Using Fuzzy Inference Systems for Lean Management Strategies in Construction Project Delivery (Record no. 814297)

MARC details
000 -LEADER
fixed length control field 02834aab a2200217 4500
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 231018b20232023|||mr||| |||| 00| 0 eng d
022 ## - INTERNATIONAL STANDARD SERIAL NUMBER
International Standard Serial Number 0733-9364
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Prieto, A.J.
9 (RLIN) 878773
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Alarcon, L.F.
9 (RLIN) 878774
245 ## - TITLE STATEMENT
Title Using Fuzzy Inference Systems for Lean Management Strategies in Construction Project Delivery
300 ## - PHYSICAL DESCRIPTION
Extent 1-15 p.
520 ## - SUMMARY, ETC.
Summary, etc. 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.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Digital Tools
9 (RLIN) 878775
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Lean Construction (LC)
9 (RLIN) 878776
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Multiple Linear Regression (MLR)
9 (RLIN) 878777
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Fuzzy Logic
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Wastes Management
9 (RLIN) 878778
773 0# - HOST ITEM ENTRY
Place, publisher, and date of publication Reston,Virginia, U.S.A : American Society of Civil Engineers/ American Concrete Institute
International Standard Serial Number 07339364
Title ASCE: Journal of Construction Engineering and Management
856 ## - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://doi.org/10.1061/JCEMD4.COENG-12922">https://doi.org/10.1061/JCEMD4.COENG-12922</a>
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Suppress in OPAC No
Koha item type Articles
-- 14993
-- Mr. Muhammad Rafique Al Haj Rajab Ali (Late)
Holdings
Not for loan Home library Serial Enumeration / chronology Total Checkouts Date last seen Koha item type
  Periodical Section Vol. 149, No.9 (September 2023)   18/10/2023 Articles