Novel hybrid models of ANFIS and metaheuristic optimizations (SCE and ABC) for prediction of compressive strength of concrete using rebound hammer field test

doi:10.1007/s11709-022-0846-9

Front. Struct. Civ. Eng.

2022, Vol. 16

Issue (8) : 1003-1016 https://doi.org/10.1007/s11709-022-0846-9

RESEARCH ARTICLE

Novel hybrid models of ANFIS and metaheuristic optimizations (SCE and ABC) for prediction of compressive strength of concrete using rebound hammer field test

Dung Quang VU¹, Fazal E. JALAL², Mudassir IQBAL³, Dam Duc NGUYEN¹, Duong Kien TRONG¹, Indra PRAKASH⁴, Binh Thai PHAM¹(

)

¹. University of Transport Technology, Hanoi 100000, Vietnam
². Department of Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
³. Department of Civil Engineering, University of Engineering and Technology, Peshawar 25120, Pakistan
⁴. DDG (R) Geological Survey of India, Gandhinagar 382010, India

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Abstract

In this study, we developed novel hybrid models namely Adaptive Neuro Fuzzy Inference System (ANFIS) optimized by Shuffled Complex Evolution (SCE) on the one hand and ANFIS with Artificial Bee Colony (ABC) on the other hand. These were used to predict compressive strength (Cs) of concrete relating to thirteen concrete-strength affecting parameters which are easy to determine in the laboratory. Field and laboratory tests data of 108 structural elements of 18 concrete bridges of the Ha Long-Van Don Expressway, Vietnam were considered. The dataset was randomly divided into a 70:30 ratio, for training (70%) and testing (30%) of the hybrid models. Performance of the developed fuzzy metaheuristic models was evaluated using standard statistical metrics: Correlation Coefficient (R), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results showed that both of the novel models depict close agreement between experimental and predicted results. However, the ANFIS-ABC model reflected better convergence of the results and better performance compared to that of ANFIS-SCE in the prediction of the concrete Cs. Thus, the ANFIS-ABC model can be used for the quick and accurate estimation of compressive strength of concrete based on easily determined parameters for the design of civil engineering structures including bridges.

Keywords shuffled complex evolution artificial bee colony ANFIS concrete compressive strength Vietnam

Corresponding Author(s): Binh Thai PHAM

Just Accepted Date: 23 August 2022 Online First Date: 31 October 2022 Issue Date: 02 December 2022

Cite this article:

Dung Quang VU,Fazal E. JALAL,Mudassir IQBAL, et al. Novel hybrid models of ANFIS and metaheuristic optimizations (SCE and ABC) for prediction of compressive strength of concrete using rebound hammer field test[J]. Front. Struct. Civ. Eng., 2022, 16(8): 1003-1016.

URL:

https://academic.hep.com.cn/fsce/EN/10.1007/s11709-022-0846-9
https://academic.hep.com.cn/fsce/EN/Y2022/V16/I8/1003

Fig.1 Photos of the study sites.

Fig.2 Rebound Hammer test at Troi River Bridge.

Fig.3 Concrete strength verses age of concrete, graph plotted from data analysis using Eqs. (2) and (3).

Tab.1 Statistical properties of 13 input parameters (attributes) used in the models study and also Cs of concrete (output parameter)

Fig.4 Distributional analysis of data used in the model study: (a) age of concrete (AC); (b) cement content (C); (c) coarse aggregate d_max 20 mm (CA); (d) sand (S); (e) water (W); (f) admixture (AD); (g) aggregate and cement ratio (A/C); (h) water and cement ratio (W/C); (i) sand and aggregate ratio (S/CA); (j) admixture and cement ratio (AD/C); (k) strength (saturated) of original stone (SS); (l) modulus of sand (MS); (m) compressive strength of Portland cement mortar at an age of 28 d (PC); (n) compressive strength of concrete (Cs).

Fig.5 Methodological flowchart of this study.

Fig.6 Methodological flowchart of ANFIS method followed in this study.

Tab.2 Hyper-parameters of ANFIS-SCE and ANFIS-ABC models used in this study

Tab.3 Comparison of errors of ANFIS-SCE and ANFIS-ABC using training dataset

Tab.4 Comparison of errors of ANFIS-SCE and ANFIS-ABC using testing dataset

Fig.7 Cost function analysis of the ANFIS-SCE model using different statistical indicators: (a) R; (b) RMSE; and (c) MAE.

Fig.8 Cost function analysis of the ANFIS-ABC model using different statistical indicators: (a) R; (b) RMSE; and (c) MAE.

Fig.9 Correlation analysis of actual and predicted values of Cc using the ANFIS-SCE model: (a) training dataset and (b) testing dataset.

Fig.10 Correlation analysis of actual and predicted values of Cc using the ANFIS-ABC model: (a) training dataset and (b) testing dataset.

Fig.11 Error analysis of the ANFIS-SCE model using (a) training dataset and (b) testing datasets.

Fig.12 Error analysis of the ANFIS-ABC model using (a) training dataset and (b) testing datasets.

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