18 / 54
MAKALE
BETON PREFABRİKASYON
KASIM 2016
◆
SAYI : 120
18
lirlenmesi”, Selçuk Üniversitesi
Mühendislik-Bilim ve Teknoloji
Dergisi S.Ü. Müh. Bilim ve Tekn.
Derg., c.1, s.1, 2013 Selcuk Univ.
J. Eng. Sci. Tech., v.1, n.1, 2013
ISSN: 2147-9364 (Elektronik).
3 Ozen M. Investigation of relation-
ship between aggregate shape
parameters and concrete strength
using imaging techniques. Anka-
ra, Turkey, 2007. Master Thesis,
Graduate School of Natural and
Applied Sciences of Middle East
Technical University in Ankara.
4 Basyıgıt C., Comak B., Kılıcars-
lan S., Uncu I.S. Assessment of
concrete com-pressive strength
by image processing tech-
nique. Construction and Building
Materials. 2012, 37, pp. 526-
532,
doi:10.1016/j.conbuild-
mat.2012.07.055.
5 German S. A. (2013). Automated
damage assessment of reinforced
concrete columns for post-earth-
quake evaluations” Doktora Tezi,
Georgia Institute of Technology.
6 Lee S., Chang L. M., Skibniewski
M. (2006). Automated recognition
of surface defects using digital
color image processing, Automa-
tion in Construction 15 (2006)
540 - 549, doi: 10.1016/j.aut-
con.2005.08.001.
7 Yamaguchi T., Hashimoto S.
(2010). Fast crack detection meth-
od for large-size concrete surface
images using percolation-based
image processing Machine Vision
and Applications (2010) 21:797–
809, doi: 10.1007/s00138-009-
0189-8.
8 Santemiz P., Alyüz N., Alagöz
F. “Uydu Görüntülerinden Dep-
rem Sonrası Hasar Tespiti”,
http://www.cmpe.boun.edu.tr/~santemiz/papers/deprem07-
TR.pdf.
9 Zhu Z., German S., Brilakis I.
(2011). Visual retrieval of concrete
crack properties for automated
post-earthquake structural safety
evaluation, Automation in Con-
struction 20 (2011) 874-883, doi:
10.1016/j.autcon.2011.03.004.
10 Paal S. G., Jeon J., Brilakis I., Des
Roches R. (2014). Automated
Damage Index Estimation of Rein-
forced Concrete Columns for Post-
Earthquake Evaluations, Journal of
Structural Engineering, ASCE, doi:
04014228.doi: 10.1061/(ASCE)
ST.1943-541X.0001200.
11 Robins, P., Austin, S., Chandler,
J., Jones, P., 2001, Cement and
Concrete Research 31 (2001)
719-729.
12 Arslan M. H. (2010). “An evalua-
tion of effective design parameters
on earthquake performance of RC
buildings using neural networks”,
Engineering Structures Volume
32, Issue 7, July 2010, Pages
1888–1898, doi:10.1016/j.engs-
truct.2010.03.010.
13 Lautour OR, Omenzetter P. (2009).
Prediction of seismic - induced
structural damage using artifi-
cial neural networks, Engineering
Structures Journal, 31, 600-606.
14 Elcordy M. F., Chang K. C., Lee
G. C., (1993). Neural networks
trained by analytically simulated
damage states, Journal of Com-
putational Civil Engineering, 7, 2,
130-145.
15 Civalek ve Ülker, 2004, Dikdörtgen
Plakların Doğrusal Olmayan Ana-
lizinde Yapay Sinir Ağı Yaklaşımı
Dikdörtgen Plakların Doğrusal Ol-
mayan Analizinde Yapay Sinir Ağı
Yaklaşımı, İMO Teknik Dergi, 2004
3171-3190, Yazı 213.
16 Dantas, A., T., A., Leite, M., B.,
Nagahama, K., J., Prediction of
compressive strength of concrete
ontaining construction and demo-
lition waste using artificial neural
networks, Construction and Build-
ing Materials 38 (2013) 717-722.
17 Doğan, G., Arslan, M.H., Baykan,
Ö.K., “ New Approaches about the
Damage Estimation of Reinforced
Concrete Buildings after Earth-
quake: Detecting the Damage of
Columns with Image Analysis”,
12
th
International Congress on Ad-
vanced in Civil Engineering (ACE),
2016, Istanbul, Turkey.
18 Doğan, G. “Betonarme Kolonların
Deprem Sonrası Hasar Seviyeleri-
nin Akıllı Sistem Tabanlı Bir Yön-
temle Belirlenmesi”, Doktora Tezi
(Devam ediyor), Fen Bilimleri Ens-
titüsü, Selçuk Üniversitesi, Konya.