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The energy or fuel consumption of the millions of vehicles that daily operate in road pavements has a significant economic and environmental impact on the use phase of road infrastructures regarding their life cycle analysis. Therefore, new solutions should be studied to reduce the vehicles energy consumption, namely due to the tire-pavement interaction, and contribute towards the sustainable development. This study aims at estimating the energy consumption due to the rolling resistance of tires moving over pavements with distinct surface characteristics. Thus, different types of asphalt mixtures were used in the surface course to determine the main parameters influencing the energy consumption. A laboratory scale prototype was developed explicitly for this evaluation. Data mining techniques were used to analyze the experimental results due to the complex correlation between the data collected during the tests, providing meaningful results. In particular, the artificial neural network allowed to obtain models with excellent capacity to estimate energy consumption. A sensitive analysis was carried out with a five input parameter model, which showed that the main parameters controlling the energy consumption are the vehicle speed and the mean texture depth.

The investment in tunneling shows an worldwide expansion trend. Reduction of risks, as part of the financial strategy of the stakeholders, has been the focus of several research studies. This paper aims to describe the construction risk prevention, in terms of occupational accidents and diseases, of the 2nd phase of the Marão Tunnel (Portugal) -the longest roadway tunnel in the Iberian Peninsula excavated with Sequential Method -with the particularity of the works being interrupted, leaving the tunnel only with primary lining for three years. The methodology is based in: 1) identification, by literature review, of most typical preventive measures and assessement of their applicability in the case study; 2) description of new preventive approaches. The paper will start with a history of work accidents, followed by the case study and, finally, it will delve into the preventive measures applied, as well as the new approaches, such as over-runs, falling blocks and risks associated with the suspension of works. Measures identified in literature were implemented and their validity was assessed. New approaches provided an safer and quickest way to work. This study is relevant to future tunnelling sites, since it is a good example of risk management using new approaches.Key words: New Austrian Tunneling Method (NATM), Sequential Excavation Method (SEM), risks, safety, tunneling. ResumenLa inversión en construcción de túneles muestra una tendencia de expansión mundial. La reducción de los riesgos laborales, como parte de la estrategia financiera de las empresas, ha protagonizado varios estudios científicos. Este artículo describe la seguridad laboral implementada durante la construcción de la 2ª fase del Túnel de Marão (Portugal), el túnel carretero más largo de la Península Ibérica ejecutado mediante el Método de Excavación Secuencial, con la particularidad de que su ejecución fue interrumpida durante tres años manteniendo el túnel sólo el revestimiento primario. La metodología se basa en: 1) identificación, mediante revisión bibliográfica, de las medidas preventivas más habituales y evaluación de su aplicabilidad al caso de estudio; 2) descripción de nuevos enfoques preventivos. El artículo comienza analizando un histórico de accidentes, seguido por el estudio de caso y finaliza profundizando en las medidas implementadas y en los nuevos enfoques, como sobrecargas, caída de bloques y riesgos asociados a la suspensión de las obras. Se implementaron medidas identificadas en la bibliografía y se evaluó su validez. Los nuevos enfoques proporcionaron una manera más segura y rápida de trabajar. Este estudio es relevante para el futuro de la construcción de túneles por la aplicación de nuevos enfoques.

The determination of mechanical properties of granitic rocks has a great importance to solve many engineering problems. Tunnelling, mining and excavations are some examples of these problems. The purpose of this paper is to apply Data Mining (DM) techniques such as multiple regressions (MR), artificial neural networks (ANN) and support vector machines (SVM), to predict the uniaxial compressive strength and the deformation modulus of the Oporto granite. This rock is a light grey, two-mica, medium-grained, hypidiomorphic granite and is located in Oporto (Portugal) and surrounding areas. Begonha (1997) and Begonha et al. (2002) studied this granite in terms of chemical, mineralogical, physical and mechanical properties. Among other things, like the weathering features, those authors applied correlation analysis to investigate the relationships between two properties either physical or mechanical or physical and mechanical. This study took the data published by those authors to build a database containing 55 rock sample records. Each record contains the free porosity (N 48), the dry bulk density (d), the ultrasonic velocity (v), the uniaxial compressive strength (σ c) and the modulus of elasticity (E). It was concluded that all the models obtained from DM techniques have good performances. Nevertheless, the best forecasting capacity was obtained with the SVM model with N 48 and v as input parameters.

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