Browsing by Author "Abdelaal, Moataz"

Now showing 1 - 5 of 5

Open Access
Advanced timber construction industry : a quantitative review of 646 global design and construction stakeholders
(2023) Orozco, Luis; Svatoš-Ražnjević, Hana; Wagner, Hans Jakob; Abdelaal, Moataz; Amtsberg, Felix; Weiskopf, Daniel; Menges, Achim
There has been a multi-storey timber construction boom since the start of the millennium. While there is now a body of research on trends, benefits, and disadvantages of timber construction, there is not yet literature on the wider market or the impact of stakeholders on it. This research investigates the (i) architects, (ii) engineers, and (iii) manufacturers involved in the realization of 300 contemporary multi-storey timber buildings from an existing survey. The analysis is based on data sourced from stakeholder websites and the building survey. It evaluates the perceived level of timber expertise of stakeholders based on service categorization and stakeholder type and relates them to the buildings they worked on. The research uses quantitative methods to answer qualitative questions on the connection between architectural variety in timber construction and the stakeholders involved. Interconnectivity between stakeholders and projects is visualized in an interactive network graph. The study shows a segmented mass timber market with relatively few impactful design and construction stakeholders, mostly located in central and northern Europe. It also identifies fabricators as the largest group of innovators advancing the industry and enabling the construction of more complex projects. It reveals the importance of collaboration and knowledge sharing for the industry’s growth.
Open Access
Multi-time scale dynamic graph visualization
(2017) Abdelaal, Moataz
Dynamic graphs build complex data structures composed of vertices, edges, and time steps. Visualizing these evolving structures is a challenging task when we are not only interested in the dynamics based on a fixed time granularity, but also in exploring the subsequences at multiple of those time granularities. In this thesis, we introduce a multi-timescale dynamic graph visualization. The dynamic graph is displayed with interleaved parallel edge splatting focusing on visual scalability to generate an overview of dynamic graph patterns first. Different time scales can then be displayed in a vertically stacked scale-to-space mapping showing finer time granularities in linked side-by-side views, which is in particular useful for comparison tasks. To obtain an uncluttered view of the evolving graph patterns, the data is first preprocessed by clustering and vertex ordering techniques. It is then plotted in a 1D bipartite layout, splatted, smoothed, and enhanced with contour lines for perceptual augmentation. Inner- and inter-scale comparisons are supported visually and algorithmically.
Open Access
STEP : sequence of time-aligned edge plots
(2024) Abdelaal, Moataz; Kannenberg, Fabian; Lhuillier, Antoine; Hlawatsch, Marcel; Menges, Achim; Weiskopf, Daniel
We present sequence of time-aligned edge plots (STEP) : a sequence- and edge-scalable visualization of dynamic networks and, more broadly, graph ensembles. We construct the graph sequence by ordering the individual graphs based on specific criteria, such as time for dynamic networks. To achieve scalability with respect to long sequences, we partition the sequence into equal-sized subsequences. Each subsequence is represented by a horizontal axis placed between two vertical axes. The horizontal axis depicts the order within the subsequence, while the two vertical axes depict the source and destination vertices. Edges within each subsequence are depicted as segmented lines extending from the source vertices on the left to the destination vertices on the right throughout the entire subsequence, and only the segments corresponding to the sequence members where the edges occur are drawn. By partitioning the sequence, STEP provides an overview of the graphs’ structural changes and avoids aspect ratio distortion. We showcase the utility of STEP for two realistic datasets. Additionally, we evaluate our approach by qualitatively comparing it against three state-of-the-art techniques using synthetic graphs with varying complexities. Furthermore, we evaluate the generalizability of STEP by applying it to a graph ensemble dataset from the architecture domain.
Open Access
Visual analysis of fitness landscapes in architectural design optimization
(2024) Abdelaal, Moataz; Galuschka, Marcel; Zorn, Max; Kannenberg, Fabian; Menges, Achim; Wortmann, Thomas; Weiskopf, Daniel; Kurzhals, Kuno
In architectural design optimization, fitness landscapes are used to visualize design space parameters in relation to one or more objective functions for which they are being optimized. In our design study with domain experts, we developed a visual analytics framework for exploring and analyzing fitness landscapes spanning data, projection, and visualization layers. Within the data layer, we employ two surrogate models and three sampling strategies to efficiently generate a wide array of landscapes. On the projection layer, we use star coordinates and UMAP as two alternative methods for obtaining a 2D embedding of the design space. Our interactive user interface can visualize fitness landscapes as a continuous density map or a discrete glyph-based map. We investigate the influence of surrogate models and sampling strategies on the resulting fitness landscapes in a parameter study. Additionally, we present findings from a user study ( N = 12), revealing how experts’ preferences regarding projection methods and visual representations may be influenced by their level of expertise, characteristics of the techniques, and the specific task at hand. Furthermore, we demonstrate the usability and usefulness of our framework by a case study from the architecture domain, involving one domain expert.
Open Access
Visual analytics for nonlinear programming in robot motion planning
(2022) Hägele, David; Abdelaal, Moataz; Oguz, Ozgur S.; Toussaint, Marc; Weiskopf, Daniel
Nonlinear programming is a complex methodology where a problem is mathematically expressed in terms of optimality while imposing constraints on feasibility. Such problems are formulated by humans and solved by optimization algorithms. We support domain experts in their challenging tasks of understanding and troubleshooting optimization runs of intricate and high-dimensional nonlinear programs through a visual analytics system. The system was designed for our collaborators’ robot motion planning problems, but is domain agnostic in most parts of the visualizations. It allows for an exploration of the iterative solving process of a nonlinear program through several linked views of the computational process. We give insights into this design study, demonstrate our system for selected real-world cases, and discuss the extension of visualization and visual analytics methods for nonlinear programming.