Jürgen Hackl

GPU-Accelerated Rendering Methods to Visually Analyze Large-Scale Disaster Simulation Data

Emerging methodologies for natural hazard risk assessments involve the execution of a multitude of different interacting simulation models that produce vast amounts of spatio-temporal datasets. This data pool is further enlarged when such simulation results are post-processed using GIS operations, for example to derive information for decision-making. The novel approach presented in this paper makes use of the GPU-accelerated rendering pipeline to perform such operations on-the-fly without storing any results on secondary memory and thus saving large amounts of storage space. Particularly, algorithms for three frequently used geospatial analysis methods are provided, namely for the computation of difference maps using map algebra and overlay operations, distance maps and buffers as examples for proximity analyses as well as kernel density estimation and inverse distance weighting as examples for statistical surfaces. In addition, a visualization tool is presented that integrates these methods using a node-based data flow architecture. The application of this visualization tool to the results of a real-world risk assessment methodology used in civil engineering shows that the memory footprint of post-processing datasets can be reduced at the order of terabytes. Although the technique has several limitations, most notably the reduced interoperability with conventional analysis tools, it can be beneficial for other use cases. When integrated into desktop GIS applications, for example, it can be used to quickly generate a preview of the results of complex analysis chains or it can reduce the amount of data to be transferred to web or mobile GIS applications.

Modelling the functional capacity losses of networks exposed to hazards

Abstract The quantification of probable network-related consequences resulting from the occurrence of (natural) hazard events supports network managers in determining the most suitable interventions to execute. This assessment should include the modelling of consequences related to the use of the network and the levels of service. A method is presented to quantify the temporal functional capacity losses of the individual objects of which networks are comprised due to hazard events to ultimately support the estimation of consequences at a network level. The method is designed to handle the effects of (i) time-varying hazards, (ii) multiple hazards, (iii) functional capacity losses that are independent of (structural) physical capacity losses and (iv) functional capacity losses that demand preventive interventions. An example demonstrates how the functional capacity losses of objects in a road network change over time as rainfall-triggered flood and mudflow events, inspection events and restoration events occur.

Stress tests for a road network using fragility functions and functional capacity loss functions

A quantitative approach to conduct a specific type of stress test on road networks is presented in this article. The objective is to help network managers determine whether their networks would perform adequately during and after the occurrence of hazard events. Conducting a stress test requires (i) modifying an existing risk model (i.e., a model to estimate the probable consequences of hazard events) by representing at least one uncertainty in the model with values that are considerably worse than median or mean values, and (ii) developing criteria to conclude if the network has an adequate post-hazard performance. Specifically, the stress test conducted in this work is focused on the uncertain behavior of individual objects that are part of a network when these are subjected to hazard loads. Here, the relationships between object behavior and hazard load are modeled using fragility functions and functional capacity loss functions. To illustrate the quantitative approach, a stress test is conducted for an example road network in Switzerland, which is affected by floods and rainfall-triggered mudflows. Beyond the focus of the stress test, this work highlights the importance of using a probabilistic approach when conducting stress tests for temporal and spatially distributed networks.

Determination of Markov Transition Probabilities to be Used in Bridge Management from Mechanistic-Empirical Models

Many bridge management systems use Markov models to predict the future deterioration of structural elements. This information is subsequently used in the determination of optimal intervention strategies and intervention programs. The input for these Markov models often consists of the condition states of the elements and how they have changed over time. This input is used to estimate the probabilities of transition of an object from each possible condition state to each other possible condition state in one time period. A complication in using Markov models is that there are situations in which there is an inadequate amount of data to estimate the transition probabilities using traditional methods (e.g., due to the lack of recording past information so that it can be easily retrieved, or because it has been collected in an inconsistent or biased manner). In this paper, a methodology to estimate the transition probabilities is presented that uses proportional data obtained by mechanistic-empirical models of the deterioration process. A restricted least-squares optimization model is used to estimate the transition probabilities. The methodology is demonstrated by using it to estimate the transition probabilities for a reinforced concrete (RC) bridge element exposed to chloride-induced corrosion. The proportional data are generated by modeling the corrosion process using mechanistic-empirical models and Monte Carlo simulations. DOI: 10.1061/(ASCE)BE.1943-5592.0001101.

Use of unmanned aerial vehicle photogrammetry to obtain topographical information to improve bridge risk assessment

AbstractBridges, as all objects in road networks, are built to provide a specified level of service over a specified time period. This level of service ensures that acceptable levels of health, saf...

Determination of Near-Optimal Restoration Programs for Transportation Networks Following Natural Hazard Events Using Simulated Annealing: Restoration programs for transportation networks following natural hazard events using simulated annealing

Disruptive events, such as earthquakes, floods, and landslides, may disrupt the service provided by transportation networks on a vast scale, as their occurrence is likely to cause multiple objects to fail simultaneously. The restoration program following a disruptive event should restore service as much, and as fast, as possible. The estimation of risk due to natural hazards must take into consideration the resilience of the network, which requires estimating the restoration program as accurately as possible. In this article, a restoration model using simulated annealing is formulated to determine near-optimal restoration programs following the occurrence of hazard events. The objective function of the model is to minimize the costs, taking into consideration the direct costs of executing the physical interventions, and the indirect costs that are being incurred due to the inadequate service being provided by the network. The constraints of the model are annual and total budget constraints, annual and total resource constraints, and the specification of the number and type of interventions to be executed within a given time period. The restoration model is demonstrated by using it to determine the near-optimal restoration program for an example road network in Switzerland following the occurrence of an extreme flood event. The strengths and weaknesses of the restoration ∗To whom correspondence should be addressed. E-mail: hackl@ibi. baug.ethz.ch. model are discussed, and an outlook for future work is

A process for the assessment of infrastructure related risk due to natural hazards

The determination of network related risks for transport infrastructure systems, such as road or railway networks, is a challenging task. Due to such complex systems, it is generally impossible to abstract the global behavior from the analysis of single components, especially under conditions such as failures or damages. People who manage infrastructure have to handle these risks. The proposed overarching risk assessment process is constructed in a way so that computational support can be constructed in modules. This allows to couple the process with detailed sub-processes to achieve varying levels of detail in the risk assessment. The use of the overarching risk assessment process is demonstrated by using it to evaluate infrastructure related risk due to natural hazards for an example region in Switzerland.