Image-Based Modeling of Bridges and Its Applications to Evaluating Resiliency of Transportation Networks

Modern urban areas are heavily dependent on transportation networks to sustain their economic life. Hence, when vital components of a regional network are disrupted, economic losses are inevitable. As evidenced by 1989, Loma Prieta and 1994, Northridge earthquakes, the seismic damages experienced by bridges alone result in extensive traffic delays and rerouting, not only hindering emergency response but also causing indirect economic losses that far surpass the direct cost of damage to infrastructure. Nevertheless, in many areas of the U.S., transportation networks lack the resilience required to sustain the potential demands of natural hazards. Traditional hazard assessment methods, in theory, provide the tools required for predicting the vulnerabilities associated with natural hazards. Nonetheless, due to their abstractions of the complex infrastructure and the coupled regional behavior, they often fall short of that expectation. This study proposes a semi-automated image-based model generation framework for producing structure-specific models and fragility functions of bridges. The framework effectively fuses geometric and semantic information extracted from Google Street View images with centerline curve geometry, surface topology, and various relevant metadata to construct extremely accurate geometric representations of bridges. Then, using class statistics available in the literature for bridge structural properties, the framework generates structural models. Both the performance of the geometry extraction procedure and the structural modeling method proposed here are validated by comparison against the structural model of a real-life bridge developed based on as-built drawings.In principle, these models can be utilized to assess physical damage for any type of hazard, but in this study, the focus is limited to seismic applications. Thus to relate the damage resulting from seismic demands from ground shaking, bridge-specific fragility functions are developed for 100 bridge structures in the immediate surroundings of Ports of Los Angeles and Long Beach. Using these fragility curves, the physical damage resulting from a magnitude 7.3 scenario earthquake on Palos Verdes fault is predicted. Subsequently, the effects of the bridge infrastructure damage to the transportation patterns in the Los Angeles metropolitan area are investigated in terms of various resilience metrics

On Triangular Splines:CAD and Quadrature

The standard representation of CAD (computer aided design) models is based on the boundary representation (B-reps) with trimmed and (topologically) stitched tensor-product NURBS patches. Due to trimming, this leads to gaps and overlaps in the models. While these can be made arbitrarily small for visualisation and manufacturing purposes, they still pose problems in downstream applications such as (isogeometric) analysis and 3D printing. It is therefore worthwhile to investigate conversion methods which (necessarily approximately) convert these models into water-tight or even smooth representations. After briefly surveying existing conversion methods, we will focus on techniques that convert CAD models into triangular spline surfaces of various levels of continuity. In the second part, we will investigate efficient quadrature rules for triangular spline space

International Conference on Continuous Optimization (ICCOPT) 2019 Conference Book

The Sixth International Conference on Continuous Optimization took place on the campus of the Technical University of Berlin, August 3-8, 2019. The ICCOPT is a flagship conference of the Mathematical Optimization Society (MOS), organized every three years. ICCOPT 2019 was hosted by the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) Berlin. It included a Summer School and a Conference with a series of plenary and semi-plenary talks, organized and contributed sessions, and poster sessions. This book comprises the full conference program. It contains, in particular, the scientific program in survey style as well as with all details, and information on the social program, the venue, special meetings, and more

Numerical predictions of the hydrodynamic drag of the plat-o tidal energy converter and comparison with measurements in a water channel

Tidal energy industry is currently involved in a strong growth phase and it is expected to play an important role as far as meeting the renewable energy targets is concerned. The product development strategy adopted by tidal energy companies is nowadays broadly based on a gradual increase of the prototypes scaling factor. Thus, the prediction of important engineering quantities such as drag at intermediate stages becomes of vital importance for developers. The diversity and complexity of the geometries adopted by second-generation tidal energy converters preclude the use of already existing drag scaling methods conceived for specific applications such as ship design. In this context, numerical simulations are regarded as a suitable alternative. This study addresses the use of Computational Fluid Dynamics (CFD) to determine the drag of the unique tidal energy converter developed by Sustainable Marine Energy (SME) called PLAT-O. Several preliminary simulations were performed on the isolated PLAT-O components. The results were compared with previous CFD studies devoted to similar form bodies and good agreement was found. Following this stage, the drag predictions on the whole PLAT-O device were undertaken and compared to existing experimental data. Significant differences between them were observed. This work has demonstrated that a RANS flow solver is not an efficient tool to predict the resistance of a small-scale PLAT-O device. In addition, this study has predicted that CFD will be suitable to assess the design of larger-scale prototypes.Outgoin

Geometric deep learning: going beyond Euclidean data

Many scientific fields study data with an underlying structure that is a non-Euclidean space. Some examples include social networks in computational social sciences, sensor networks in communications, functional networks in brain imaging, regulatory networks in genetics, and meshed surfaces in computer graphics. In many applications, such geometric data are large and complex (in the case of social networks, on the scale of billions), and are natural targets for machine learning techniques. In particular, we would like to use deep neural networks, which have recently proven to be powerful tools for a broad range of problems from computer vision, natural language processing, and audio analysis. However, these tools have been most successful on data with an underlying Euclidean or grid-like structure, and in cases where the invariances of these structures are built into networks used to model them. Geometric deep learning is an umbrella term for emerging techniques attempting to generalize (structured) deep neural models to non-Euclidean domains such as graphs and manifolds. The purpose of this paper is to overview different examples of geometric deep learning problems and present available solutions, key difficulties, applications, and future research directions in this nascent field