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Graph Based Disassembly Sequencing with Structural and Stability Constraints
Item type: Conference Paper
Karsan, Zain; Dillenburger, Benjamin; De Wolf, Catherine (2025)
Disassembly is an important strategy in achieving material circularity and closing the loop in material flow. While disassembly sequencing is heavily studied in manufacturing, few examples consider the constraints present in construction. To address this gap we propose a graph based method for disassembly sequencing with constraints on stability and internal stress. We test our algorithm on three frame structures of increasing complexity and element count using construction specific heuristics to determine source nodes and disassembly direction. Our algorithm can compute feasible disassembly sequences with sufficient speed to support applications in online robotic path planning.
Dataset for: Momentum space imaging reveals chemical gating of 2D electron and hole gases in nitride heterostructures
Item type: Dataset
Della Valle, Enrico (2026)
Electrolyte Structure Governs Formate Oxidation in Water-in-Salt Systems
Item type: Journal Article
Trapp, Katharina; Kosasang, Soracha; Ingenmey, Johannes; et al. (2026)
We disentangle reactant concentration from local structural effects in water-in-salt electrolytes using the formate oxidation reaction on Pt. First, we observe that formate oxidation currents plateau at high concentrations. Using molecular dynamics and NMR spectroscopy, we attribute this observation to ion clustering of the kosmotropic formate reactant, which reduces conductivity and impedes reactant transport. Then, we demonstrate that this limitation can be overcome by introducing a chaotropic anion (perchlorate) that disrupts clustering and facilitates a further increase in formate oxidation currents. However, when perchlorate is introduced in excess, the hydrogen-bonding network is disrupted, which leads to hindered proton transport, local acidification, and enhanced CO poisoning, as evidenced by SEIRAS. Our findings demonstrate a direct link between bulk electrolyte structure and catalytic activity, which can be used to enhance catalytic performance at high reactant concentrations.
Multi-Robotic System for Welded Steel Disassembly
Item type: Conference Paper
Karsan, Zain; Dillenburger, Benjamin; De Wolf, Catherine (2025)
Material reuse is an important strategy in reducing the embodied carbon of future construction. However, reuse is challenged by the complexity and cost of careful disassembly. While robotic automation of on-site tasks such as worksite monitoring, plaster finishing or excavation are becoming more commonplace, disassembly automation is still confined to factory work cells. Therefore, we propose the use of a multi-robotic system for on-site disassembly operations. Our system involves two robots that work in conjunction to remove structural steel elements from a welded assembly. We design and build a crane mounted robot for lifting and work-holding, which works alongside a conventional industrial robot outfitted with a plasma cutting torch. To perform disassembly tasks, we develop a workflow involving scene reconstruction of RGB-D data, collision-aware path planning and a user interface to coordinate the actions of the crane mounted robot. We test our setup on the disassembly of a beam from a prototypical structure and compare our operation time with observations from a case study in practice. We demonstrate reductions in cutting and removal time and show that robotic automation of construction tools could support more efficient building disassembly, making material reuse a more feasible reality.
Quantifying Model Selection Uncertainty in Structural Analysis: Methodology and Application
Item type: Journal Article
Yang, Ya-Heng; Becker, Tracy C. (2026)
With increasing focus on complex engineering systems under rare events, computational models are critical for predictions due to the scarcity or absence of data. However, selecting an appropriate model can be challenging. Using a single model without available test calibration could result in significant bias in performance predictions. A case study of a steel moment frame reveals substantial discrepancies between predictions given different model selections, particularly under increasing ground motion intensities. To address this challenge, this paper proposes a model averaging methodology based on the logic tree approach to incorporate multiple models and capture their full spectrum of potential outcomes. A combination of dimensionality reduction techniques and Bayesian inference is employed to determine the weight of each model candidate. The weight is represented as a Dirichlet prior, which is determined by incorporating expert knowledge and structural component testing results. This approach enables the assignment of a more informative prior even when no observational data is available, while also leaving room for updating the prior if data becomes available. By leveraging model averaging and uncertainty propagation techniques to address model uncertainty in structural analysis, a response interval is then generated for the quantity of interest. This methodology provides a more comprehensive, informative, and feasible assessment to address model selection uncertainty to improve predictive capabilities and enhance the ability of engineers to treat design in a probabilistic manner. In its present formulation, however, the approach is applicable to precollapse response regimes where finite response quantities can be defined and does not explicitly address collapse-state realizations.