Hybrid data-driven approaches to predicting the compressive strength of ultra-high-performance concrete using SHAP and PDP analyses
Shahjalal University of Science and Technology · Leading University · +3 more institutions
Abstract
Ultra-high-performance concrete (UHPC) is a cutting-edge and advanced constructions material known for its exceptional mechanical properties and durability. Recently, machine learning (ML) methods play a pivotal role in predicting the compressive strength (CS) of UHPC and evaluating the dominant input parameters for a suitable mix design. This research, three hybrid machine learning models were utilized: Random Forest (RF), AdaBoost (AB), and Gradient Boosting (GB) algorithms with particle swarm optimization (PSO), namely AB-PSO, RF-PSO, and GB-PSO, to predict compressive strength and perform SHAP (Shapley additive explanation) analysis. To build predictive hybrid ML models, a dataset of 810 experimental data…
Citation impact
- FWCI
- 29.74
- Percentile
- 100%
- References
- 118
Authors
6- AKAbul KashemCorresponding
Shahjalal University of Science and Technology, Leading University
- RKRezaul Karim
Khulna University of Engineering and Technology
- SCSomir Chandra Malo
Pabna University of Science and Technology
- PDPobithra Das
Leading University
- SDShuvo Dip Datta
Khulna University of Engineering and Technology
Topics & keywords
- Compressive strength
- Predictive modelling
- Particle swarm optimization
- Random forest
- Boosting (machine learning)
- Computer science
- AdaBoost
- Silica fume