Predictive modelling

Engineering

Curing (chemistry)

Geotechnical engineering

Materials science

Computer science

Silica fume

Cement

Superplasticizer

Composite material

Structural engineering

Artificial neural network

Machine learning

Compressive strength

Structures

Using soft computing methods could be of great interest in predicting the compressive strength of Ultra-High-Performance Fibre Reinforced Concrete (UHPFRC). Therefore, this study developed four soft computing techniques. The models are the Linear- relationship (LR), pure quadratic, M5P-tree (M5P), and artificial neural network (ANN). The models were trained and developed using 306 datasets comprising 11 input parameters, including the curing temperature (T), the water-to-cement ratio (w/c), silica fume (SF), cement content (C), fiber content (Fb), water (W), sand content (S), superplasticizer (SP), fiber aspect ratio (AR) and curing time (t). Experimental results were used and compared to the model performances to validate the developed models. The models were developed based on 192 training datasets, and the model's accuracy was checked using 41 testing datasets; the model's outcomes were validated using 32 experimental datasets. The results show a high prediction accuracy of the compressive strength of UHPFRC using ANN models. Based on the optimum developed ANN model, a closed-form equation is presented, proving to be a reliable and useful tool for researchers and, above all, for practicing engineers in compressive strength prediction.

Prediction of concrete materials compressive strength using surrogate models