Summary of Integrated Bim and Machine Learning System For Circularity Prediction Of Construction Demolition Waste, by Abdullahi Saka et al.
Integrated BIM and Machine Learning System for Circularity Prediction of Construction Demolition Waste
by Abdullahi Saka, Ridwan Taiwo, Nurudeen Saka, Benjamin Oluleye, Jamiu Dauda, Lukman Akanbi
First submitted to arxiv on: 20 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach to quantifying construction and demolition waste (C&DW) using machine learning (ML) models, specifically XGBoost, and variable modelling (VM). The study leverages a dataset of 2280 projects to develop a system for predicting quantities of recyclable and landfill materials from building demolitions. The ML model achieves high accuracy, with an R2 value of 0.9977 and a Mean Absolute Error of 5.0910 on the testing dataset. The integration with Building Information Modelling (BIM) enables better planning and management of C&DW, facilitating a circular economy in the industry. The study provides practical tools for implementation and contributes to empirical studies on pre-demolition auditing at the project level. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding a way to accurately predict how much waste will be generated during building demolitions. This is important because managing construction and demolition waste (C&DW) is crucial for the environment. Currently, there are limited studies on this topic, and most focus on construction waste instead of demolition waste. The researchers used machine learning models and data from 2280 projects to develop a system that can predict how much recyclable and non-recyclable materials will be generated during demolitions. This will help people plan and manage C&DW better, which is good for the environment. |
Keywords
» Artificial intelligence » Machine learning » Xgboost
