刘景矿1，滕 岳1，巩恩沁2
（1.广州大学工商管理学院，广州510006；2.华南理工大学土木与交通学院，广州510640）
摘 要：随着我国城镇化进程的深入，以及各地城市更新改造产生了大量建筑废弃物。建筑废弃物减量化管理就显得尤为重要，已成为相关学者的研究热点。本文通过文献综述法，以系统的观点梳理了近年来国内外学者的相关研究。综述的主要内容包括建筑废弃物产量估算和建筑废弃物管理，其中建筑废弃物管理分为管理内容与管理方法，管理内容包括了建筑废弃物“4R”管理、建筑废弃物生命周期管理等，管理方法包括了系统动力学仿真和大数据应用。最后进行文献评述，并且认为大数据、人工智能等技术应用于建筑废弃物全生命周期管理是趋势所在。该研究有助于加强行业人员对建筑废弃物减量化管理的深度认识，以及为学者提供全面系统的研究视角。
关键词：城镇化；城市更新改造；减量化管理；大数据；人工智能
中图分类号：X799.1 文献标识码：A
Trend of the Research on Reduction M anagement of Construction W aste：A Systematic Perspective(www.xing528.com)
LIU Jingkuɑng，TENG Yue，GONG Enqing
（1.School of Management Guangzhou University，Guangzhou 510006；2.School of Civil Engineering and Transportation，South China University of Technology，Guangzhou 510640）
Abstract：With the deepening process of urbanization in China and the renewal and transformation of various cities，a large number of construction wastes are produced.Reduction management of construction waste is particularly important，and has become a research hotspot of relevant scholars.Through the literature review，this paper combs the relevant research of domestic and foreign scholars in recent years from a systematic point of view.The main contents of the review include the estimation of the output of construction waste and themanagement of construction waste.The management of construction waste is divided into management content and management method.The management content includes“4R”management of construction waste and life cyclemanagement of construction waste.The management method includes system dynamics simulation and large data application.Finally，the literature review ismade，and it is considered that the application of big data，artificial intelligence and other technologies to the life cycle management of construction waste is the trend.This study will help to strengthen the industry personnel’s in-depth understanding of the management of building waste reduction，and provide a comprehensive and systematic research perspective for scholars.
Key words：Urbanization；Urban renewal and transformation；Reduction management；Big data；Artificial intelligence
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