Tomografía computarizada con rayos-x y sistema de imágenes de agregados (aims) para el estudio de mezclas asfálticas y agregados

La caracterización de las propiedades de los materiales empleados en ingeniería de pavimentos es fundamental pa-ra garantizar diseños confiables, estructuras durables y planes de mantenimiento y rehabilitación efectivos. Este artí-culo describe dos técnicas no destructivas basadas en la toma y procesamiento de imágenes que han sido exitosa-mente empleadas para caracterizar materiales de pavimentos: 1) tomografía computarizada con rayos-X, y 2) Siste-ma de Imágenes de Agregados. La primera técnica permite caracterizar la estructura interna de mezclas asfálticas con el fin de analizar y modelar su desempeño. En particular, esta técnica ha permitido estudiar el contenido, tama-ño, distribución y conectividad de los vacíos y la relación de estas variables con la susceptibilidad al deterioro por la presencia de humedad, la capilaridad y la permeabilidad de las mezclas. El Sistema de Imágenes de Agregados fue desarrollado para caracterizar las propiedades morfológicas de los agregados (i.e., forma, angularidad y textura), técnica que proporciona importantes ventajas con respecto a los ensayos estándar ya que las mediciones son obje-tivas, de rápida ejecución, repetibles y reproducibles. El objetivo de este documento es describir los aspectos teóri-cos básicos y algunas aplicaciones recientes de estas técnicas que representan nuevas herramientas para mejorar los procesos de caracterización de los materiales empleados en ingeniería de pavimentos.Achieving reliable pavement design, durable roadway structures and effective maintenance and rehabilitation plans requires the suitable characterisation of the materials used in pavement construction. This paper describes two non-destructive techniques based on image acquisition and analysis and their successful application in pavement engi-neering: X-ray computed tomography (X-ray CT) and aggregate imaging system (AIMS). The former has been used for characterising the internal structure of asphalt mixes to analyse and model their performance; it has been particu-larly used for studying the content, size, distribution and connectivity of air-voids and these variables’ relationship with moisture damage susceptibility, capillarity and permeability within the mixes. AIMS was intended for characterri-sing aggregates’ morphological properties (i.e., form, angularity and texture). This technique provides important ad-vantages regarding the standard methods used for obtaining the same aggregate properties: it is objective, reliable, reproducible and can be carried out quickly. This paper was aimed at describing these two techniques’ theoretical backgrounds, mention some recent applications and provide insight into how existing characterisation of materials used in pavement construction can be improved

Machine learning techniques to estimate the degree of binder activity of reclaimed asphalt pavement

This paper describes the development of novel/state-of-art computational framework to accurately predict the degree of binder activity of a reclaimed asphalt pavement sample as a percentage of the indirect tensile strength (ITS) using a reduced number of input variables that are relatively easy to obtain, namely compaction temperature, air voids and ITS. Different machine learning (ML) techniques were applied to obtain the most accurate data representation model. Specifically, three ML techniques were applied: 6th-degree multivariate polynomial regression with regularization, artificial neural network and random forest regression. The three techniques produced models with very similar precision, reporting a mean absolute error ranging from 12.2 to 12.8% of maximum ITS on the test data set. The work presented in this paper is an evolution in terms of data analysis of the results obtained within the interlaboratory tests conducted by Task Group 5 of the RILEM Technical Committee 264 on Reclaimed Asphalt Pavement. Hence, despite it has strong bonds with this framework, this work was developed independently and can be considered as a natural follow-up

X-ray computed tomography and aggregate image system (AIMS) for studying hot mix asphalt and aggregates

Achieving reliable pavement design, durable roadway structures and effective maintenance and rehabilitation plans requires the suitable characterisation of the materials used in pavement construction. This paper describes two non-destructive techniques based on image acquisition and analysis and their successful application in pavement engi-neering: X-ray computed tomography (X-ray CT) and aggregate imaging system (AIMS). The former has been used for characterising the internal structure of asphalt mixes to analyse and model their performance; it has been particu-larly used for studying the content, size, distribution and connectivity of air-voids and these variables’ relationship with moisture damage susceptibility, capillarity and permeability within the mixes. AIMS was intended for characterri-sing aggregates’ morphological properties (i.e., form, angularity and texture). This technique provides important ad-vantages regarding the standard methods used for obtaining the same aggregate properties: it is objective, reliable, reproducible and can be carried out quickly. This paper was aimed at describing these two techniques’ theoretical backgrounds, mention some recent applications and provide insight into how existing characterisation of materials used in pavement construction can be improved