000			02207nam a22003017a 4500
003			KOHA_MİRAKIL
005			20210727084833.0
008			201008b cy ||||| |||| 00| 0 eng d
040			_aCY-NiCIU _beng _cCY-NiCIU _erda
041			_ceng
090			_aYL 1780 _bI44 2020
100	1		_aIHEANYICHUKWU, Chigozirim Goodnews
245	1	0	_aNEURAL NETWORK CLASSIFICATION OF ASPHALT PAVEMENT DEFECTS/ _cChigozirim Goodnews IHEANYICHUKWU; Supervisors: Mohammed Ali MOSABERPANAH, Umar Ozgunalp
260			_c2020
300			_aVII, 63 sheets; _btables, figures, illustrations, _c30.5 cm _eCD.
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
502			_aThesis (MSc) - CYPRUS INTERNATIONAL UNIVERSITY INSTITUTE OF GRADUATE STUDIES AND RESEARCH Civil Engineering Department
504			_aIncludes bibliography sheets 57-63
520			_aABSTRACT Asphalt pavements are prone to deterioration from the moment they are laid, these deteriorations are in form of cracks. The most common type of these cracks are linear cracks, transverse cracks, alligator cracks and potholes. Asphalt pavement conditions are rated with the Pavement Condition Index which involves manually identifying cracks and their severity. This manual method of identifying cracks is time consuming while lots are spent of financing the endeavor. This thesis aims to classify pavement cracks using a pretrained neural network. Automatic pavement classification can automate the tasks involved in calculating the Pavement Condition Index while cutting down on the financial aspects. There is still work to be done with data collection and data availability but the results show promise. The network has a training accuracy of 92.86% but struggles to classify images into their correct classes with a precision of 56.57%. Keywords: asphalt, pavement classification, neural networks, machine learning, Alexnet
650		0	_aAsphalt
650		0	_aCivil engineering
700	1		_aSupervisors: MOSABERPANAH, Mohammed Ali _91773
700	1		_aSupervisors: Ozgunalp, Umar _91773
942			_2ddc _cTS
999			_c141065 _d141065