Challenges in `seeing' through particulate materials

In this perspective article, we discuss the challenges of imaging granular particles in three dimensions. Starting with a brief motivation for investigating particulate materials, we provide an overview of selected experimental approaches developed for studying static or dynamic granular systems. We then list some of the challenges and solutions associated with X-ray tomography, one of the standard methods to study static packings. Subsequently, we discuss new techniques such as `smart' tracer and radar tracking for granular dynamics. We close by giving our personal view on the outstanding problems and potential solutions in the future.Comment: 17 pages, 5 figure

Brake Steer Torque Optimized Corner Braking of Motorcycles

This thesis deals with the Brake Steer Torque (BST) induced stand-up tendency of Powered Two Wheelers (PTW) and measures to lower the associated risk for running wide on curve accidents with sudden, unforeseen braking. Focus is set on the BST Avoidance Mechanism (BSTAM), a chassis design that eliminates the BST through lateral inclination of the kinematic steering axis. A simple mathematical model is used to identify its main influences on the driving behavior and derive an optimized system layout. Its theoretical potential is evaluated against the standard chassis using different cornering adaptive brake force distributions and riding styles. For the first time ever, a motorcycle with state-of-the-art brake system (Honda CBR 600 RR, C-ABS) is equipped with a BSTAM and tested in corner braking experiments. Compared to the baseline, it is significantly reducing BST related disturbances and improving directional control. The gained insights can be stepping stones to enhance PTW safety by enabling future assistance systems with autonomous corner braking

Quantifying Flood Vulnerability Reduction via Private Precaution

Private precaution is an important component in contemporary flood risk management and climate adaptation. However, quantitative knowledge about vulnerability reduction via private precautionary measures is scarce and their effects are hardly considered in loss modeling and risk assessments. However, this is a prerequisite to enable temporally dynamic flood damage and risk modeling, and thus the evaluation of risk management and adaptation strategies. To quantify the average reduction in vulnerability of residential buildings via private precaution empirical vulnerability data (n = 948) is used. Households with and without precautionary measures undertaken before the flood event are classified into treatment and nontreatment groups and matched. Postmatching regression is used to quantify the treatment effect. Additionally, we test state-of-the-art flood loss models regarding their capability to capture this difference in vulnerability. The estimated average treatment effect of implementing private precaution is between 11 and 15 thousand EUR per household, confirming the significant effectiveness of private precautionary measures in reducing flood vulnerability. From all tested flood loss models, the expert Bayesian network-based model BN-FLEMOps and the rule-based loss model FLEMOps perform best in capturing the difference in vulnerability due to private precaution. Thus, the use of such loss models is suggested for flood risk assessments to effectively support evaluations and decision making for adaptable flood risk management.European Union http://dx.doi.org/10.13039/100011102Peer Reviewe

Hierarchical Bayesian approach for modeling spatiotemporal variability in flood damage processes

Flood damage processes are complex and vary between events and regions. State-of-the-art flood loss models are often developed on the basis of empirical damage data from specific case studies and do not perform well when spatially and temporally transferred. This is due to the fact that such localized models often cover only a small set of possible damage processes from one event and a region. On the other hand, a single generalized model covering multiple events and different regions ignores the variability in damage processes across regions and events due to variables that are not explicitly accounted for individual households. We implement a hierarchical Bayesian approach to parameterize widely used depth-damage functions resulting in a hierarchical (multilevel) Bayesian model (HBM) for flood loss estimation that accounts for spatiotemporal heterogeneity in damage processes. We test and prove the hypothesis that, in transfer scenarios, HBMs are superior compared to generalized and localized regression models. In order to improve loss predictions for regions and events for which no empirical damage data are available, we use variables pertaining to specific region- and event-characteristics representing commonly available expert knowledge as group-level predictors within the HBM

Enhancing Flood Impact Analysis using Interactive Retrieval of Social Media Images

The analysis of natural disasters in a timely manner often suffers from limited sensor data. This limitation could be alleviated by leveraging information contained in images of the event posted on social media platforms, so-called “Volunteered Geographic Information (VGI)”. To save the analyst from manual inspection of all images posted online, we propose to use content-based image retrieval with the possibility of relevance feedback for retrieving only relevant images of the event. To evaluate this approach, we introduce a new dataset of 3,710 flood images, annotated by domain experts regarding their relevance with respect to three tasks (determining the flooded area, inundation depth, water pollution). We compare several image features and relevance feedback methods on that dataset, mixed with 97,085 distractor images, and are able to improve the precision among the top 100 results from 55% to 87% after 5 rounds of feedback

The flood of June 2013 in Germany: how much do we know about its impacts?

In June 2013, widespread flooding and consequent damage and losses occurred in Central Europe, especially in Germany. This paper explores what data are available to investigate the adverse impacts of the event, what kind of information can be retrieved from these data and how well data and information fulfil requirements that were recently proposed for disaster reporting on the European and international levels. In accordance with the European Floods Directive (2007/60/EC), impacts on human health, economic activities (and assets), cultural heritage and the environment are described on the national and sub-national scale. Information from governmental reports is complemented by communications on traffic disruptions and surveys of flood-affected residents and companies. Overall, the impacts of the flood event in 2013 were manifold. The study reveals that flood-affected residents suffered from a large range of impacts, among which mental health and supply problems were perceived more seriously than financial losses. The most frequent damage type among affected companies was business interruption. This demonstrates that the current scientific focus on direct (financial) damage is insufficient to describe the overall impacts and severity of flood events. The case further demonstrates that procedures and standards for impact data collection in Germany are widely missing. Present impact data in Germany are fragmentary, heterogeneous, incomplete and difficult to access. In order to fulfil, for example, the monitoring and reporting requirements of the Sendai Framework for Disaster Risk Reduction 2015–2030 that was adopted in March 2015 in Sendai, Japan, more efforts on impact data collection are needed