What does this research mean for the field?

Integrating remote sensing and geographic information systems with a stacking ensemble machine learning model accurately predicts regional-level construction and demolition waste generation, with the number of residential units and buildings being the most critical predictive features. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To develop a predictive framework for regional-level construction and demolition waste generation by integrating remote sensing, GIS techniques, and machine learning models.

September 14, 2026International Journal of Construction Management

Prediction of construction and demolition waste using remote sensing-GIS integration and stacking ensemble learning

View Full Paper

Ask AI

Bookmark

Authors

NENehal Elshaboury WAWael M. AlMetwaly

Discussion

Member takes

Overview

Machine learning study demonstrates accurate regional construction waste prediction using geospatial data, suggesting viable strategies for urban circular economy planning.

Key Points

To develop a predictive framework for regional-level construction and demolition waste generation by integrating remote sensing, GIS techniques, and machine learning models.
Integrated remote sensing and geographic information system (GIS) techniques to construct a geospatial database of demographic, economic, environmental, and construction drivers.
Evaluated six machine learning algorithms—including extreme gradient boosting (XGB) and random forest (RF)—and combined the top performers into a stacking ensemble model.
Applied SHapley Additive exPlanations (SHAP) analysis to quantify feature importance and interpret the contribution of individual predictive drivers.
The stacking ensemble model combining XGB and RF outperformed standalone models, achieving an R2 of 0.93, EVS of 0.93, RMSE of 460,880.96, and MAE of 331,259.05.
SHAP feature importance analysis identified the number of residential units and the total count of buildings as the primary drivers of construction and demolition waste generation.

Cite This Study

Elshaboury et al. (2026) studied this question.

synapsesocial.com/papers/6aa7b3d00926e14a848b30ed https://doi.org/10.1080/15623599.2026.2710874

View Full Paper

Ask AI

Bookmark

September 14, 2026International Journal of Construction Management

Prediction of construction and demolition waste using remote sensing-GIS integration and stacking ensemble learning

View Full Paper

Ask AI

Bookmark

Authors

NENehal Elshaboury WAWael M. AlMetwaly

Discussion

Member takes

Overview

Machine learning study demonstrates accurate regional construction waste prediction using geospatial data, suggesting viable strategies for urban circular economy planning.

Key Points

To develop a predictive framework for regional-level construction and demolition waste generation by integrating remote sensing, GIS techniques, and machine learning models.
Integrated remote sensing and geographic information system (GIS) techniques to construct a geospatial database of demographic, economic, environmental, and construction drivers.
Evaluated six machine learning algorithms—including extreme gradient boosting (XGB) and random forest (RF)—and combined the top performers into a stacking ensemble model.
Applied SHapley Additive exPlanations (SHAP) analysis to quantify feature importance and interpret the contribution of individual predictive drivers.
The stacking ensemble model combining XGB and RF outperformed standalone models, achieving an R2 of 0.93, EVS of 0.93, RMSE of 460,880.96, and MAE of 331,259.05.
SHAP feature importance analysis identified the number of residential units and the total count of buildings as the primary drivers of construction and demolition waste generation.

Cite This Study

Elshaboury et al. (2026) studied this question.

synapsesocial.com/papers/6aa7b3d00926e14a848b30ed https://doi.org/10.1080/15623599.2026.2710874

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

View Full Paper

Ask AI

Bookmark