Urban Road Infrastructure Maintenance Planning with Application of Neural Networks

Table 1

Input values for the modeling process.

Independent variable number	Name of input variable	Range of used values
1	Specific load on the contact area	(0.5, 0.6, 0.7, and 0.8 MN/m^2)
2	Asphalt layer	Modulus of elasticity (2000, 4000, 6000, 8000, and 10,000 MN/m^2)
3		Thickness (3, 6, 9, 12, 15, 18, and 21 cm)
4		Poisson’s ratio,
5		Volume binder content, Bc-v = 13%
6	Unbound granular material	Modulus of elasticity, Ms = 400 MN/m^2
7		Thickness, d = 20–80 cm (20, 40, 60, and 80 cm)
8		Poisson’s ratio,
9	Subgrade	Modulus of elasticity, Ms = 60 MN/m^2
10		Poisson’s ratio,