finite element simulation and model identification of an

||Chair of Structural Mechanics and Monitoring

Main Goals: Literature review; material model identification. Create an FE model of linear elasticity in FEniCS

(www.fenicsproject.org). Material model identification according to measured experimental data

(displacement field). Validate the suitability of the identified model.

Supervision:Abbas Jafari ([email protected])Dr. Thomas Titscher ([email protected])Dr. Jörg F. Unger ([email protected])Prof. Dr Eleni Chatzi ([email protected])

17. .202105

Finite element simulation and model identification of an additively manufactured structureAdditive manufacturing is a powerful way of constructing structures with complicated components and geometries. The goal of this master thesis is to develop a finite element model for an additively manufactured lattice structure and then identify constitutive parameters of the model in accordance with measured experimental data. An elastic model will be developed in FEniCSafter generating a proper mesh for the complicated geometry of the structure. An important aspect is the computation of sensitivities with respect to model parameters. Those quantities are required for the identification step, where available experimental CT-data - in the form of a deformation field under specific loading scenarios - will be used to identify the corresponding model parameters. Another goal of this project is to verify the applicability of the prepared model for the data which has been used for the identification.

Lattice structure(additively manufactured)

mailto:[email protected]


||Chair of Structural Mechanics

Main Goals: Literature review, development of suitable simplified interaction model. Virtual track ride simulation using simplified and complete interaction

model. Comparison of outputs. Parametric optimization of simulation models to approach dynamics

measured on real-world vehicles. Model augmentation via inference of unmeasured parameters. Characterization and detection of track and vehicle defects.Supervision:Prof. Dr Eleni Chatzi ([email protected]) Marcel Zurkirchen ([email protected])Cyprien Hoelzl ([email protected])Find out more: https://sites.google.com/view/omism-aims/home

28.1 .20191

Modelling of train-track interactionCritical to the safety, reliability and durability of the railway network is the early detection of faults using on-board monitoring (OBM), i.e. continuous monitoring of the infrastructure with passenger vehicles.Goal is to build a simplified model of the train-track interaction dynamics under the boundary conditions and vehicle parameter uncertainty. This model can then used to simulate the train dynamics under different operating conditions (i.e. varying speed, geometric defects, wheel condition, track type…). Provided to the students are: a full train-track multi-body model along with geometry, defects and the infrastructure types.

Topic 1: Modelling of train-track interaction (1/1)


https://sites.google.com/view/omism-aims/home


Main Goals: Literature review Development of hardware and software. Testing+Calibration on test stand. Installation of system on real-world vehicle (accompanying SBB

measurement ride). Comparison of measured diagnostic vehicle accelerations with sensor

node accelerations.Supervision:Prof. Dr Eleni Chatzi ([email protected]) Marcel Zurkirchen ([email protected])Cyprien Hoelzl ([email protected])Find out more: https://sites.google.com/view/omism-aims/home

28.1 .20191

Wireless sensor nodes for railway monitoringCritical to the safety, reliability and durability of the railway network is the early detection of faults using on-board monitoring (OBM), i.e. continuous monitoring of the infrastructure with passenger vehicles.Goal is to build an inexpensive wireless sensor node based system that collects information, processes and ultimately transmits it to the infrastructure managers. The hardware in combination with software will be used for infrastructure diagnosis.

Topic 2: Wireless sensor nodes for railway monitoring (1/1)


https://sites.google.com/view/omism-aims/home


Supervision:Prof. Dr Eleni Chatzi ([email protected])

Dr. Yves Reuland ([email protected])

Panagiotis Martakis ([email protected])

11/9/2020

Structural Health Monitoring for the seismic assessment of existingstructuresA large part of the existing building stock in central European countries is not designed to fulfil current, if any, seismic standards. In addition, most of them have already exceeded their design life span and influence of ageing on material properties remains unknown. As a consequence, seismic vulnerability of the existing building stock is nowadays highly uncertain.

