Publications by authors named "Massimo Losa"

Self-melting ice asphalt pavement materials inhibit pavement freezing and improve driving safety. This paper aims to study the long-term salt release characteristics of self-melting ice asphalt mixtures and the impact on pavement after complete salt release. Firstly, a method to accelerate the rapid release of salt based on the Los Angeles abrasion tester.

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Road surface properties have a major impact on pavement's life service conditions. Nowadays, contactless techniques are widely used to monitor road surfaces due to their portability and high precision. Among the different possibilities, laser profilometers are widely used, even though they have two major drawbacks: spatial information is missed and the cost of the equipment is considerable.

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Four Accident Prediction Models have been defined for Italian two-lane rural, suburban, and urban roads by exploiting different Machine Learning Algorithms. Specifically, a Classification and Regression Tree, a Boosted Regression Tree, a Random Forest, and a Support Vector Machine have been implemented to predict the number of Fatal and Injury crashes on a 905-km network, which experienced 5,802 FI crashes in 2008-2016. The dataset incorporates geometrical, functional, and environmental information.

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This paper proposes a methodology for correlating products derived by Synthetic Aperture Radar (SAR) measurements and laser profilometric road roughness surveys. The procedure stems from two previous studies, in which several Machine Learning Algorithms (MLAs) have been calibrated for predicting the average vertical displacement (in terms of mm/year) of road pavements as a result of exogenous phenomena occurrence, such as subsidence. Such algorithms are based on surveys performed with Persistent Scatterer Interferometric SAR (PS-InSAR) over an area of 964 km in the Tuscany Region, Central Italy.

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