This work analyzes a new indoor laboratory data set looking at early fire indicators in controlled and realistic experiments representing different incipient fire scenarios. The experiments were performed within the confines of an indoor laboratory setting using multiple distributed sensor nodes on different room positions. Each sensor node collected data of particulate matter (PM), volatile organic compounds (VOC), CO, CO2, H2, UV, air temperature and humidity in terms of a multivariate time series. These data hold immense value for researchers within the machine learning and data science communities who are keen on exploring innovative advanced statistical and machine learning techniques. They serve as a valuable resource for the development of early fire detection systems. The analysis of the collected data was carried out in dependence of the manhattan distance between the fire source and the sensor node. We found that especially larger particles (> 0.5 μm) and VOCs show a significant dependency with respect to the intensity as a function of the manhattan distance to the source. Moreover, we observed differences in the propagation behavior of VOCs, PM, and CO, which are particularly relevant in incipient fire scenarios due to the presence of strand popagation effects.

Despite all their advantages, load-bearing concrete shell structures with double curvatures are not frequently in use. The main reason is the complexity of their construction. In such a context, this article starts with a brief, critical review of existing technologies while their pros and cons are highlighted. Against that background the authors then propose a new approach for the highly automated fabrication of gridshell structures from variable modules. To demonstrate the feasibility of such a new technology, a demonstrator called ConDIT 1.0, a sphere-like shell structure composed of several frames was designed and built. The frame modules were fabricated automatically using extrusion-based 3D printing and a printable, strain-hardening cement-based composite (SHCC). This article presents the design of ConDIT 1.0, the mechanical material characterization of printed SHCC, the technology of module production, the results of geometry verification for print modules using 3D scanning, and the procedure for the demonstrator’s assembly.

In the digital age, 3D as-built monitoring of structures and buildings is becoming increasingly important. It provides the basis for automated quality control and progress monitoring through as-built and as-planned model- comparisons, as well as 3D modelling for automated repair, refurbishment and reuse in existing construction. Especially for 3D model-based manufacturing processes such as additive manufacturing, efficient as-built monitoring is essen- tial to ensure the quality and accuracy of buildings and products. Therefore, three different 3D scanning devices have been evaluated for 3D as- built monitoring of concrete structures with different characteristics: An indus- trial handheld laser scanner commonly used in automotive and mechanical en- gineering, a mobile device with lidar for commercial public use, and a terrestri- al laser scanner commonly used for surveying. The devices were tested on 3D printed, cast, and assembled structures of different sizes and geometries. The scan results are compared to the as-planned models based on their dimen- sional distances using Cloud-to-Cloud Comparison (C2C). The results show that the applicability is highly dependent on the size, geometric complexity and surface texture of the concrete structures. While small and flat structures can be captured completely and with micrometer accuracy using the handheld industri- al laser scanner, terrestrial laser scanners are suitable for capturing large struc- tures and environments. The handheld, photogrammetry-based mobile device has demonstrated the broadest applicability for concrete structures. The accura- cy and coverage of the point cloud results were respectively high. Therefore, photogrammetry-based scanning with advanced mobile devices is a cost- effective, fast and portable alternative for 3D as-built monitoring and data ac- quisition of a wide range of concrete structures.