Flexible pavement layer moduli determination : an adaptive artificial neural network approach

The estimation of pavement layer moduli through the use of an artificial neural network is a new concept which provides a less strenuous strategy for backcalculation procedures. Artificial Neural Networks are biologically inspired models of the human nervous system. They are specifically designed to carry out a mapping characteristic. This study demonstrates how an artificial neural network uses non-destructive pavement test data in determining flexible pavement layer moduli. The input parameters include plate loadings, corresponding sensor deflections, temperature of pavement surface, pavement layer thicknesses and independently deduced pavement layer moduli.

The spectral distribution of biologically active solar radiation at Miami, Florida, USA

The spectral distribution of solar radiation was studied under different sky conditions during a 15- month period in Miami, Florida (USA), and over a latitudinal gradient at solar maximum. Spectroradiometric scans were characterized for total irradiance (300- 3000 nm) and the relative energetic and photon contributions of the following wavelength regions: UV-B (300-320nm); UV-A (320-400nm); B (400-500rim); PAR (400-700 nm); R (600-700 nm); and FR (728- 732 rim). Notable results include: (i) significantly higher UV-A energy fluxes than currently in use for laboratory experiments involving the biological effects of this bandwidth (values ranged from 33.6 to 55.4 W/m 2 in Miami over the year); (ii) marked diurnal shifts in B:R and R:FR, with elevated R:FR values in early morning: (iii) a strong correlation between R: FR and atmospheric water content; and (iv) unusually high PAR values under direct sunlight with cloudy skies (2484 ~tmot/2 per s).