Prediction Compressive Strength of Concrete Containing GGBFS using Random Forest Model

Table 4

Comparison of different machine learning models for predicting compressive strength of concrete.

Reference	Machine learning algorithm	Input	Number of data	Performance measure
Saridemir et al. [38]	ANN and fuzzy logic models ANFIS	5 inputs: TA, C, GGBFS, W, and Agg.	284	R = 0.9904
Bilim et al. [33]	ANN model	6 inputs: C, GGBFS, W, SP, Agg., and TA	225	R = 0.9798
Kandiri et al. [30]	Hybridized multiobjective ANN and a multiobjective slap swarm algorithm (MOSSA)/the M5P model tree algorithm	7 inputs: C, GGBFS, GGBFS grade (SG), W, fine Agg., coarse Agg., and TA	624	R = 0.9700
Han et al. [28]	ANN model	7 inputs: curing temperature, W/binder, GGBFS/total binder, W, fine Agg., coarse Agg., SP	269	R = 0.9803
Boukhatem et al. [29]	ANN model	5 inputs: C, W/C, GGBFS, temperature, TA	726	R = 0.9600
Boğa et al. [31]	ANN model and the adaptive neuro-fuzzy inference system (ANFIS)	4 inputs: cure type, curing period, BFS ratio, CNI ratio	162	R = 0.9854
This work	RF model	8 inputs: C, W, coarse Agg. or gravel, fine Agg. or sand, GGBFS, FA, SP, TA	453	R = 0.9729, MSE = 4.9585, MAE = 3.9423

C: cement; GGBFS: ground granulated blast furnace slag; W: water; SP: superplasticizer; TA: age of samples; FA: fly ash; Agg.: aggregate.