Computers and Concrete

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Prediction of compressive strength of sustainable concrete using machine learning tools

  • Lokesh Choudhary (Department of Multidisciplinary Engineering, The NorthCap University) ;
  • Vaishali Sahu (Department of Multidisciplinary Engineering, The NorthCap University) ;
  • Archanaa Dongre (Department of Structural Engineering, Veermata Jijabai Technological Institute) ;
  • Aman Garg (Department of Multidisciplinary Engineering, The NorthCap University)
  • Received : 2023.01.19
  • Accepted : 2023.08.30
  • Published : 2024.02.25
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Abstract

The technique of experimentally determining concrete's compressive strength for a given mix design is time-consuming and difficult. The goal of the current work is to propose a best working predictive model based on different machine learning algorithms such as Gradient Boosting Machine (GBM), Stacked Ensemble (SE), Distributed Random Forest (DRF), Extremely Randomized Trees (XRT), Generalized Linear Model (GLM), and Deep Learning (DL) that can forecast the compressive strength of ternary geopolymer concrete mix without carrying out any experimental procedure. A geopolymer mix uses supplementary cementitious materials obtained as industrial by-products instead of cement. The input variables used for assessing the best machine learning algorithm not only include individual ingredient quantities, but molarity of the alkali activator and age of testing as well. Myriad statistical parameters used to measure the effectiveness of the models in forecasting the compressive strength of ternary geopolymer concrete mix, it has been found that GBM performs better than all other algorithms. A sensitivity analysis carried out towards the end of the study suggests that GBM model predicts results close to the experimental conditions with an accuracy between 95.6 % to 98.2 % for testing and training datasets.

Keywords

Acknowledgement

LC would like to acknowledge the contribution made by AG in the work carried out for the present study.

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