Investigation of ANN Model Containing One Hidden Layer for Predicting Compressive Strength of Concrete with Blast-Furnace Slag and Fly Ash

Table 6

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

Reference	Machine learning algorithm	Input	Number of data samples	Performance measure
Han et al. [41]	ANN model	7 inputs: curing temperature, water to binder ratio, BFS to total binder ratio, water, fine aggregate, coarse aggregate, and superplasticizer	269	R^2 = 0.9610
Boukhatem et al. [27]	ANN model	5 inputs: cement, water-to-cement ratio, slag content, temperature, age of samples	726	R^2 = 0.9216
Kandiri et al. [28]	Hybridized multiobjective ANN and a multiobjective slap swarm algorithm (MOSSA), M5P model tree algorithm	7 inputs: cement, BFS, BFS grade, water, fine aggregate, coarse aggregate, age of samples	624	ANN-16, R^2 = 0.941; ANN-7: R^2 = 0.865; M5P: R = 0.884
Boğa et al. [29]	ANN model and the adaptive neuro-fuzzy inference system (ANFIS)	4 inputs: cure type, curing period, BFS ratio, CNI ratio	162	ANN: R^2 = 0.9710; ANFIS: R^2 = 0.665
Bilim et al. [30]	ANN model	6 inputs: cement, ground granulated blast-furnace slag, water, hyperplasticizer, aggregate, and age of samples	225	R^2 = 0.9600
Sarıdemir et al. [31]	ANN and fuzzy logic models ANFIS	5 inputs: age of samples, cement, BFS, water, and aggregate	284	ANN: R^2 = 0.981; FL: R^2 = 0.968
Bui et al. [44]	Modified firefly algorithm-artificial neural network (MFA-ANN)	8 input parameters: cement, BFS, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, and age of samples	1133	R^2 = 0.9025
Feng et al. [70]	AdaBoost algorithm	8 inputs: cement, BFS, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, age of samples	1030	AdaBoost: R^2 = 0.982; ANN: R^2 = 0.903; SVM: R^2 = 0.855
Behnood et al. [42]	M5P model tree algorithm	8 inputs: cement, blast-furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, age of sample	1912	R^2 = 0.900
Golafshani and Behnood [69]	Biogeography-based programming	8 inputs: cement, silica fume, water, coarse aggregate, fine aggregate, superplasticizer, maximum aggregate size, age of sample	1030	RMSE = 8.5389; MAE = 6.3882; BBP: R^2 = 0.8806
Dao et al. [43]	Gaussian process regression and ANN model	8 input parameters: cement, BFS, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, age of samples	1030	R^2 = 0.8930; RMSE = 5.46; MAE = 3.86
This paper	ANN model	8 inputs: cement, water, coarse aggregate or gravel, fine aggregate or sand, blast-furnace slag, fly ash, superplasticizer, age of samples	1274	R^2 = 0.9285; RMSE = 4.4266; MAE = 3.2971