Development of an Optimal Machine Learning Model to Predict CO2 Emissions at the Building Demolition Stage DOI Creative Commons
Gi-Wook Cha,

Choon-Wook Park

Buildings, Год журнала: 2025, Номер 15(4), С. 526 - 526

Опубликована: Фев. 9, 2025

The construction industry accounts for approximately 28% of global CO2 emissions, and emission management at the building demolition stage is important achieving carbon neutrality goals. Systematic studies on stage, however, are still lacking. In this study, research development optimal machine learning (ML) models was conducted to predict emissions stage. were predicted by applying various ML algorithms (e.g., gradient boosting [GBM], decision tree, random forest), based information features equipment used demolition, as well energy consumption data. GBM selected a model with prediction performance. It exhibited very high accuracy R2 values 0.997, 0.983, 0.984 training, test, validation sets, respectively. also showed excellent results in generalization performance, it effectively learned data patterns without overfitting residual analysis mean absolute error (MAE) evaluation. found that such floor area, equipment, wall type, structure significantly affect area key factors. developed study can be support decision-making initial design evaluate sustainability, establish reduction strategies. enables efficient collection processing provides scalability analytical approaches compared existing life cycle assessment (LCA) approach. future, deemed necessary develop tools enable comprehensive through system boundary expansion.

Язык: Английский

Optimal Machine Learning Model to Predict Demolition Waste Generation for a Circular Economy DOI Open Access
Gi-Wook Cha,

Choon-Wook Park

Опубликована: Июнь 25, 2024

A suitable waste management strategy is crucial for a sustainable and efficient circular economy in the construction sector, requires precise data on volume of demolition (DW) gen-erated. Therefore, we developed an optimal machine learning (ML) model to forecast quantity recycling landfill based characteristics DW. dataset comprising infor-mation 150 buildings, equipment utilized, five types generated (i.e., recyclable mineral, combustible, specified, mix waste, minerals) was constructed. ML models were predict quantities such waste. Artificial neural network, decision tree, gradient boosting machine, k-nearest neighbors, linear regression, random forest (RF), support vector regression applied, derived via hyperparameter tuning. The RF demonstrated superior performance. In both validation test phases, “recyclable mineral waste” combustible achieved accuracies 0.987 0.972, re-spectively. metals” “landfill specified 0.953 0.858 or higher, respectively. Moreover, exhibited accuracy 0.984 higher. SHapley Additive exPlanations analysis highlighted floor area as primary input variable influencing type employed emerged another impacting wastes generated. can provide management, thereby facilitating decision-making process industry professionals.

Язык: Английский

Процитировано

4

Construction and Demolition Waste Generation Prediction by Using Artificial Neural Networks and Metaheuristic Algorithms DOI Creative Commons

Rabih Awad,

Cenk Budayan, Aslı Pelin Gürgün

и другие.

Buildings, Год журнала: 2024, Номер 14(11), С. 3695 - 3695

Опубликована: Ноя. 20, 2024

In the actual estimation of construction and demolition waste (C&DW), it is significantly relevant to effective management, design, planning at project stages, but lack reliable methods historical data prevents C&DW quantities for both short- long-term planning. To address this gap, study aims predict in projects more accurately by integrating gray wolf optimization algorithm (GWO) Archimedes (AOA) into an artificial neural network (ANN). This uses concerning work 200 real-life performed Gaza Strip. Different performance parameters, such as mean absolute error (MAE), square (MSE), root squared (RMSE), coefficient determination (R2), are used evaluate effectiveness models developed. The results have shown that AOA-ANN model outperforms other terms accuracy (R2 = 0.023728, MSE 0.00056304, RMSE MAE 0.0086648). Moreover, new hybrid yields accurate estimations with minimal input making process feasible.

Язык: Английский

Процитировано

4

Applying digital technologies in construction waste management for facilitating sustainability DOI
Wenbo Zhao, Jian Li Hao, Guobin Gong

и другие.

Journal of Environmental Management, Год журнала: 2024, Номер 373, С. 123560 - 123560

Опубликована: Дек. 5, 2024

Язык: Английский

Процитировано

3

Pioneering Zero Waste Technologies Within the Framework of Sustainable Progress DOI
Amar Prakash Garg, Monika Chaudhary

Advances in environmental engineering and green technologies book series, Год журнала: 2025, Номер unknown, С. 267 - 294

Опубликована: Янв. 16, 2025

Zero waste, as defined by the Waste International Alliance (ZWIA) refers to conservation of all resources means responsible production, consumption, reuse, and recovery products, packaging, materials without burning with no substantial discharges land, water, or air that threaten environment, human health, various other life forms. An estimated 11.2 billion metric tons solid waste is collected every year worldwide, approximately 5% overall greenhouse gas emissions are caused decomposition organic elements alone in environment. It projected production municipal garbage will increase from 2.3 2023 3.8 2050. The predicted global direct cost management 2020 was $252 billion, which be doubled If we don't find a solution quickly, it may become unfixable convert earth into “gas chamber.”. using AI-driven technologies sustainable because recycling plastic produces hazardous chemicals.

Язык: Английский

Процитировано

0

Development of an Optimal Machine Learning Model to Predict CO2 Emissions at the Building Demolition Stage DOI Creative Commons
Gi-Wook Cha,

Choon-Wook Park

Buildings, Год журнала: 2025, Номер 15(4), С. 526 - 526

Опубликована: Фев. 9, 2025

The construction industry accounts for approximately 28% of global CO2 emissions, and emission management at the building demolition stage is important achieving carbon neutrality goals. Systematic studies on stage, however, are still lacking. In this study, research development optimal machine learning (ML) models was conducted to predict emissions stage. were predicted by applying various ML algorithms (e.g., gradient boosting [GBM], decision tree, random forest), based information features equipment used demolition, as well energy consumption data. GBM selected a model with prediction performance. It exhibited very high accuracy R2 values 0.997, 0.983, 0.984 training, test, validation sets, respectively. also showed excellent results in generalization performance, it effectively learned data patterns without overfitting residual analysis mean absolute error (MAE) evaluation. found that such floor area, equipment, wall type, structure significantly affect area key factors. developed study can be support decision-making initial design evaluate sustainability, establish reduction strategies. enables efficient collection processing provides scalability analytical approaches compared existing life cycle assessment (LCA) approach. future, deemed necessary develop tools enable comprehensive through system boundary expansion.

Язык: Английский

Процитировано

0