We discuss if and how it is possible to develop meaningful e-assessments in Engineering Mechanics. The focus is on complex example problems, resembling traditional paper-pencil exams. Moreover, the switch to e-assessments should be as transparent as possible for the students, i.e., it shouldn’t lead to additional difficulties, while still maintaining sufficiently high discrimination indices for all questions. Example problems have been designed in such a way, that it is possible to account for a great variety of inputs ranging from graphical to numerical and algebraic as well as string input types. Thanks to the implementation of random variables it is even possible to create an individual set of initial values for every participant. Additionally, when dealing with complex example problems errors carried forward have to be taken into account. Different approaches to do so are detailed and discussed, e.g., pre-defined paths for sub-questions, usage of students’ previous inputs or decision trees. The main finding is that complex example problems in Engineering Mechanics can very well be used in e-assessments if the design of these questions is well structured into meaningful sub-questions and errors carried forward are accounted for.

Im „Stufenplan Digitales Planen und Bauen“ des Bundesministeriums für Verkehr und digitale Infrastruktur wird für öffentliche Infrastrukturprojekte gefordert, die Methode Building Information Modeling anzuwenden. Daraus folgt die Forderung der Bundesfachabteilung Spezialtiefbau im Hauptverband der Deutschen Bauindustrie e. V. nach einem Fachmodell Baugrund vom Auftraggeber. Das Fachmodell soll aus der Erdoberfläche in Form eines digitalen Geländemodells, dem Bodenschichtenmodell und auch den zugrunde liegenden Daten von Bohrungen, Vermessung etc. bestehen sowie Schichtinformationen und Bodenkennwerte des Baugrunds auf der Basis des geotechnischen Berichts enthalten. Der Verlauf der Schichtgrenzen zwischen den Aufschlüssen ist dabei durch Anwendung von Interpolationsmethoden abzuleiten und durch manuelle Nachbearbeitung anzupassen. Um diese Herausforderungen zu meistern, werden hierfür verschiedene Softwares evaluiert.

Computer aided orthopedic surgery suffers from low clinical adoption, despite increased accuracy and patient safety. This can partly be attributed to cumbersome and often radiation intensive registration methods. Emerging RGB-D sensors combined with artificial intelligence data-driven methods have the potential to streamline these procedures. However, developing such methods requires vast amount of data. To this end, a multi-modal approach that enables acquisition of large clinical data, tailored to pedicle screw placement, using RGB-D sensors and a co-calibrated high-end optical tracking system was developed. The resulting dataset comprises RGB-D recordings of pedicle screw placement along with individually tracked ground truth poses and shapes of spine levels L1–L5 from ten cadaveric specimens. Besides a detailed description of our setup, quantitative and qualitative outcome measures are provided. We found a mean target registration error of 1.5 mm. The median deviation between measured and ground truth bone surface was 2.4 mm. In addition, a surgeon rated the overall alignment based on 10% random samples as 5.8 on a scale from 1 to 6. Generation of labeled RGB-D data for orthopedic interventions with satisfactory accuracy is feasible, and its publication shall promote future development of data-driven artificial intelligence methods for fast and reliable intraoperative registration.

Classification of outdoor point clouds is an intensely studied topic, particularly with respect to the separation of vegetation from the terrain and manmade structures. In the presence of many overhanging and vertical structures, the (relative) height is no longer a reliable criterion for such a separation. An alternative would be to apply supervised classification; however, thousands of examples are typically required for appropriate training. In this paper, an unsupervised and rotation-invariant method is presented and evaluated for three datasets with very different characteristics. The method allows us to detect planar patches by filtering and clustering so-called superpoints, whereby the well-known but suitably modified random sampling and consensus (RANSAC) approach plays a key role for plane estimation in outlier-rich data. The performance of our method is compared to that produced by supervised classifiers common for remote sensing settings: random forest as learner and feature sets for point cloud processing, like covariance-based features or point descriptors. It is shown that for point clouds resulting from airborne laser scans, the detection accuracy of the proposed method is over 96% and, as such, higher than that of standard supervised classification approaches. Because of artifacts caused by interpolation during 3D stereo matching, the overall accuracy was lower for photogrammetric point clouds (74–77%). However, using additional salient features, such as the normalized green–red difference index, the results became more accurate and less dependent on the data source.