11 to 20 of 468 Results
Sep 30, 2026Lights-Off On-Site Timber Construction
This Dataverse contains the software, hardware design resources, and experimental data developed for a multi-robot construction system for the automated on-site adhesive bonding of prefabricated cross-laminated timber (CLT) floor panels. The system combines a digitally controlled tower crane with compact robots temporarily embedded within the panel... |
Sep 28, 2026
The Using Wood Well (UWW) project investigates the use of timber in construction, with the aim of improving material efficiency and supporting circular economy principles. The project analyses building data at multiple scales, ranging from whole-building timber volumes to frame- and floor-level material composition, and develops metrics for assessi... |
Tabular Data - 151.9 KB - 22 Variables, 240 Observations - UNF:6:NOZENC/TgjE+aRQsa/u0fQ==
Contains complementary project metadata for the 240 multi-storey timber buildings. The file provides a summary of key project information and classification variables, standardized country identifiers, documentation availability indicators (drawings, photographs, videos, reports, books), and source references. It supports filtering and evaluation o... |
Sep 3, 2026
The research aims to develop core technologies for automated on-site timber construction with a primary focus on automating the on-site positioning, joining, and gluing of large-scale timber elements. In recent decades, the timber construction industry has significantly gained importance and competitiveness through the development of digital proces... |
ZIP Archive - 1.1 GB -
MD5: 46dae7b31080431a7267073671d85ce8
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Jul 22, 2026
This dataverse provides datasets, documentation, and research outputs generated as part of the research conducted by the Material Programming Research Group at the Institute for Computational Design and Construction (ICD), University of Stuttgart. It covers research on programmable material systems, self-shaping structures, and their computational... |
Unknown - 2.2 MB -
MD5: 872cef509dee8c8b912f90bfb58b8d30
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Unknown - 2.2 MB -
MD5: 82628c7c04968e1e5473a5a2cb344918
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Unknown - 44 B -
MD5: aad3d560a114fb7a194b50584959687b
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Unknown - 93.7 KB -
MD5: 5258e60efac0215877d990c5330ef867
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