Holdings: Boosting-based ensemble machine learning models for predicting unconfined compressive strength of geopolymer stabilized clayey soil

Boosting-based ensemble machine learning models for predicting unconfined compressive strength of geopolymer stabilized clayey soil

The present research employs new boosting-based ensemble machine learning models i.e., gradient boosting (GB) and adaptive boosting (AdaBoost) to predict the unconfined compressive strength (UCS) of geopolymer stabilized clayey soil. The GB and AdaBoost models were developed and validated using 270...

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Bibliographic Details
Main Authors:	Abdullah G.M.S., Ahmad M., Babur M., Badshah M.U., Al-Mansob R.A., Gamil Y., Fawad M.
Other Authors:	56606096100
Format:	Article
Published:	Nature Research 2025
Subjects:	alkali sodium hydroxide article artificial neural network compressive strength data base fly ash furnace human machine learning mean absolute error mean squared error random forest reliability root mean squared error sensitivity analysis slag soil
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Boosting-based ensemble machine learning models for predicting unconfined compressive strength of geopolymer stabilized clayey soil

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