Data science

F - 21 Fiber-Optic Measurements Under High-Frequency Load

Ensuring the safety of aging infrastructure is of great importance. Distributed fiber-optic sensors (DFOS) are increasingly used for early damage detection, as they continuously measure strain over extended lengths with high spatial resolution, thereby offering the potential to detect cracks in concrete structures. However, challenges arise with high-frequency strain changes, such as those caused by traffic loads on bridges. These can impair the quality of the measurement data and thus the reliability of crack detection.

As part of this project, measurement data from experiments on the openLAB research bridge will be used to evaluate the potential of DFOS for strain measurement and crack detection under dynamic loading. The work consists of the following tasks:

• Literature review on distributed fiber-optic sensors with a focus on measurements under dynamic loading
• Analysis of experimental data to investigate the influence of high-frequency loads on the availability and quality of measurement data, as well as on the reliability of crack width calculations
• Proposal of solutions to improve the measurement data quality of DFOS under dynamic loading

Details of the task will be refined prior and while working on the project. Interest/experience in software development/programming is advantageous.

Contact:
Miriam Kroschel
+49 351 463-36073

F - 19  AI-assisted multi-modal conceptual design for bridges

In the early stages of bridge design (conceptual design), engineers must quickly generate and evaluate numerous bridge alternatives to satisfy diverse terrain, environmental, structural and economic requirements. Traditional manual methods are typically time-consuming, resource-intensive, and rely heavily on designers' experiences, limiting the systematic exploration of optimal design solutions. The integration of Artificial Intelligence (AI) with multi-modal data, including numerical parameters, geometric data, textual standards and historical design data, could significantly enhance decision-making, accelerate the generation of feasible design concepts and improve early-stage evaluations regarding cost, feasibility and sustainability.

The primary objective of this research is to develop an AI-assisted multi-modal framework capable of efficiently generating and evaluating conceptual bridge designs, specifically for slab, slab-beam, and composite bridges. The approach aims to automate preliminary bridge type selection, estimation of key cross-sectional parameters, and rapid evaluation of multiple early-stage design options in terms of structural feasibility, cost-efficiency, and sustainability. Based on this objective, the following tasks are involved:

• Literature research regarding potential AI algorithms for the engineering design problem
• Collect and structure multi-modal data (project parameters, site geometry, historical designs, and design guidelines)
• Develop AI models for automatic bridge type selection and preliminary prediction of cross-sectional and geometric parameters
• Implement a rule-based validation tool to ensure conceptual designs meet structural and regulatory requirements
• Perform preliminary evaluations of material usage, construction costs, and carbon emissions for design comparison

The work is part of the research project mFUND-HyBridGen – Hybrid Bridge Generator: AI-based bridge generator with knowledge and experience data and early citizen participation. Details of the task will be refined prior and while working on the project. Interest/experience in AI-based methods for structural engineering (civil/computational engineering) is advantageous.

Contact:
Han Qian
+49 351 463 33083

F - 18 Image data requirements for 3D bridge reconstruction

Full title: Image data requirements for 3D reconstruction of bridge constructions: a parametric study

The experience of engineers plays a pivotal role in the field of bridge design, which can, at times, result in substantial disparities in quality. The analysis of existing bridge structures is complicated by the fact that design-relevant parameters are usually not freely accessible. Consequently, publicly accessible images serve as an important data source for acquiring information about bridge structures from different perspectives.

In order to derive geometric bridge parameters from these images, a precise 3D reconstruction of the structures and their surroundings is required. The Pic2Bridge research project is investigating the feasibility of and suitable methods for the 3D reconstruction of bridges based on image data.

The objective of this thesis is to conduct a parameter study, with the aim of analyzing which requirements and boundary conditions must be imposed on the images used to achieve the most optimal 3D reconstruction. Based on a given 3D reconstruction approach, the following questions, among others, will be addressed:

• How many images are required?
• From which perspectives should the images be taken?
• What overlap and image quality are required?

Possible work steps:

• Literature research on the properties and requirements of image data for 3D reconstruction
• Collection and selection of suitable image data from selected bridges
• Development of a structured concept for the implementation of the parameter study, including the selection of relevant image parameters
• Development of an evaluation concept for the numerical and visual assessment of the reconstruction results
• Carrying out the parameter study and evaluating the results

The exact task definition will be jointly coordinated and refined before the start and during the work process.

Prerequisites:

Interest or initial experience in programming with Python and common libraries (e.g., Numpy, Pandas, Matplotlib) as well as in the use of high-performance computers (HPC) are advantageous.

Contact:
Morris Benedikt Florek
+49 351 46340975

F - 1 Damage assessment with ultrasonic measurements

Full title: Damage assessment of cyclically loaded concrete structures with ultrasonic measurements

Concrete structures under a given load do not fail because they abruptly change from a "normal" state to a fracture state, but because the degradation process progresses with increasing load until material failure occurs. When subjected to mechanical loads, stresses first concentrate around material defects or interfaces at the microscale, destroying bonds between individual molecules. With increasing mechanical load, the microcracks then grow and unite, leading to the formation of macrocracks. During this process, the lattice structure of the material, which serves as a propagation medium for the stress waves of an ultrasonic pulse, is progressively changed and in this way the damage can be detected.

The objective of this thesis is to relate ultrasonic measurements of degradation evolution from concrete specimens and beams subjected to cyclic loading to hypotheses of damage accumulation. From these correlations and using concepts of robustness and redundancy, safety factors will be determined and the remaining useful life will be evaluated.

Contact:
Raúl Enrique Beltrán Gutiérrez
0351 463 33675