This work focuses on the development of a parametric numerical model for the automated estimation of uncertain modelling properties based on real data. Dynamic measurements on real buildings will be conducted and the collected data will serve as reference for the validation of modelling assumptions. Starting from a generic building, the developed model should allow for the generation of multiple building configurations, in order to estimate the nonlinear seismic response of a “family” of structures. Parametric studies on the effect of different geometric and material properties on the seismic response will be conducted, in order to explore the impact of various structural features on the seismic performance. Main Goals: Develop a parametric numerical model for the automated model updating and the generation of multiple building configurations, based on a

generic one.

Conduct dynamic measurements on real buildings and use the data to update the numerical models. Evaluate the seismic performance of the calibrated models and compare the results to the ones obtained before model updating.

Starting from the monitored structures, use the parametric model to generate “families” of buildings and explore the importance of various structural features on the seismic performance.





11/9/2020

Machine learning for building characterisation based on expected seismic performanceA large part of the existing building stock in central European countries is not designed to fulfil current, if any, seismic standards. In addition, most of them have already exceeded their design life span and influence of ageing on material properties remains unknown. As a consequence, seismic vulnerability of the existing building stock is nowadays highly uncertain.

This work focuses on the deployment of Machine Learning tools for the direct connection of structural features with seismic performance metrics. To this end, building databases will be exploited for the extraction of structural features and numerical models will be evaluated for the generation of response metrics. Ideally, a properly trained network will provide estimations of the seismic performance of buildings based exclusively on structural features, without the need of numerical analysis.Main Goals: Explore available building databases that could be exploited for the structural building characterisation.

Extract structural features from available databases of existing buildings, available numerical models and dynamic measurements of real buildings.

Investigate the current state of practice regarding seismic performance metrics and structural features that characterize the existing buildings.

Generate seismic performance metrics from available numerical models.

Deploy Machine learning tools to connect structural features with seismic performance metrics





11/9/2020

BIM-aided vulnerability assessment of existing structures

A large part of the existing building stock in central European countries is not designed to fulfil current, if any, seismic standards. In addition, most of them have already exceeded their design life span and influence of ageing on material properties remains unknown. As a consequence, seismic vulnerability of the existing building stock is nowadays highly uncertain.

This work focuses on development of an interface for the automated extraction of structural features from 3D BIM models. These features can be further used for the typological characterisation of buildings and finally for the vulnerability assessment of existing structures at city-scale. Automatizing the extraction of structural features from BIM models will enable designers to assess effortless the seismic vulnerability, while the impact of model manipulations in terms of vulnerability will be directly estimated into the BIM model. Ultimately, after integrating properly tuned artificial intelligence networks, this interface will allow for the prediction of the seismic performance of the studied building without the need of numerical analysis. Main Goals: Investigate the current state of practice regarding seismic vulnerability assessment at city-scale.

Explore building typologies and structural features that characterize the existing buildings. Develop an automated interface in order to extract structural features directly from 3D BIM models.

Assess the vulnerability of characteristic buildings based on the extracted features.


Main Goals:• Detect damage using sensor data and automatically predict damage severity.• Combine sensor data and models to reduce uncertainties in predicting building

responses.• Conduct dynamic measurements in real buildings undergoing demolition.• Understand typical simplifications and assumptions in modelling buildings.

Takeaways :This Master thesis allows students to acquire/refine skills in key parts of modern structural engineering: time-history analysis, non-linear dynamics, interpretation of sensor data, model assumptions, structural-health monitoring.

11.09.2020

Smart structures: using sensor data to assess seismic damageA large part of the existing building stock in central European countries has notbeen designed to comply with current, if any, seismic standards. In addition, most ofthem have exceeded their designed service span and influence of ageing onmaterial properties remains unknown. Therefore, seismic vulnerability of theexisting building stock is highly uncertain.

Many uncertainties accumulate during nonlinear simulations of building responsesto earthquakes (ground motions, simplified nonlinearity formulation and geometricalrepresentation, material properties, etc.). With permanently installed sensors, thebuilding response can be directly measured, thereby reducing uncertaintiessignificantly.With appropriate indicators of damage, processing of sensor data allows for a near-real-time and objective assessment of damage after earthquakes.

