Cloud Model-Based Intelligent Construction Management Level Assessment of Prefabricated Building Projects
Author(s): |
Hongda An
Lei Jiang Xingwen Chen Yunli Gao Qingchun Wang |
---|---|
Medium: | journal article |
Language(s): | English |
Published in: | Buildings, 8 October 2024, n. 10, v. 14 |
Page(s): | 3242 |
DOI: | 10.3390/buildings14103242 |
Abstract: |
Intelligent construction is vital for achieving new building industrialization by enhancing prefabricated buildings through integrated, digital, and intelligent management across production and construction processes. Despite its significance, detailed research on evaluating the intelligent construction management (ICM) level of prefabricated projects remains limited. This study aims to develop a comprehensive, multi-level, multi-dimensional ICM assessment system. By reviewing the literature, engaging in expert discussions, and conducting case studies—specifically using a project in Guangzhou as an example—this study employs the Analytic Hierarchy Process (AHP) and entropy weight methods to assign indicator weights. Utilizing cloud model theory, it establishes evaluation standards for intelligent construction management. This model identifies the project’s ICM level, suggests practical improvement methods, and validates its applicability. This work not only advances theoretical understanding but also provides a practical framework for assessing ICM levels in prefabricated projects, thus contributing significantly to the field by offering new research perspectives and empirical evidence. |
Copyright: | © 2024 by the authors; licensee MDPI, Basel, Switzerland. |
License: | This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met. |
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data sheet - Reference-ID
10804584 - Published on:
10/11/2024 - Last updated on:
10/11/2024