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Computers and Concrete Volume 33, Number 2, February 2024 , pages 137-145 DOI: https://doi.org/10.12989/cac.2024.33.2.137 |
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Prediction of compressive strength of sustainable concrete using machine learning tools |
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Lokesh Choudhary, Vaishali Sahu, Archanaa Dongre and Aman Garg
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| Abstract | ||
| The technique of experimentally determining concrete's compressive strength for a given mix design is timeconsuming 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. | ||
| Key Words | ||
| compressive strength prediction; GBM; machine learning; sensitivity analysis; ternary geopolymer concrete | ||
| Address | ||
| Lokesh Choudhary and Vaishali Sahu: Department of Multidisciplinary Engineering, The NorthCap University, Sector- 23A, Gurugram-122017, Haryana, India Archanaa Dongre: Department of Structural Engineering, Veermata Jijabai Technological Institute, HR Mahajani Road, Matunga, Mumbai-400019, Maharashtra, India Aman Garg: 1) Department of Multidisciplinary Engineering, The NorthCap University, Sector- 23A, Gurugram-122017, Haryana, India, 2) State Key Laboratory of Intelligent Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China | ||