Constructability knowledge acquisition: A machine learning approach

Lueprasert, K (1996) Constructability knowledge acquisition: A machine learning approach. PhD thesis, Purdue University, USA.

Abstract

Project constructability can be devaluated significantly because of poor structural design decisions. However, the aspects of structural design decisions in constructability have not been thoroughly emphasized in the constructability concepts currently applied in the industry. This research proposes a methodology to acquire constructability knowledge according to structural design decisions made during conceptual phase. Constructability is understood as "an important feature of a structural design and construction project site conditions which determines the level of complexity of executing the associated structural assembly task". Constructability knowledge is acquired from structural design data of building structures, proposed construction methods, and resource availability conditions. Determining constructability of a project requires experience and expertise, which may not be available. A inductive learning system is proposed as an alternative knowledge acquisition tool. The system is capable of knowledge acquisition and generating desired concepts from classified constructability examples. Three methods for; (1) the preparation of constructability examples; (2) the constructability knowledge acquisition; and (3) the verification and validation of acquired knowledge, were proposed to develop such a learning system for constructability knowledge acquisition. Constructability knowledge is acquired in form of decision rules, and can be updated by implementing multistage knowledge acquisition process. Direct data extraction is proposed to extract structural design data from design drawings in CAD. Additional information necessary to the knowledge acquisition can be obtained from preliminary project plan and proposal. Acquired constructability knowledge can be used for future applications in the constructability domain, e.g.identifying potential structural design problems to improve overall project's constructability.

Item Type: Thesis (Doctoral)
Thesis advisor: Skibniewski, M J
Uncontrolled Keywords: complexity; construction project; constructability; construction method; design decision; learning; structural design
Index terms: machine learning, structural design, complexity, design decision, construction project, methodology, validation, construction method, proposal, constructability, knowledge acquisition, design drawing
Subjects: technical documentation, artificial intelligence, project planning, architectural engineering, building construction, student development, systems engineering, construction integration, research methods, design practice, professional development, production management
Topics: Education, Site Management, Digital Applications, Design Practice, Information Management, Engineering Principles, Research Practice, Project Management
Descriptive scope: 3 PCT

N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here