How much traffic is too much? Finding the right vehicle quota for a scenic mountain road in the Italian Alps

An effective yet neglected option to limit the detrimental effects of car traffic in natural tourist destinations is the imposition of vehicle quotas. Defining the right quota for a road system, however, may not be straightforward because of the complex connection between the number of vehicles entering the system and traffic levels across space and over time. In this paper, we present a novel approach to tackle this issue that combines agent-based modeling and standards of quality, and we use it to define an hourly quota aimed at limiting traffic congestion and demand for parking along a scenic road in the Dolomites (Italian Alps). The model is designed and calibrated using geospatial and traffic data, and the acceptability of the quotas is further tested according to the hourly modal splits they might induce. Our model simulations highlight that, by redistributing morning traffic inflows, the quota can almost eliminate congestion with only a negligible impact on overall traffic figures. Further, while traffic reductions of up to 35% may be needed to eliminate traffic-related issues, more reasonable reductions (i.e. 10–25%) may be enough to address most of those. From an empirical perspective, the paper shows the effectiveness of quotas in sustainable transport and tourism; from a policy and management perspective, it proposes an approach for the definition of an ideal quota. The design of a quota system, however, requires detailed implementation and communication strategies, and more advanced simulation tools to capture circulation patterns induced by such strategies

A comparison of Random Set and Point Estimate Methods in Finite Element Analysis of Tunnel Excavation

Risk and Reliability in Geotechnical Engineerin

On the Effect of Shield Friction in Hard Rock TBM Excavation

publishedVersio

The Importance of Laboratory Experiments in Landslide Investigation

This study focuses on a better understanding of mass movements and on the influences of different boundary conditions on velocities of creeping slopes. A well monitored example of a slowly creeping landslide is the mass movement Hochmais - Atemkopf, situated in the Kaunertal, Tyrol, Austria (Fig. 1). The long term monitoring program for more than 40 years of this landslide gives a good impression of its time dependent behaviour. A large amount of additional data, as geological mapping, boreholes, geophysical investigation and so on provides a funded base for the model’s geometry. The most influencing factor for finite element calculations is besides the model’s geometry the rheological model and the therefor adapted material properties. Creep laboratory experiments have been performed and evaluated for the most active sliding zone. Long term shear tests from 1964 have been reevaluated and compared with current long term triaxial tests. The experiments reveal a non linear dependence between equivalent stress and displacement rate. An elasto, visco - plastic rheological model with a non-linear viscose deformation has been fitted to those results

Interpretação climática do registro isotópico e da acumulação em testemunho de firn antártico do setor do Mar de Weddell

