The study presents a machine learning-driven methodology for projecting the compressive strength of lightweight hemp-based blocks,framing as an ecologically sound replacement for standard construction resources.These ...The study presents a machine learning-driven methodology for projecting the compressive strength of lightweight hemp-based blocks,framing as an ecologically sound replacement for standard construction resources.These blocks are formulated using hemp hurd,lime,cement,glass powder,and water.Five advanced models,Random Forest,Gradient Boosting,Extreme Gradient Boosting,Categorical Boosting(CatBoost),and Artificial Neural Networks(ANNs),were rigorously developed and evaluated,using a well-structured data set.Critical preprocessing protocols encompassed normalization,feature selection,and exploratory data analysis.The model performance was evaluated using the metrics R^(2),RMSE,MAE,WMAPE,and Nash-Sutcliffe Efficiency.Visual tools such as regression plots,3-dimensional surface plots,Taylor diagrams,and Regression Error Characteristic curves were also employed.The results indicated that CatBoost outperformed ANN and other combined methods in generating accurate predictions.The amount of cement and the curing time were more influential than the amounts of lime and glass powder in the mixes.The results revealed that ensemble machine learning models uncovered nonlinear relationships,facilitating the prediction of biocomposite material performance.The approach supports sustainable construction by offering mix designers a scalable,data-driven alternative to the trial-and-error method.This technology reduces testing costs and enhances the accuracy of mix design optimization,thereby accelerating the adoption of hemp-based blocks in green building projects.展开更多
文摘The study presents a machine learning-driven methodology for projecting the compressive strength of lightweight hemp-based blocks,framing as an ecologically sound replacement for standard construction resources.These blocks are formulated using hemp hurd,lime,cement,glass powder,and water.Five advanced models,Random Forest,Gradient Boosting,Extreme Gradient Boosting,Categorical Boosting(CatBoost),and Artificial Neural Networks(ANNs),were rigorously developed and evaluated,using a well-structured data set.Critical preprocessing protocols encompassed normalization,feature selection,and exploratory data analysis.The model performance was evaluated using the metrics R^(2),RMSE,MAE,WMAPE,and Nash-Sutcliffe Efficiency.Visual tools such as regression plots,3-dimensional surface plots,Taylor diagrams,and Regression Error Characteristic curves were also employed.The results indicated that CatBoost outperformed ANN and other combined methods in generating accurate predictions.The amount of cement and the curing time were more influential than the amounts of lime and glass powder in the mixes.The results revealed that ensemble machine learning models uncovered nonlinear relationships,facilitating the prediction of biocomposite material performance.The approach supports sustainable construction by offering mix designers a scalable,data-driven alternative to the trial-and-error method.This technology reduces testing costs and enhances the accuracy of mix design optimization,thereby accelerating the adoption of hemp-based blocks in green building projects.
