PMID- 32899331
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20201001
IS  - 1996-1944 (Print)
IS  - 1996-1944 (Electronic)
IS  - 1996-1944 (Linking)
VI  - 13
IP  - 17
DP  - 2020 Sep 3
TI  - A Novel Feature Selection Approach Based on Tree Models for Evaluating the 
      Punching Shear Capacity of Steel Fiber-Reinforced Concrete Flat Slabs.
LID - 10.3390/ma13173902 [doi]
LID - 3902
AB  - When designing flat slabs made of steel fiber-reinforced concrete (SFRC), it is 
      very important to predict their punching shear capacity accurately. The use of 
      machine learning seems to be a great way to improve the accuracy of empirical 
      equations currently used in this field. Accordingly, this study utilized tree 
      predictive models (i.e., random forest (RF), random tree (RT), and classification 
      and regression trees (CART)) as well as a novel feature selection (FS) technique 
      to introduce a new model capable of estimating the punching shear capacity of the 
      SFRC flat slabs. Furthermore, to automatically create the structure of the 
      predictive models, the current study employed a sequential algorithm of the FS 
      model. In order to perform the training stage for the proposed models, a dataset 
      consisting of 140 samples with six influential components (i.e., the depth of the 
      slab, the effective depth of the slab, the length of the column, the compressive 
      strength of the concrete, the reinforcement ratio, and the fiber volume) were 
      collected from the relevant literature. Afterward, the sequential FS models were 
      trained and verified using the above-mentioned database. To evaluate the accuracy 
      of the proposed models for both testing and training datasets, various 
      statistical indices, including the coefficient of determination (R(2)) and root 
      mean square error (RMSE), were utilized. The results obtained from the 
      experiments indicated that the FS-RT model outperformed FS-RF and FS-CART models 
      in terms of prediction accuracy. The range of R(2) and RMSE values were obtained 
      as 0.9476-0.9831 and 14.4965-24.9310, respectively; in this regard, the FS-RT 
      hybrid technique demonstrated the best performance. It was concluded that the 
      three hybrid techniques proposed in this paper, i.e., FS-RT, FS-RF, and FS-CART, 
      could be applied to predicting SFRC flat slabs.
FAU - Lu, Shasha
AU  - Lu S
AD  - Civil Engineering College, Liaoning Technical University, Fuxin 123000, China.
FAU - Koopialipoor, Mohammadreza
AU  - Koopialipoor M
AD  - Faculty of Civil and Environmental Engineering, Amirkabir University of 
      Technology, Tehran 15914, Iran.
FAU - Asteris, Panagiotis G
AU  - Asteris PG
AUID- ORCID: 0000-0002-7142-4981
AD  - Computational Mechanics Laboratory, School of Pedagogical and Technological 
      Education, 14121 Heraklion, Greece.
FAU - Bahri, Maziyar
AU  - Bahri M
AD  - Department of Building Structures and Geotechnical Engineering, Higher Technical 
      School of Architecture, Universidad de Sevilla, 41012 Sevilla, Spain.
FAU - Armaghani, Danial Jahed
AU  - Armaghani DJ
AD  - Institute of Research and Development, Duy Tan University, Da Nang 550000, 
      Vietnam.
LA  - eng
PT  - Journal Article
DEP - 20200903
PL  - Switzerland
TA  - Materials (Basel)
JT  - Materials (Basel, Switzerland)
JID - 101555929
PMC - PMC7503283
OTO - NOTNLM
OT  - feature selection
OT  - fiber-reinforced concrete
OT  - hybrid predictive models
OT  - punching shear capacity
OT  - tree model
COIS- The authors declare no conflict of interest.
EDAT- 2020/09/10 06:00
MHDA- 2020/09/10 06:01
PMCR- 2020/09/03
CRDT- 2020/09/09 01:03
PHST- 2020/08/02 00:00 [received]
PHST- 2020/08/26 00:00 [revised]
PHST- 2020/08/31 00:00 [accepted]
PHST- 2020/09/09 01:03 [entrez]
PHST- 2020/09/10 06:00 [pubmed]
PHST- 2020/09/10 06:01 [medline]
PHST- 2020/09/03 00:00 [pmc-release]
AID - ma13173902 [pii]
AID - materials-13-03902 [pii]
AID - 10.3390/ma13173902 [doi]
PST - epublish
SO  - Materials (Basel). 2020 Sep 3;13(17):3902. doi: 10.3390/ma13173902.