O presente estudo teve como foco o registro de isótopos estáveis de água – 18O, D e excesso de deutério (d=D-8⨯18O) – e de acumulação do sítio Criosfera 1 (CR1; 84° 00’ 00.00’’ S; 79° 29’ 39.00’’ W; 1285 m de altitude), situado na bacia de drenagem glacial da corrente de gelo Möller, no Setor do Mar de Weddell, próximo a divisão entre Antártica Ocidental e Oriental. O objetivo foi avaliar a história deposicional, verificar a qualidade do sinal climático e se as informações climáticas de curto prazo (século 21) se encontram preservadas nesse sítio. Neste trabalho foi analisado dois testemunhos: o testemunho raso de firn TT01 e os primeiros ⁓10 metros do testemunho de gelo CR1. O testemunho TT01 (83° 59’ 59,50’’ S, 79° 29’ 31,40’’ W; 9,471 m ou 4,097 m eq. H2O; ρ: 0,44±0,07 g.cm-3) foi recuperado durante a Travessia Brasileira à Antártica Ocidental no verão austral de 2015, enquanto o testemunho CR1 (83° 59' 59,1'' S, 79° 29' 19,3'' W; 9,130 m ou 4,093 m eq. H2O; ρ: 0,45±0,08 g.cm-3) foi recuperado na expedição do verão austral de 2012. Ambos testemunhos foram descontaminados e derretidos em um sistema de derretimento contínuo com amostragem discreta subcentimétrica nos Laboratórios Limpos Classe ISO 5 do Climate Change Institute (UMAINE/USA). As razões isotópicas foram determinadas pela técnica de espectrometria à laser por cavidade ressonante ring-down (WSCRDS) no Laboratório de Isótopos Estáveis do Centro Polar e Climático (UFRGS/Brasil). No total, cerca de 633 amostras com resolução de 0,03 m foram analisadas e a acurácia das medidas foi melhor que 0,2‰ e 0,9‰ para 18O e D, respectivamente. Análises de cromatografia por troca iônica foram somente performadas para o testemunho TT01 com intuito de aprimorar a datação. A datação do TT01 foi realizada pelo método de contagem de camadas anuais usando principalmente a variação sazonal dos s e da razão nssSO4 2-/ Na+. Na datação do CR1 foi somente utilizado a variação sazonal dos s. Os 9,471 m do TT01 cobriram 16 anos (1999-2015), com um erro estimado de <5 meses, enquanto os 9,130 m do CR1 cobriram 13 anos (1999-2012) e o erro estimado da datação foi de ⁓6 meses. O registro de acumulação foi estendido até 2018 com os dados de altura de neve do Módulo Científico Automático Criosfera 1. Para o período de 1999-2018 foi estimada uma taxa de acumulação de 0,24±0,09 m eq. H2O a-1 e observado uma tendência de decréscimo significante na acumulação anual. Para atender os objetivos, foi explorado as relações dos registros isotópicos e de acumulação com: parâmetros meteorológicos (temperatura, direção e velocidade do vento, pressão média ao nível do mar, concentração de gelo marinho), eventos climáticos extremos de precipitação e de vento e índices climáticos (e.g., índices de variabilidade climática de larga escala (e.g., SAM e ENSO) e da zona climatológica de baixa pressão do mar de Amundsen (ASL)). Este estudo mostra que registro preservado no sítio Criosfera 1 é fortemente tendenciado por eventos extremos de precipitação. Além, ele aponta que tanto o registro da composição isotópica quanto o de acumulação do imprimem variabilidade circulação atmosférica de larga escala, principalmente a influência do SAM, mas também a influência de forçante tropical (e.g., ENSO).This study investigated the stable water isotopes – 18O, D and d-excess (d=D-8⨯18O) – and snow accumulation records from the Criosfera 1 site (CR1; 84° 00' 00.00'' S; 79° 29' 39.00'' W; 1285 m altitude), located in the upper reaches of the Möller Ice Stream basin, Weddell Sea Sector, near to the boundary between West and East Antarctica. The objective was to evaluate the depositional history, verify the quality of the signal stored and if the short-term climate information (⁓21st century) is preserved at this site. In this work two cores were analyzed: the TT01 shallow firn core and the first ⁓10 meters of the CR1 ice core. The TT01 core (83° 59' 59.50'' S, 79° 29' 31.40'' W; 9.471 m or 4.097 m H2O eq.; ρ: 0.44±0.07 g.cm-3) was recovered during the Brazilian Crossing to West Antarctica in the austral summer of 2015, while the CR1 core (83° 59' 59.1'' S, 79° 29' 19.3'' W; 9,130 m or 4,093 m eq. H2O ; ρ: 0.45±0.08 g.cm-3) was recovered in the austral summer 2012 expedition. Both cores were decontaminated and melted in a continuous melting system with discrete sub-centimeter sampling at Climate's ISO Class 5 Clean Laboratories Change Institute (UMAINE/USA). The isotopic ratios were determined by ring-down resonant cavity laser spectrometry (WS-CRDS) at the Laboratory of Stable Isotopes of the Centro Polar e Climático (UFRGS/Brazil). In total, about 633 samples with a resolution of 0.03 m were analysed and the accuracy of the measurements was better than 0.2‰ and 0.9‰ for 18O and D, respectively. Ion exchange chromatography analyzes were only performed for the TT01 core in order to improve the dating. The TT01 dating was performed by the annual layer count method using mainly the seasonal variation of the s and the nssSO4 2-/Na+ ratio. In the CR1 dating, only the seasonal variation of s was used. The TT01 cover 16 years (1999-2015), with an estimated error of <5 months, while the CR1 core cover 13 years (1999-2012) and the estimated error of the dating is ⁓6 months. The accumulation record was extended until 2018 using snow height data from the Criosfera 1 Automatic Weather Station. For the 1999-2018 period, an accumulation rate of 0.24±0.09 m eq. H2O yr-1 and a significant decreasing trend in annual accumulation was observed. To achieve our objectives, the relationship between isotopic and accumulation records was explored with: meteorological parameters (temperature, wind direction and speed, mean pressure at sea level, concentration of sea ice), extreme weather events of precipitation (EPE) and wind (SWE), and climate indices (e.g., large-scale climate variability indices (e.g., SAM and ENSO) and the Amundsen Sea Low Pressure indices (ASL)). This study shows that the record preserved at the Criosfera 1 site is strongly biased by EPEs. Furthermore, it points out that both the isotopic composition and accumulation records imprint the large-scale atmospheric circulation variability, mainly the influence of SAM but also the tropical forcings (e.g., ENSO)

Dramaturgisk tænkning i en praksis uden scene: når klovnen er en indgang til mennesket

Hvilken rolle spiller dramaturgi, når vi skaber et improviseret møde sammen med mennesker, der ikke har opsøgt teatret og ikke nødvendigvis opdager, at det er teatrets magi, der skaber den gode oplevelse? Hvordan skaber dramaturgisk tænkning værdi i en praksis langt væk fra teatrets skrå brædder

Capabilities and Challenges Using Machine Learning in Tunnelling

Digitalization changes the design and operational processes in tunnelling. The way of gathering geological data in the field of tunnelling, the methods of rock mass classification as well as the application of tunnel design analyses, tunnel construction processes and tunnel maintenance will be influenced by this digital transformation. The ongoing digitalization in tunnelling through applications like building information modelling and artificial intelligence, addressing a variety of difficult tasks, is moving forward. Increasing overall amounts of data (big data), combined with the ease to access strong computing powers, are leading to a sharp increase in the successful application of data analytics and techniques of artificial intelligence. Artificial Intelligence now arrives also in the fields of geotechnical engineering, tunnelling and engineering geology. The chapter focuses on the potential for machine learning methods – a branch of Artificial Intelligence - in tunnelling. Examples will show that training artificial neural networks in a supervised manner works and yields valuable information. Unsupervised machine learning approaches will be also discussed, where the final classification is not imposed upon the data, but learned from it. Finally, reinforcement learning seems to be trendsetting but not being in use for specific tunnel applications yet