Machine learning based evaluation of concrete strength from saturated to dry by non-destructive methods
Yükleniyor...
Tarih
2023
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Machine learning (ML) techniques have been increasingly applied in various scientific fields, including non-destructive testing (NDT), to enhance efficiency and speed up data analysis. In this
study, an approach that aims to predict the compressive strength and quality of concrete with
NDT and ML algorithms is presented to provide a great advantage in terms of both time and cost
and to shorten the long laboratory periods. In the study, we aimed to set up a laboratory environment for determining the strengths of concrete samples by using Schmidt hardness values from
NDT, curing times, water contents, and ultrasonic velocities. The data obtained in the laboratory
environment was subjected to machine learning algorithms in the next process. In the laboratory
environment, which is the first stage of the study, because concrete can be found at a variety of
humidity levels depending on where it is used, 63 concrete samples were exposed to curing for 7,
28, and 90 days. Then these samples were put through a pressure test and subsequently exposed
to various moisture conditions, from saturated to dry. Thus, the moisture state of concrete samples was evaluated based on their dryness or saturation in tests conducted on concrete samples.
Afterwards, the ultrasonic velocities and Schmidt hardness values of these samples were measured. In the second stage, the strengths of concrete samples were classified with LR, MLP, SVM,
and k-NN algorithms, and high R values were achieved. As a result of the studies carried out, the
best R value (0.89) was achieved in the k-NN algorithm. This study has demonstrated that concrete strength values may be approximated with the k-NN method at high levels using sparse data
collected in a laboratory setting.
Açıklama
Anahtar Kelimeler
Non-destructive test, Compressive strength, Water content, Machine learning, Regression
Kaynak
Journal of Building Engineering
WoS Q Değeri
N/A
Scopus Q Değeri
Q1
Cilt
Sayı
76
Künye
Günaydın, O., Akbaş, E., Özbeyaz, A. ve Güçlüer, K. (2023). Machine learning based evaluation of concrete strength from saturated to dry by non-destructive methods. Journal of Building Engineering, (76), 1-13.
