Current Organizations / Institutes

The organizations/institutes from the scholar's latest article


Steven Robert Lorenzen has a total of 12 co-authors. Here are the top co-authors by number of shared publications:


Latest Publications

  • Open Access

    Influence Surfaces for Reconstructing Foot-Specific Ground Reaction Forces and Location from Dual-Contact Measurements on a Single Force Plate

    by Max Johannes Alois Fritzsche, Hagen Berthold, Steven Robert Lorenzen, Clemens Hübler

    Force plates are the gold standard for measuring ground reaction forces, but their reliance on equilibrium equations limits them to single-contact measurements. During the double support phase of walking, when both feet touch the ground simultaneously, conventional force plates yield only the combined force and center of pressure. The individual foot contributions cannot be separated. This paper applies influence surface theory to extend force plate analysis beyond equilibrium-based methods. Instead of evaluating global force and moment balances, influence surfaces characterize how loads at different positions contribute to structural responses at individual supports. This enables load identification for multiple simultaneous contacts, with the number of separable loads determined by the available sensor count.The influence surfaces are trained from walking data using Gaussian Process Regression, capturing the real plate behaviour without requiring finite element modelling. To reduce complexity, the inverse problem is addressed sequentially. First, load positions are estimated. Second, the horizontal force components are determined.The method is validated on a 2.5m x 1.0m force plate using static tests with known load positions and walking trials with synchronized instrumented insoles providing the load distribution. The results show that the approach successfully reconstructs positions and three-dimensional ground reaction forces for both contacts, generally achieving accuracy comparable to equilibrium-based single-contact measurements while preserving natural gait.

  • BIM‐Integration von Sensordaten aus dem Monitoring von Eisenbahnbrücken im Betrieb

    by Jascha Brötzmann, Günther Grunert, Christian‐Dominik Thiele, Uwe Rüppel, Steven Lorenzen

    Das Konzept des Building Information Modeling, kurz BIM, wird im AEC‐Sektor (Architektur, Ingenieurwesen, Bauwesen) v. a. in der Planung immer häufiger angewendet. Dies ist auf mehrere Faktoren zurückzuführen, darunter rechtliche Rahmenbedingungen sowie Fortschritte im Bereich der Softwareentwicklung und Datenverarbeitung. Dieser kontinuierliche Fortschritt führt zu einem breiten Spektrum an Anwendungsmöglichkeiten. So lässt sich das BIM‐Konzept daher auch für Anwendungen im Betrieb einsetzen, wie bspw. beim Monitoring von Eisenbahnbrücken. Hier werden Brückenbauwerke und Züge mit Sensoren ausgestattet, um die Brücken in Echtzeit überwachen und bewerten zu können. Die dabei aufgenommenen Sensordaten können in bestehende BIM‐Modelle integriert werden, um u. a. ein Update von Tragwerksmodellen durchzuführen und so realitätsnähere Tragwerksmodelle zu erhalten. Weiterhin ist auf der Grundlage der Auswertung der Sensordaten eine frühzeitige Schadenserkennung möglich und die aktualisierten Modelle können eine Abschätzung der Folgen erlauben. Diese Untersuchungen sind Teil des Forschungsprojekts ZEKISS, bei dem die Autoren hierfür Lösungsmöglichkeiten erarbeiten.


  • Open Access

    Virtual Axle Detector Based on Analysis of Bridge Acceleration Measurements by Fully Convolutional Network

    by Steven Robert Lorenzen, Henrik Riedel, Maximilian Michael Rupp, Leon Schmeiser, Hagen Berthold, Andrei Firus, Jens Schneider

    In the practical application of the Bridge Weigh-In-Motion (BWIM) methods, the position of the wheels or axles during the passage of a vehicle is a prerequisite in most cases. To avoid the use of conventional axle detectors and bridge type-specific methods, we propose a novel method for axle detection using accelerometers placed arbitrarily on a bridge. In order to develop a model that is as simple and comprehensible as possible, the axle detection task is implemented as a binary classification problem instead of a regression problem. The model is implemented as a Fully Convolutional Network to process signals in the form of Continuous Wavelet Transforms. This allows passages of any length to be processed in a single step with maximum efficiency while utilising multiple scales in a single evaluation. This allows our method to use acceleration signals from any location on the bridge structure and act as Virtual Axle Detectors (VADs) without being limited to specific structural types of bridges. To test the proposed method, we analysed 3787 train passages recorded on a steel trough railway bridge of a long-distance traffic line. Results of the measurement data show that our model detects 95% of the axles, which means that 128,599 out of 134,800 previously unseen axles were correctly detected. In total, 90% of the axles were detected with a maximum spatial error of 20 cm, at a maximum velocity of vmax=56.3m/s. The analysis shows that our developed model can use accelerometers as VADs even under real operating conditions.