Life prediction of underground structure by sulfate corrosion using Harris hawks optimizing genetic programming
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- @Article{XIE:2022:engappai,
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author = "Yuan Xie and Wei Gao and Yiwei Wang and Xin Chen and
Shuangshuang Ge and Sen Wang",
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title = "Life prediction of underground structure by sulfate
corrosion using Harris hawks optimizing genetic
programming",
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journal = "Engineering Applications of Artificial Intelligence",
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volume = "115",
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pages = "105190",
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year = "2022",
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ISSN = "0952-1976",
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DOI = "doi:10.1016/j.engappai.2022.105190",
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URL = "https://www.sciencedirect.com/science/article/pii/S0952197622002883",
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keywords = "genetic algorithms, genetic programming, RC
underground structure, Sulfate corrosion, Life
prediction, Harris hawks optimization",
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abstract = "A corrosive sulfate environment can cause strong
deterioration and destruction of reinforced concrete
(RC) underground structures and seriously reduce their
service life. Thus, it is very important to predict the
service life of RC underground structures in corrosive
sulfate environments. However, the service life of
underground structures is affected by numerous
complicated engineering and environmental factors and
cannot be determined by traditional theoretical and
experimental investigations. Therefore, to solve this
problem, a new data-driven method based on Harris hawks
optimizing genetic programming (HHO-GP) is proposed. In
this new method, to improve the traditional genetic
programming (GP), a new global optimization algorithm
called Harris hawks optimization (HHO) is adopted to
optimize its main controlling parameters. Based on 25
groups of real engineering data, the life prediction
model of underground structures in corrosive sulfate
environments with 12 main engineering and environmental
influence factors is established by the HHO-GP method.
The results show that the average relative training
error (5.5percent) and predicting error (6.3percent) of
the new prediction model are small. Therefore, the
proposed HHO-GP method can construct a suitable life
prediction model based on only real engineering data,
regardless of how many complicated influencing factors
are considered. Moreover, our data-driven life
prediction model is described by one explicit
polynomial function based on 12 influencing factors.
Thus, it can be applied in real engineering simply and
easily. Finally, the influence of the main controlling
parameters of the HHO-GP on its accuracy and efficiency
is analyzed. The results reveal that considering the
computing accuracy and efficiency and the model
completeness, the small population size and maximum
iterations of HHO are suitable, whose recommended
values are all 15. The population size and maximum
number of iterations of GP have little influence on the
prediction accuracy. Their recommended values all can
be 50",
- }
Genetic Programming entries for
Yuan Xie
Wei Gao
Yiwei Wang
Xin Chen
Shuangshuang Ge
Sen Wang
Citations