在现场规模上估算建筑垃圾产生量（CWG）对于有效的建筑垃圾管理（CWM）是一项至关重要但具有挑战性的任务。由于缺乏详细的CWG数据，现有的现场规模CWG建模方法在获得准确的结果方面面临困难，他们中的大多数没有考虑预测变量之间的复杂关系。本研究试图通过提出一种新颖的CWG建模方法来解决这一问题，该方法集成了改进的现场测量（IOM）和基于支持向量机（SVM）的预测模型。为了实现这一目标,对206个正在进行的商业建筑工地进行了调查，以获得五种废物的预测值和废物产生率（WGR）（即，无机非金属废物，有机废物,金属废料,复合垃圾，和危险废物)在三个施工阶段产生(即，下层结构阶段，上层建筑阶段，和完成阶段)。将数据引入SVM以发展预测变量与WGR之间的关系。正在建设中的实际商业建筑被用来证明所提出的方法的适用性。结果表明，IOM的优越性可以作为实现稳健的CWG数据收集的基础。此外,基于SVM的WGR预测模型(SWPM)比反向传播神经网络(R2=75.14%)和多元线性回归(R2=61.93%)能获得更准确的预测结果(R2=86.87%)。
Estimation of construction waste generation (CWG) at the field scale is a crucial but challenging task for effective construction waste management (CWM). Extant field-scale CWG modeling approaches have faced difficulties in obtaining accurate results due to a lack of detailed CWG data, and most of them fail to consider the complex relationship among predictive variables. This study attempts to tackle this issue by proposing a novel CWG modeling approach that integrates improved on-site measurement (IOM) and a support vector machine (SVM)-based prediction model. To achieve this goal, 206 ongoing commercial construction sites were investigated to obtain the predictor values and waste generation rates (WGRs) of five types of waste (i.e., inorganic nonmetallic waste, organic waste, metal waste, composite waste, and hazardous waste) generated at three construction stages (i.e., the understructure stage, superstructure stage, and finishing stage). The data were introduced to the SVM to develop the relationships between predictive variables and WGRs. An actual commercial building under construction was used to demonstrate the applicability of the proposed approach. The results showed that the superiority of the IOM can be used as a basis to implement robust CWG data collection. In addition, the SVM-based WGR prediction model (SWPM) can obtain more accurate prediction results (R2 = 86.87%) than the back-propagation neural network (R2 = 75.14%) and multiple linear regression (R2 = 61.93%).
PDF(Sci-hub)