Factors Affecting Variance and Bias of Non-Nuclear Density Gauges for Porous European Mixes and Densegraded Friction Courses

Abstract

It is well known that the implementation of contractor quality control/agency quality assurance (QC/QA) can support quality improvements in hot mix asphalt (HMA) pavements, both for porous European mixes (PEMs) and dense-graded friction courses (DGFCs). On the other hand, many reasons and reports highlight the importance of proper in situ air voids content, due to major influence on service properties (rate of rutting, fatigue life, structural strength, permeability, ravelling, etc.). Another relevant standpoint is that in-lab determinations of density, though often reliable and accurate, are low-speed tests. All these facts outline the potential role of field measurement of density through non-nuclear density gauges. In the light of the above facts, the main object of the paper was confined as the study of factors affecting variance and bias of non-nuclear density gauges both for PEMs and DGFCs. Bias, variance and parameters' dependence resulted to be appreciably affected by mix typology and characteristics. In particular, when related to mix type, monovariate regressions with low-speed methods resulted able to provide a useful tool in QC/QA procedures and road asset management. Further practical applications have been outlined.

Improving infrastructure sustainability in suburban and urban areas: is porous asphalt the right answer? and how?

“…On the contrary, top layer showed an air void content of 16-20%. For both the layers, the relationship between permeability and air void content resulted consistent with [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. Both for the top and the bottom layer, in the J tests, air void content and permeability resulted closer to the target than in the H tests.…”

“…Note that: Pb' stands for asphalt binder content by weight of aggregate (%, EN 12697-6); P is the penetration (0.1 mm, EN 1426-7); SP stands for softening point (°C, EN 1427-7); D is the ductility at 25 °C (cm, ASTM D113-86, CNR B.U. N. 44/74); ER stands for Elastic Recovery (= (d/200)*100, %, EN 13398-3); the viscosity is expressed in mPas (ASTM D4402-06); the Richness modulus of the reclaimed pavement was derived according to [16][17][18][19]. …”

Detection and Monitoring of Bottom-Up Cracks in Road Pavement Using a Machine-Learning Approach

“…Despite the noteworthy improvement of the Information and Communication Technologies (ICT), which is very well represented by the emerging field of the Internet of Things (IoT), it is still rare to find, in real contexts, monitoring systems based on ICT or IoT solutions that are able to detect and monitoring concealed distresses (e.g., bottom-up cracks). The current monitoring systems refer to mobile scanning technologies, such as those based on instrumented vehicles [9], unmanned aerial vehicles, airplanes, and satellites [10], ground penetrating radar [11], traffic speed deflectometer [12], smartphones' accelerometers [13], and non-nuclear density gauges [14]. The main drawback of the above-mentioned technologies refers to the fact that they are focused on the recognition of surface distresses only, or on the derivation of these latter from surface-related parameters (e.g., texture and regularity [15], noise propagation [16], sound absorption [17], and vibration [18]).…”

Properties of Cellulose Fibres and Waste Plastic Modified Porous Friction Course Mixes

“…Th us, all the experimented mixes are expected to exhibit adequate stability to resist the plastic deformation. However, fi eld performance of these mixes can be assessed by frequent inspection using any kind non-destructive testing methods; like non-nuclear density gauges (Praticò et al 2009).…”