International Journal of Optimization in Civil Engineering
عنوان نشریه
Iran University of Science & Technology
Engineering & Technology
http://ijoce.iust.ac.ir
18
agent2
2228-7558
doi
en
jalali
1395
7
1
gregorian
2016
10
1
6
4
online
1
fulltext
en
GENETIC PROGRAMMING AND MULTIVARIATE ADAPTIVE REGRESION SPLINES FOR PRIDICTION OF BRIDGE RISKS AND COMPARISION OF PERFORMANCES
Optimal design
Optimal design
پژوهشي
Research
<p>In this paper, two different data driven models, genetic programming (GP) and multivariate adoptive regression splines (MARS), have been adopted to create the models for prediction of bridge risk score. Input parameters of bridge risks consists of safe risk rating (SRR), functional risk rating (FRR), sustainability risk rating (SUR), environmental risk rating (ERR) and target output. The total dataset contains 66 bridges data in which 70% of dataset is taken as training and the remaining 30% is considered for testing dataset. The accuracy of the models are determined from the coefficient of determination (R<sup>2</sup>). If the R<sup>2</sup> the testing model is close to the R<sup>2 </sup>value of the training model, that particular model is to be consider as robust model. The modeling mechanisms and performance is quite different for both the methods hence comparative study is carried out. Thus concluded robust models performance based on the R<sup>2</sup> value, is checked with mathematical statistical equations. In this study both models were performed, examined and compared the results with mathematical methods successfully. From this work, it is found that both the proposed methods have good capability in predestining the results. Finally, the results reveals that genetic Programming is marginally outperforms over the MARS technique.</p>
bridge risks, genetic programming, multivariate adoptive regression splines, performance criteria.
547
555
http://ijoce.iust.ac.ir/browse.php?a_code=A-10-66-115&slc_lang=en&sid=1
M.
Venkata Rao
`180031947532846001032`

180031947532846001032
No
P.
Rama Mohan Rao
`180031947532846001033`

180031947532846001033
Yes