Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models
- Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Heraklion, GR, 14121 Athens (Greece)
- Department of Civil Engineering, National Institute of Technology Patna, Patna 800005, Bihar (India)
- Department of Civil and Structural Engineering, University of Sheffield, Sheffield (United Kingdom)
This study aims to implement a hybrid ensemble surrogate machine learning technique in predicting the compressive strength (CS) of concrete, an important parameter used for durability design and service life prediction of concrete structures in civil engineering projects. For this purpose, an experimental database consisting of 1030 records has been compiled from the machine learning repository of the University of California, Irvine. The database was used to train and validate four conventional machine learning (CML) models, namely Artificial Neural Network (ANN), Linear and Non-Linear Multivariate Adaptive Regression Splines (MARS-L and MARS-C), Gaussian Process Regression (GPR), and Minimax Probability Machine Regression (MPMR). Subsequently, the predicted outputs of CML models were combined and trained using ANN to construct the Hybrid Ensemble Model (HENSM). It is observed that the proposed HENSM produces higher predictive accuracy compared to the CML models used in the present study. The predictive performance of all models for CS prediction was compared using the testing dataset and it is found that the HENSM model attained the highest predictive accuracy in both phases. Based on the experimental results, the newly constructed HENSM model is very potential to be a new alternative in handling the overfitting issues of CML models and hence, can be used to predict the concrete CS, including the design of less polluting and more sustainable concrete constructions.
- OSTI ID:
- 23206271
- Journal Information:
- Cement and Concrete Research, Journal Name: Cement and Concrete Research Vol. 145; ISSN CCNRAI; ISSN 0008-8846
- Country of Publication:
- United States
- Language:
- English
Similar Records
Comparison of machine learning techniques to optimize the analysis of plutonium surrogate material via a portable LIBS device
Gaussian Process Regression for Aggregate Baseline Load Forecasting