
Integrating machine learning with QFD for selecting functional requirements in construction automation
Author(s) -
Edgar C. Tamayo,
Yasir Imtiaz Khan,
Mohamed AlHussein,
Ahmed Jawad Qureshi
Publication year - 2020
Publication title -
international journal of industrialized construction
Language(s) - English
Resource type - Journals
ISSN - 2563-5034
DOI - 10.29173/ijic235
Subject(s) - functional requirement , quality function deployment , computer science , non functional requirement , artificial intelligence , requirements analysis , software deployment , machine learning , function (biology) , systems engineering , software engineering , engineering , software development , software , operations management , software construction , value engineering , programming language , evolutionary biology , biology
An important aspect of the conceptual design is at the customer requirement definition stage, where an optimal number of functional requirements are specified with the application of quality function deployment. To facilitate a systematic specification of functional requirements, state-of-the-art unsupervised machine learning techniques will be introduced in the feature selection of functional requirements. However, the scarcity of references on unsupervised feature selection in the literature reflects the difficulty associated with this topic. At the customer requirement definition phase, three techniques will be proposed for selecting functional requirements, namely: (a) principal component analysis, (b) forward orthogonal search, and (c) Kohonen self-organizing map neural network. These machine learning feature selection techniques address the limitations of current approaches in systematically determining the minimum functional requirements from the mapping of customer requirements in quality function deployment. When applied to the conceptual design of the transportable automated wood wall framing machine that is under development at the University of Alberta, the proposed feature selection techniques have been observed to be: (i) fast, (ii) amenable to small quality function deployment dataset, and (iii) adequate in realizing design objectives. The results presented in this paper can be easily extended to online determination of customer requirements and functional requirements, project management, contract management, and marketing.