Data science

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 - 16 Numerical investigation of shear cracking in concrete beams

The maintenance and assessment of bridge infrastructure is critical to the economy, as these structures are vital for transportation and commerce Many concrete bridges are aging and may not comply with modern technical standards due to outdated design methods and increased traffic loads. One of the failing mechanisms detected when recalculating existing bridges in accordance with the recalculation guidelines is insufficient shear load-bearing capacity, leading to the premature demolition of numerous bridges.

This project aims to model concrete beams with varying lengths and reinforcement ratios using ATENA software to simulate and analyze shear cracking behavior. The objective is to develop a reliable numerical model that accurately predicts shear cracking, providing a foundation for future experimental testing. Based on the key aspects, the following tasks should be solved:

Literature review on shear cracking in concrete
Literature review on ATENA software and shear modeling parameters
Development of a numerical model with ATENA software
Evaluation of shear cracking patterns
Perform parametric studies
Summarize all the results to draw conclusions and outlooks.

Contact:
Kleo Lila
0351 463 40471

F - 13 Detection of multidimensional states with fiber optics

Full title: Measuring multi-dimensional tension and strain fields in concrete structures with distributed fiber optic sensors

Fiber optic sensors make quasi-continuous strain measurements possible. Therefore, this measurement technique has a great potential for monitoring the behavior of structures, such as global deformations. By mounting those linear sensors in a mesh-like manner, the three-dimensional deformation state of the structure can be detected. Hence, approaches to estimate two- and three-dimensional strain, stress and deformation fields from linear strain data are to be developed in this thesis. The algorithms are to be implemented into a Python software framework and validated by means of experimental data.

The work consists of the following tasks:

Literature research regarding distributed fiber optic sensors and efficient mapping approaches
Implementation of (geo-referenced) localization and orientation of the fiber optic sensor in three-dimensional space
Linking the sensor to virtual models of the structure
Reconstruction of the strain field of the structure by means of interpolation or numerical deformation calculations
Calculation of the stress field and deformation state by means of analytical or numerical methods
Implementation of the (analytical) algorithms into the Python software framework
Validation of the developed methods using experimental data

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

Contact:
Bertram Richter
0351 463 32822

F - 10 Quality measures for distributed fiber optic sensor data

Fiber optic sensors make quasi-continuous strain measurements possible. Therefore, the interest in deployment of this measurement technique grows for structural health monitoring, especially for crack width monitoring. The quality of the resulting data depends on several faktors (sensor type, application method, measurement settings, etc.). Different sensor applications and their fitners for crack width monitoring were investigated. Due to missing quality measures, objective assessments are not yet possible.

Hence, such quality measures for fiber optic sensor data are to be developed in this thesis. The algorithms are to be implemented into a Python software framework and validated by means of experimental data. The work consists of the following tasks:

Literature research regarding distributed fiber optic sensors and data quality measures
Discussion of existing approaches for quality assessment and development of own approaches
Development of quality measure for technical disturbances (noise, errors, missing data)
Development of quality measure for strain peaks (detectability, stability)
Implementation of the algorithms into the Python software framework
Validation of the developed methods using experimental data
Analysis of the experiments regarding data qualtity and deriving setup recommendations

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

Contact:
Bertram Richter
0351 463 32822

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