TY - JOUR

T1 - High strength concrete compressive strength prediction using an evolutionary computational intelligence algorithm

AU - Jibril, M. M.

AU - Malami, Salim Idris

AU - Muhammad, U. J.

AU - Bashir, Abba

AU - Usman, A. G.

AU - Salami, Babatunde A.

AU - Rotimi, Abdulazeez

AU - Ibrahim, A. G.

AU - Abba, S. I.

N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

PY - 2023/6/14

Y1 - 2023/6/14

N2 - The most crucial mechanical property of concrete is compression strength (CS). Insufficient compressive strength can therefore result in severe failure, which can be beyond repair. Therefore, predicting concrete strength accurately and early is a key challenge for researchers and concrete designers. High-strength concrete (HSC) is an extremely complicated material, making it challenging to simulate its behavior. The CS of HSC was predicted in this research using an adaptive neuro-fuzzy inference system (ANFIS), backpropagation neural networks (BPNN), Gaussian process regression (GPR), and NARX neural network (NARX) in the initial case. In the second case, an ensemble model of k-nearest neighbor (k-NN) was proposed due to the poor performance of model combination M1 & M2 in ANFIS, BPNN, NARX, and M1 in GPR. The output variable is the 28-day CS (MPa), and the input variables are cement (Ce) Kg/m3, water (W) Kg/m3, superplasticizer (S) Kg/m3, coarse aggregate (CA) Kg/m3, and fine aggregate (FA) Kg/m3. The outcomes depict that the suggested approach is predictively consistent for forecasting the CS of HSC, to sum up. The MATLAB 2019a toolkit was employed to generate the ML learning models (ANFIS, BPNN, GPR, and NARX), whereas E-Views 11.0 was used for pre- and post-processing of the data, respectively. The BPNN and NARX algorithm was trained and validated using MATLAB ML toolbox. The outcome shows that the combination M3 partakes in the preeminent performance evaluation criterion when associated with the other models, where ANFIS-M3 prediction outperforms all other models with NSE, R 2, R = 1, and MAPE = 0.261 & 0.006 in both the calibration and verification phases, correspondingly, in the first case. In contrast, the ensemble of BPNN and GPR surpasses all other models in the second scenario, with NSE, R 2, R = 1, and MAPE = 0.000, in both calibration and verification phases. Comparisons of total performance showed that the proposed models can be a valuable tool for predicting the CS of HSC.

AB - The most crucial mechanical property of concrete is compression strength (CS). Insufficient compressive strength can therefore result in severe failure, which can be beyond repair. Therefore, predicting concrete strength accurately and early is a key challenge for researchers and concrete designers. High-strength concrete (HSC) is an extremely complicated material, making it challenging to simulate its behavior. The CS of HSC was predicted in this research using an adaptive neuro-fuzzy inference system (ANFIS), backpropagation neural networks (BPNN), Gaussian process regression (GPR), and NARX neural network (NARX) in the initial case. In the second case, an ensemble model of k-nearest neighbor (k-NN) was proposed due to the poor performance of model combination M1 & M2 in ANFIS, BPNN, NARX, and M1 in GPR. The output variable is the 28-day CS (MPa), and the input variables are cement (Ce) Kg/m3, water (W) Kg/m3, superplasticizer (S) Kg/m3, coarse aggregate (CA) Kg/m3, and fine aggregate (FA) Kg/m3. The outcomes depict that the suggested approach is predictively consistent for forecasting the CS of HSC, to sum up. The MATLAB 2019a toolkit was employed to generate the ML learning models (ANFIS, BPNN, GPR, and NARX), whereas E-Views 11.0 was used for pre- and post-processing of the data, respectively. The BPNN and NARX algorithm was trained and validated using MATLAB ML toolbox. The outcome shows that the combination M3 partakes in the preeminent performance evaluation criterion when associated with the other models, where ANFIS-M3 prediction outperforms all other models with NSE, R 2, R = 1, and MAPE = 0.261 & 0.006 in both the calibration and verification phases, correspondingly, in the first case. In contrast, the ensemble of BPNN and GPR surpasses all other models in the second scenario, with NSE, R 2, R = 1, and MAPE = 0.000, in both calibration and verification phases. Comparisons of total performance showed that the proposed models can be a valuable tool for predicting the CS of HSC.

KW - Adaptive neuro-fuzzy inference system

KW - Backpropagation neural networks

KW - Gaussian process regression

KW - High-strength concrete

KW - NARX neural network

UR - http://www.scopus.com/inward/record.url?scp=85163130414&partnerID=8YFLogxK

U2 - 10.1007/s42107-023-00746-7

DO - 10.1007/s42107-023-00746-7

M3 - Article

AN - SCOPUS:85163130414

SN - 1563-0854

VL - 24

SP - 3727

EP - 3741

JO - Asian Journal of Civil Engineering

JF - Asian Journal of Civil Engineering

IS - 8

ER -