Supervision:Prof. Dr Eleni Chatzi ([email protected]) Dr Yves Reuland ([email protected]) Panagiotis Martakis ([email protected])


||Chair of Structural Mechanics and Monitoring 04.11.2020

DeepOpt: Acceleration of topology optimization in elasticity with machinelearning

Gradient-based topology optimization in linear elasticity is a well established procedure. Through adding or removing material in an iterative manner based on mathematically derived gradients of changes in the structure’s behavior according to changes in the geometry, structures for optimized stiffness-to-weight ratio can be automatically derived. Unfortunately, this iterative procedure is relatively computationally expensive, since for each gradient computation a full finite element solution needs to be performed.It is proposed to leverage modern deep learning techniques, to accelerate 2D structural topology optimization. The student will first have to create a dataset of boundary-conditions paired with optimized shapes and intermediate optimized designs for 2D elasticity. All shapes are going to be defined on a 2D grid. This is going to be performed using existing python code. The pairs of boundary conditions/optimized shape are going to be used as a training dataset for a neural network, which will trained on how to generate directly the optimized shape directly from input boundary conditions.

Student Requirements Stress analysis & linear elasticity. Intuition for 2D plane-

stress/plane-strain problems. Preferably some experience implementing the Finite Element Method Familiarity with Deep Learning techniques (methods of regularization, experience training deep neural networks CNNs for image segmentation).

Familiarity with Deep Learning techniques (methods of regularization, experience training deep neural networks)

Good python skills. Experience with BASH scripting and setting up hyper-parameter runs for deep learning problems will be required to set up parallel computations on Euler for the 1st part of the project.

Deliverables:he deliverables are going to be stored in a git repository and will include: (1) the code for creating the dataset (aparametrized python code for running shape optimization problems), (2) the training/validation dataset, (3) jupyternotebooks runnable in google colab demonstrating the efficacy of the method (4) a brief report (20 to 30 pages)

Excited about the project? Write us at:Mylonas Charilaos ([email protected]. Dr. Eleni Chatzi ([email protected])



||Chair of Structural Mechanics and Monitoring 28.1 .20191

Simulating the fluid-structure interaction of an airfoil

Main Goals: Literature review on Fluid-Structure Interaction models of 2D airfoil sections. Development of the Fluid-Structure Interaction model in Comsol. Perform uncertainty propagation and quantification to analyse the effect of various input variables on the strucural

deformations due to wind loading.

Supervision:Prof. Dr Eleni Chatzi ([email protected]) Dr. Imad Abdallah ([email protected])Gregory Duethé ([email protected])

https://doi.org/10.1016/j.jfluidstructs.2015.10.010

COMSOL is an interactive software for modelling and simulating scientific and engineering problems, with an easy and intuitive user interface. The student will develop a COMSOL Fluid-Structure Interaction model to simulate the effects of wind loading on a 2D airfoil section structural deformation. The student will conduct simulations experiments to analyse the effects of various input variables on the 2D airfoil structural deformations.


https://doi.org/10.1016/j.jfluidstructs.2015.10.010

||Chair of Structural Mechanics and Monitoring 28.1 .20191

System identification and modal analysis of a wind turbine

Main Goals: Literature review on system identification and modal analysis on wind turbines. Implementation, evaluation and comparison of several system identification and operational modal analysis algorithms in

Matlab or Python.

Supervision:Prof. Dr Eleni Chatzi ([email protected]) Dr. Imad Abdallah ([email protected])Gregory Duethé ([email protected])

Vibration measurements from a wind turbine are to be used in the task of system identification and operational modal analysis. The wind turbine is located in Winterthur and the vibration measurements are based on accelerometers located along the concrete tower and the nacelle of the wind turbine. The approach is to fit a model to the vibrations data and extract the eigenfrequencies, damping ratios, mode shapes and, if possible, modal scaling factors. The model is estimated from output-only data. The results will be used for the calibration of a numerical model of a wind turbine.


finite element simulation and model identification of an

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