Abstract：
Data is needed for making informed decisions regarding managing waste in the time of construction and demolition phases of buildings. However, data availability is very limited in most developing countries in the area of waste generation. The objective of this study is to employ an artificial intelligence (AI)-based approach to develop a reliable model for forecasting monthly construction and demolition waste (C&DW) generation in the case study of Tehran, Iran. We have trained different prediction models using various AI algorithms, including multilayer perceptron neural network, radial basis function neural network, support vector machines, and adaptive neuro-fuzzy inference system (ANFIS). According to the findings, all employed AI algorithms demonstrated high prediction performance for C&DW forecasting models. The ANFIS model, with R2 = 0.96 and RMSE = 0.04209, was identified as the model that better represented the observed values of C&DW generation. The better efficiency of the ANFIS model could be due to its effective enhancement of neural networks to model subjective variables based on fuzzy logic capabilities. The developed prediction model can be employed as an efficient tool for policy and decision-making for C&DW management by predicting waste quantities in the future.
摘要：
在建筑物的建造和拆除阶段，需要数据来做出有关管理废物的明智决策。然而,在大多数发展中国家，废物产生领域的数据可用性非常有限。这项研究的目的是采用基于人工智能（AI）的方法来开发可靠的模型，以预测德黑兰案例研究中的每月建筑和拆除废物（C＆DW）生成。伊朗。我们使用各种AI算法训练了不同的预测模型，包括多层感知器神经网络，径向基函数神经网络，支持向量机，和自适应神经模糊推理系统(ANFIS)。根据调查结果,所有采用的人工智能算法对C&DW预测模型都表现出很高的预测性能。ANFIS模型,R2=0.96和RMSE=0.04209，被确定为更好地代表C和DW代的观察值的模型。ANFIS模型的更好效率可能是由于其有效增强了神经网络以基于模糊逻辑能力对主观变量进行建模。通过预测未来的废物数量，可以将开发的预测模型用作C＆DW管理的政策和决策的有效工具。
