Prieto, A J and Alarcón, L F (2023) Using fuzzy inference systems for lean management strategies in construction project delivery. Journal of Construction Engineering and Management, 149(9): 04023083, ISSN 0733-9364
Abstract
When using lean waste management in construction project delivery, computational methodologies are currently an innovative technology for the implementation of efficient and effective improvement strategies in the development of Industry 4.0 in Chile. Lean models are able to manage data obtained from construction projects along with the data obtained from the knowledge base of professional experts (expert survey). The waste management of construction projects under the lean philosophy requires cooperative efforts, where the opinion of professional experts is completely paramount to analyze multidisciplinary knowledge. Therefore, new protocols and disruptive procedures based on artificial intelligence (AI) tools can help decision makers prioritize activities, minimize uncertainty, and avoid wasteful actions that add no value to the project and thus can be minimized or completely eliminated. The vagueness of subjective human judgment in the degree of application of lean waste management in project delivery is modeled by a fuzzy logic model that includes additional considerations related to the lean implementation. Moreover, multiple linear regression analysis has been implemented in order to verify and validate the previous digital fuzzy model. In this sense, the main aim of this study is to develop new approaches regarding AI systems, using fuzzy sets and multiple linear regression for managing waste in construction project delivery in the metropolitan area of Santiago, Chile. A theorized application of the models reveals that the sample (100 construction projects) can be classified into three lean waste condition levels: high, medium, or low waste effects. The outcomes of this research will contribute to the Chilean construction industry environment and will open new ways for harnessing AI-based technology in the construction industry to the fullest potential, to achieve better time and cost predictability with a client- and end-user-centered world view.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | digital tools; fuzzy logic; lean construction; multiple linear regression; wastes management |
| Index terms: | strategy, survey, philosophy, construction project delivery, knowledge base, lean construction, fuzzy inference, Santiago, construction project, methodology, waste management, fuzzy set, industry 4.0, management strategy, fuzzy logic, judgment, Chile, implementation, construction industry, regression analysis, artificial intelligence, project delivery |
| Subjects: | waste management, dispute resolution, data collection methods, technology adoption, artificial intelligence, data science, contractual arrangements, project delivery, production management, Geography, management, research methods, decision-making and optimization, philosophical studies, industry analysis, information systems, statistical analysis, project controls, building construction |
| Topics: | Project Management, Geographical Context, Research Practice, Business Strategy, Procurement, Sustainability, Legal Issues, Digital Applications, Site Management, Time Control |
| Descriptive scope: | 5 PCTEA |
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