1. Department of Civil and Environmental Engineering, College of Engineering, Shantou University, Shantou 515063, China 2. Department of Civil Engineering, Misr Higher Institute of Engineering and Technology, Mansoura, Egypt 3. Department of Civil Engineering, Higher Future Institute of Engineering and Technology, Mansoura, Egypt 4. Guangdong Engineering Center for Structure Safety and Health Monitoring, Shantou University, Shantou 515063, China 5. Civil and Architectural Construction Department, Faculty of Technology and Education, Suez University, Ismailia, Egypt 6. Department of Civil and Environmental Engineering, Collage of Engineering, University of Sharjah, Sharjah 27272, UAE
This study presents a new systematic algorithm to optimize the durability of reinforced recycled aggregate concrete. The proposed algorithm integrates machine learning with a new version of the firefly algorithm called chaotic based firefly algorithm (CFA) to evolve a rational and efficient predictive model. The CFA optimizer is augmented with chaotic maps and Lévy flight to improve the firefly performance in forecasting the chloride penetrability of strengthened recycled aggregate concrete (RAC). A comprehensive and credible database of distinctive chloride migration coefficient results is used to establish the developed algorithm. A dataset composite of nine effective parameters, including concrete components and fundamental characteristics of recycled aggregate (RA), is used as input to predict the migration coefficient of strengthened RAC as output. k-fold cross validation algorithm is utilized to validate the hybrid algorithm. Three numerical benchmark analyses are applied to prove the superiority and applicability of the CFA algorithm in predicting chloride penetrability. Results show that the developed CFA approach significantly outperforms the firefly algorithm on almost tested functions and demonstrates powerful prediction. In addition, the proposed strategy can be an active tool to recognize the contradictions in the experimental results and can be especially beneficial for assessing the chloride resistance of RAC.
Fireflies are split into two groups according to gender and have corresponding different ways for updating motions.
multi collection system
Thomas et al. [27]
cross entropy firefly
CFA
A cross entropy model is applied according to Monte Carlo approach.
hybrid mode
Wang et al. [28]
orthogonal firefly
OFA
The firefly is integrated with particle swarm optimization to establish an orthogonal learning model.
hybrid mode
Wang and Song [29]
firefly algorithm
FA
The recognized model added a Lévy flight as a multiplication element for the random process.
novel mode
Li et al. [30]
adaptive firefly
AFA
The adaptive control parameters are used to adapt and control the model.
adaptive mode
Wang and Song [29]
Yin?Yang firefly
YYFA
The established model is dependent on dimensional Cauchy mutation and a stochastic attraction technique is adopted in the model.
novel move mode
Wang et al. [31]
Tab.1
Fig.1
Fig.2
Fig.3
Fig.4
Fig.5
Fig.6
parameter
unit
type
dataset size range
mean
water cement ratio (w/c)
input
(0.35–0.55)
0.47
water content (w)
kg/m3
input
(157.5–226.8)
205.42
cement content (c)
kg/m3
input
(175.5–553.5)
371.57
sand (S)
kg/m3
input
(479.0–787)
665.86
coarse recycled aggregate (RA)
kg/m3
input
(832.14–1080)
1011.46
pozzolanic materials (PM)
kg/m3
input
(11.13–227.5)
101.09
curing age (T)
day
input
(1.0–365)
60.97
particle density (D)
input
(2.34–4.8)
2.59
water absorption (WA)
%
input
(3.89–7.06)
5.04
electric charge (EC)
C
output
(1155–8790)
3234.06
Tab.2
Fig.7
Fig.8
benchmark function
range
CFA
typical FA
mean
stdev.
mean
stdev.
[?100,100]
2.85e?02
2.14e?02
1.32e?01
1.95e?01
[?600,600]
1.84e?03
4.15e?03
1.19e?02
4.65e?02
[?5.12,5.12]
3.27e+00
1.87e+00
4.21e+01
3.51e+01
Tab.3
Fig.9
parameter
characteristic/value
MF kind
Gaussian
fuzzy structure
Takagi-Sugeno-type
output MF
linear
epoch number in ANFIS
300
minimum improvement
1 × 10?5
kind of initial FIS
genfis3
initial step size
0.01
step size decreasing rate
0.9
step size increasing rate
1.1
training values
66
testing values
17
clustering approach
c-mean
Tab.4
No.
number of clusters
results of network
training dataset
testing dataset
R2
RMSE
mae
R2
RMSE
mae
ANFIS 1
5
0.902
0.103
0.121
0.864
0.166
0.561
ANFIS 2
8
0.916
0.091
0.106
0.870
0.156
0.420
ANFIS 3
10
0.937
0.088
0.093
0.873
0.149
0.401
ANFIS 4
11
0.928
0.098
0.101
0.871
0.153
0.417
Tab.5
Fig.10
CFA parameter-type
value
coefficient of light absorption (γ)
1.2
coefficient of attraction base (β0)
2.0
coefficient of movement (α0)
0.3
population size
100
fitness function
RMSE
cross-validation
10 folds
bifurcation parameter (μ)
4
number of iterations
1000
Tab.6
Fig.11
Fig.12
Fig.13
Fig.14
Fig.15
Fig.16
Fig.17
model
ANFIS-CFA
ANFIS
PSO
GA
ranking
3.5
5.8
6.2
6.4
Tab.7
Fig.18
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