Deep learning model for Demolition Waste Prediction in a circular economy
Deep learning model for Demolition Waste Prediction in a circular economy
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DOI：
10.1016/j.jclepro.2020.122843
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发表时间：
2020-11-20
影响因子：
11.1
通讯作者：
Salami, Rafiu O.
中科院分区：
文献类型：
--
作者：
Akanbi, Lukman A.;Oyedele, Ahmed O.;Salami, Rafiu O.
成功的循环经济的一个基本要求是材料的持续使用。在建筑物寿命结束时规划建筑材料的再利用通常是一项困难的任务，因为通常只有有限的时间可用于建筑物拆除和材料回收。在这项研究中，开发了深度学习模型，用于预测在拆除之前从建筑物中可获得的打捞和废弃材料的数量（以吨为单位）。用于深度神经网络模型开发的数据集是从英国拆除行业从业者获得的2280个建筑物拆除记录中提取的。数据以8：1：1的比例划分为训练、测试和验证数据集。深度学习模型是在R编程环境中使用深度学习框架开发的。三个深度学习模型的平均R平方值为0.97，平均绝对误差在17.93和19.04之间。该模型进行了评估与案例研究建筑设计的四个场景。评估结果表明，鉴于建筑物的基本特征，可以高度准确地预测拆除后从建筑物中回收的材料数量。开发的模型将在拆除前审计工作中为拆除工程师和废物管理规划人员提供决策支持功能。(C)2020爱思唯尔有限公司版权所有。
An essential requirement for a successful circular economy is the continuous use of materials. Planning for building materials reuse at the end-of-life of buildings is usually a difficult task because limited time are usually made available for building removal and materials recovery. In this study, deep learning models were developed for predicting the amount (in tons) of salvage and waste materials that are obtainable from buildings at the end-of-life prior to demolition. Datasets used for deep neural network model developments were extracted from 2280 building demolition records obtained from the practitioners in the UK Demolition Industry. The data was partitioned into training, testing and validation datasets in the ratio 8:1:1. Deep learning models were developed with a deep learning framework in R programming environment. The average R-squared value for the three deep learning models is 0.97 with Mean Absolute Error between 17.93 and 19.04. The models were evaluated with four scenarios of a case study building design. The results of the evaluation show that, given basic features of buildings, it is possible to predict with a high level of accuracy, the amount of materials that would be recovered from a building after demolition. The models developed will provide decision support functionalities to demolition engineers and waste management planners during the pre-demolition audit exercise. (C) 2020 Elsevier Ltd. All rights reserved.