI

https://directory.doabooks.org/handle/20.500.12854/99998

Keywords

confinement of concrete; CFST composite column; artificial intelligence; gene-expression programming; hybrid techniques; finite element method (FEM); imbalanced data; travel mode choice data; hybrid support vector machine-based model; rock excavation; soft computing; cutter life index; rock strength; brittleness; classification; slope stability; tree-based models; random forest; AdaBoost; decision tree; 3D bridge model; IFC-based bridge model; engineering document; document fragment; integrated operation; granular model; incremental granular model; interval-based fuzzy c-means clustering; coverage; specificity; performance index; piezocone; soil classification; fuzzy C-means clustering; neuro-fuzzy; scratch-resistant; hydrophobic; GPTES; transparent; sol–gel; blasting; ground vibration; PPV prediction; whale optimization algorithm; salp swarm optimizer; spread foundation; retaining structures; economic design; rock brittleness; linear genetic programming; bagged regression tree; lazy machine learning method; SCC; compressive strength; fly ash; statistical analysis; modeling; blockchain technology; intelligent technology; internet of vehicles; malicious nodes; identification algorithm; inverse analysis; hydraulic conductivities; Gray Wolf Optimizer; thermal conductivity; geothermal systems; gene expression programming (GEP); non-linear multivariable regression (NLMR); P-wave; porosity; backpropagation neural network; blast-induced ground vibration; Gaussian process regression; green campus; shared free-floating electric scooter; usage frequency prediction; battery electric; battery pack; energy performance; simulation; second life batteries; off-grid PV system; residential building; EV charging station; optimization; metaheuristic algorithms; streamflow forecasting; concrete; water-reducer contents; workability; slump retention; conceptual framework; crowd-machine hybrid interaction; design implications; hybrid intelligence; survey; taxonomy