Steven Robert Lorenzen
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:
- Clemens Hübler
TU Darmstadt, Institute of Structural Mechanics and Design, Franziska-Braun-Str. 3, 64287 Darmstadt, Germany
3 shared publications - Hagen Berthold
Institute for Structural Mechanics and Design, Technical University of Darmstadt, 64287 Darmstadt, Germany
3 shared publications - Henrik Riedel
Glass Competence Center, Institute of Structural Mechanics and Design, Technical University of Darmstadt, Darmstadt, Germany
2 shared publications - Max Johannes Alois Fritzsche
Technical University of Darmstadt
2 shared publications - Andrei Firus
SEA Tec
1 shared publication Christian‐Dominik Thiele
Technische Universität Darmstadt Institut für Numerische Methoden und Informatik im Bauwesen, IIB Franziska-Braun-Str. 7 64287 Darmstadt
1 shared publication
Latest Publications
- Open Access
- 16 June 2026
- Published by Elsevier in Measurement
- Vol. 284
Influence surfaces for reconstructing individual point loads and positions from multi-contact measurements on a single force plate
- 16 June 2026
- Published by Elsevier in Measurement
- Vol. 284
Open AccessInfluence surfaces for reconstructing individual point loads and positions from multi-contact measurements on a single force plate
- Open Access
- 13 February 2026
- Published by Elsevier in SSRN Electronic Journal
Influence Surfaces for Reconstructing Foot-Specific Ground Reaction Forces and Location from Dual-Contact Measurements on a Single Force Plate
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.
- 13 February 2026
- Published by Elsevier in SSRN Electronic Journal
Open AccessInfluence Surfaces for Reconstructing Foot-Specific Ground Reaction Forces and Location from Dual-Contact Measurements on a Single Force Plate
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.
- Open Access
- 17 December 2024
- Published by Elsevier in Engineering Applications of Artificial Intelligence
- Vol. 141
Object-size-driven design of convolutional neural networks: virtual axle detection based on raw data
- 17 December 2024
- Published by Elsevier in Engineering Applications of Artificial Intelligence
- Vol. 141
Open AccessObject-size-driven design of convolutional neural networks: virtual axle detection based on raw data
- 19 February 2024
- Published by Wiley in Bautechnik
- Vol. 101 (3) , 166-173
BIM‐Integration von Sensordaten aus dem Monitoring von Eisenbahnbrücken im Betrieb
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.
- 19 February 2024
- Published by Wiley in Bautechnik
- Vol. 101 (3) , 166-173
BIM‐Integration von Sensordaten aus dem Monitoring von Eisenbahnbrücken im Betrieb
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
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 . The analysis shows that our developed model can use accelerometers as VADs even under real operating conditions.Open AccessVirtual Axle Detector Based on Analysis of Bridge Acceleration Measurements by Fully Convolutional Network
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 . The analysis shows that our developed model can use accelerometers as VADs even under real operating conditions.