Model for estimating construction waste generation in masonry building

Author:

Nagalli André1ORCID,de Carvalho Karina Querne2ORCID

Affiliation:

1. Professor, The Federal University of Technology – Paraná (UTFPR), Curitiba, Paraná, Brazil (corresponding author: )

2. Professor, The Federal University of Technology – Paraná (UTFPR), Curitiba, Paraná, Brazil

Abstract

The civil construction sector is an important generator of solid waste. Estimating the quantities of construction waste is crucial in the waste management process. In Brazil, although legislation has required that builders promote waste management since 2002, there are no specific generation models. This work describes a predictive waste generation model implemented with good results in Curitiba, Paraná, Brazil. Through the analysis of 48 buildings and their characteristics, including human and management aspects, a model is proposed to estimate the total volume of waste that is expected on a building site. The analysis considered the following parameters: work execution time, total constructed area, type of masonry, team training and monitoring. The model application revealed that the model is preferred to single or direct relations, such as those that the literature usually recommends. The model presented good results for projects with a total constructed area larger than 4000 m2. Finally, the average error associated with the prediction was 29·8%, which is considered acceptable due to the complexity of the phenomena and the lack of better models.

Publisher

Thomas Telford Ltd.

Subject

Waste Management and Disposal,Civil and Structural Engineering

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Glass powder from non-returnable bottles: Pozzolanic additive to mortar;Engineering & Technology Scientific Journal;2022-10-08

2. Electrical installations running through masonry walls: developing waste generation indicators for environmental performance analysis;Revista Brasileira de Ciências Ambientais;2022

3. Estimation of construction waste generation using machine learning;Proceedings of the Institution of Civil Engineers - Waste and Resource Management;2021-02

4. Editorial;Proceedings of the Institution of Civil Engineers - Waste and Resource Management;2019-02

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