prikaz prve stranice dokumenta Ensemble learning based compressive strength prediction of concrete structures through real‑time non‑destructive testing
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Ensemble learning based compressive strength prediction of concrete structures through real‑time non‑destructive testing
Scientific reports, 14 (2024), 1; 1824. https://doi.org/10.1038/s41598-024-52046-y
Arora, Harish Chandra; Bhushan, Bharat; Kumar, Aman; Kumar, Prashant; Hadzima‑Nyarko, Marijana; Radu, Dorin; Cazacu, Christiana Emilia; Kapoor, Nishant Raj

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Arora, H. C., Bhushan, B., Kumar, A., Kumar, P., Hadzima‑Nyarko, M., Radu, D. ... Kapoor, N. R. (2024). Ensemble learning based compressive strength prediction of concrete structures through real‑time non‑destructive testing. Scientific reports, 14. (1). doi: 10.1038/s41598-024-52046-y

Arora, Harish Chandra, et al. "Ensemble learning based compressive strength prediction of concrete structures through real‑time non‑destructive testing." Scientific reports, vol. 14, br. 1, 2024. https://doi.org/10.1038/s41598-024-52046-y

Arora, Harish Chandra, Bharat Bhushan, Aman Kumar, Prashant Kumar, Marijana Hadzima‑Nyarko, Dorin Radu, Christiana Emilia Cazacu i Nishant Raj Kapoor. "Ensemble learning based compressive strength prediction of concrete structures through real‑time non‑destructive testing." Scientific reports 14, br. 1 (2024). https://doi.org/10.1038/s41598-024-52046-y

Arora, H. C., et al. (2024) 'Ensemble learning based compressive strength prediction of concrete structures through real‑time non‑destructive testing', Scientific reports, 14(1). doi: 10.1038/s41598-024-52046-y

Arora HC, Bhushan B, Kumar A, Kumar P, Hadzima‑Nyarko M, Radu D, i sur.. Ensemble learning based compressive strength prediction of concrete structures through real‑time non‑destructive testing. Scientific reports [Internet]. 2024. [pristupljeno 13.03.2025.];14(1). doi: 10.1038/s41598-024-52046-y

H. C. Arora, et al., "Ensemble learning based compressive strength prediction of concrete structures through real‑time non‑destructive testing", Scientific reports, vol. 14, br. 1, 2024. [Online]. Dostupno na: https://urn.nsk.hr/urn:nbn:hr:133:816181. [Citirano: 13.03.2025.]

Za citiranje koristite ovu mrežnu adresu: https://urn.nsk.hr/urn:nbn:hr:133:816181
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Naslov (engleski)Ensemble learning based compressive strength prediction of concrete structures through real‑time non‑destructive testing
AutorHarish Chandra Arora
AutorBharat Bhushan
AutorAman Kumar
AutorPrashant Kumar
AutorMarijana Hadzima‑Nyarko
AutorDorin Radu
AutorChristiana Emilia Cazacu
AutorNishant Raj Kapoor
Autorova ustanovaSveučilište Josipa Jurja Strossmayera u Osijeku
Građevinski i arhitektonski fakultet Osijek
Znanstveno / umjetničko
područje, polje i grana		TEHNIČKE ZNANOSTI
Građevinarstvo
Sažetak (engleski)
This study conducts an extensive comparative analysis of computational intelligence approaches aimed at predicting the compressive strength (CS) of concrete, utilizing two non-destructive testing (NDT) methods: the rebound hammer (RH) and the ultrasonic pulse velocity (UPV) test. In the ensemble learning approach, the six most popular algorithms (Adaboost, CatBoost, gradient boosting tree (GBT), random forest (RF), stacking, and extreme gradient boosting (XGB)) have been used to develop the... Više prediction models of CS of concrete based on NDT. The ML models have been developed using a total of 721 samples, of which 111 were cast in the laboratory, 134 were obtained from in-situ testing, and the other samples were gathered from the literature. Among the three categories of analytical models—RH models, UPV models, and combined RH and UPV models; seven, ten, and thirteen models have been used respectively. AdaBoost, CatBoost, GBT, RF, Stacking, and XGB models have been used to improve the accuracy and dependability of the analytical models. The RH-M5, UPV-M6, and C-M6 (combined UPV and RH model) models were found with highest performance level amongst all the analytical models. The MAPE value of XGB was observed to be 84.37%, 83.24%, 77.33%, 59.46%, and 81.08% lower than AdaBoost, CatBoost, GBT, RF, and stacking, respectively. The performance of XGB model has been found best than other soft computing techniques and existing traditional predictive models. Sakrij dio sažetka
Ključne riječi (engleski)
Jezikengleski
Vrsta publikacijeZnanstveni rad - Izvorni znanstveni rad
Status objaveObjavljen
Vrsta recenzijeRecenziran - međunarodna recenzija
Verzija publikacijeObjavljena verzija rada (izdavačev PDF)
Naslov časopisaScientific reports
Brojčani podacivol. 14, br. 1, 1824
e-ISSN2045-2322
DOIhttps://doi.org/10.1038/s41598-024-52046-y
URN:NBNurn:nbn:hr:133:816181
Datum objave publikacije2024
URL dokumentahttps://www.nature.com/articles/s41598-024-52046-y
Vrsta resursaTekst
Prava pristupaOtvoreni pristup
Uvjeti korištenjaIkona licence Imenovanje 4.0 međunarodna (CC BY 4.0)
Datum i vrijeme pohrane2024-12-19 12:10:18
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