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Pregled bibliografske jedinice broj: 1079206

Machine learning approaches for estimation of compressive strength of concrete


(Division of Construction Engineering and Management, Purdue University, West Lafayette, IN 47907, USA ; State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China) Hadzima-Nyarko, Marijana; Nyarko, Emmanuel Karlo; Lu, Hongfang; Zhu, Senlin
Machine learning approaches for estimation of compressive strength of concrete // European physical journal plus, 135 (2020), 682, 23 doi:10.1140/epjp/s13360-020-00703-2 (međunarodna recenzija, članak, znanstveni)


CROSBI ID: 1079206 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Machine learning approaches for estimation of compressive strength of concrete

Autori
Hadzima-Nyarko, Marijana ; Nyarko, Emmanuel Karlo ; Lu, Hongfang ; Zhu, Senlin

Kolaboracija
Division of Construction Engineering and Management, Purdue University, West Lafayette, IN 47907, USA ; State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China

Izvornik
European physical journal plus (2190-5444) 135 (2020); 682, 23

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
rubberized concrete ; compressive strength ; machine learning models ; artificial neural network (ANN) ; k-nearest neighbor (KNN) ; regression trees (RT) ; random forests (RF)

Sažetak
Estimation of compressive strength of rubberized concrete is important for engineering safety. In this study, measured data (the compressive strength of rubberized concrete and its impacting factors) were collected by literature review (457 samples). In order to accurately predict the compressive strength of rubberized concrete, four machine learning models [artificial neural network (ANN), k-nearest neighbor (KNN), regression trees (RT), and random forests (RF)] were developed and compared to estimate the compressive strength of rubberized concrete, and the modeling results were compared with two traditional expressions. The model performance was evaluated using three performance indicators: the Nash—Sutcliffe efficiency coefficient (NSC), the root-mean- squared error (RMSE), and the mean absolute error (MAE). The results showed that the RT model performs the best, followed by the ANN and RF in the model training phase. In the model testing phase, the ANN model performs the best, followed by the RT, RF, and KNN. The overall results indicated that the ANN model performs the best, followed by RT and RF, and the KNN model performs the worst. The ANN and RT models outperformed the two traditional expressions. The tree-based models (RT and RF) and KNN model may not be applicative to estimate the compressive strength of rubberized concrete due to the generally poor performances in the model testing phase compared with that in the model training phase. The results showed that the traditional ANN model is sufficient for the accurate estimation of the compressive strength of rubberized concrete when the model is properly trained. The results in the present research can provide reference for the prediction of the compressive strength of rubberized concrete, which will benefit engineering management and safety as well.

Izvorni jezik
Engleski

Znanstvena područja
Građevinarstvo, Računarstvo



POVEZANOST RADA


Projekti:
HRZZ-UIP-2017-05-7113 - Razvoj armiranobetonskih elemenata i sustava s otpadnim prahom automobilskih guma (ReCoTiP) (Miličević, Ivana, HRZZ - 2017-05) ( POIROT)

Ustanove:
Građevinski i arhitektonski fakultet Osijek,
Fakultet elektrotehnike, računarstva i informacijskih tehnologija Osijek

Citiraj ovu publikaciju

(Division of Construction Engineering and Management, Purdue University, West Lafayette, IN 47907, USA ; State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China) Hadzima-Nyarko, Marijana; Nyarko, Emmanuel Karlo; Lu, Hongfang; Zhu, Senlin
Machine learning approaches for estimation of compressive strength of concrete // European physical journal plus, 135 (2020), 682, 23 doi:10.1140/epjp/s13360-020-00703-2 (međunarodna recenzija, članak, znanstveni)
(Division of Construction Engineering and Management, Purdue University, West Lafayette, IN 47907, USA ; State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China) (Division of Construction Engineering and Management, Purdue University, West Lafayette, IN 47907, USA, State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China) Hadzima-Nyarko, Marijana, Nyarko, E., Lu, H. & Zhu, S. (2020) Machine learning approaches for estimation of compressive strength of concrete. European physical journal plus, 135, 682, 23 doi:10.1140/epjp/s13360-020-00703-2.
@article{article, year = {2020}, pages = {23}, DOI = {10.1140/epjp/s13360-020-00703-2}, chapter = {682}, keywords = {rubberized concrete, compressive strength, machine learning models, artificial neural network (ANN), k-nearest neighbor (KNN), regression trees (RT), random forests (RF)}, journal = {European physical journal plus}, doi = {10.1140/epjp/s13360-020-00703-2}, volume = {135}, issn = {2190-5444}, title = {Machine learning approaches for estimation of compressive strength of concrete}, keyword = {rubberized concrete, compressive strength, machine learning models, artificial neural network (ANN), k-nearest neighbor (KNN), regression trees (RT), random forests (RF)}, chapternumber = {682} }
@article{article, year = {2020}, pages = {23}, DOI = {10.1140/epjp/s13360-020-00703-2}, chapter = {682}, keywords = {rubberized concrete, compressive strength, machine learning models, artificial neural network (ANN), k-nearest neighbor (KNN), regression trees (RT), random forests (RF)}, journal = {European physical journal plus}, doi = {10.1140/epjp/s13360-020-00703-2}, volume = {135}, issn = {2190-5444}, title = {Machine learning approaches for estimation of compressive strength of concrete}, keyword = {rubberized concrete, compressive strength, machine learning models, artificial neural network (ANN), k-nearest neighbor (KNN), regression trees (RT), random forests (RF)}, chapternumber = {682} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


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