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Machine learning modelling to design micro-structured concrete absorber of carbon dioxide (CO2)Led by: Prof. Fadi AldakheelYear: 2024
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Multiscale Modeling of Porous Materials Enhanced by Deep Neural NetworksLed by: Yousef HeiderTeam:Year: 2024
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Artificial intelligence in biomechanics and biomedical applicationsLed by: Prof. Dr.-Ing. Fadi AldakheelTeam:Year: 2023
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Physics-augmented machine learning for computational fracture mechanicsLed by: Prof. Dr.-Ing. Fadi AldakheelTeam:Year: 2023
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Machine learning aided design of architectural materialsLed by: Prof. Fadi AldakheelYear: 2023
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Application of stochastic and machine learning approaches for efficient analysis of dynamical systemsDynamical systems find a wide range of applications for problems involving response analysis, reliability assessment, and system control of engineering structures, biomechanical structures, and biological models, among others. Especially in civil engineering, structural responses of e.g. buildings, bridges, and offshore structures under time-dependent excitation are determined by dynamic simulations and subsequently the outcomes serve as basis for further performance analyses. Analysis of the system behavior and reliability assessment of dynamically excited systems often requires a considerable number of computationally expensive time-dependent simulations, which is a major challenge, especially for realistic complex models.Team:Year: 2022Funding: IRTG 2657, DFG
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Hybrid physics-based and data-driven dynamical systems identification using kernel-based methodsThis project focuses on exploring the different alternatives to assemble so-called hybrid physics-based and data-driven dynamical models and exploring their performance capabilities in engineering tasks such as reliability analysis or closed-loop control. The idea is to combine an optimal linear representation of the system under study-- e.g., optimal in the least square sense-- and extend it to adopt so-called kernel models that can "learn" the system's unmodeled (nonlinear) dynamics. The resulting model is a nonlinear one composed of linear and nonlinear parts. The linear part can be constructed based on some known physics of the real system, which makes it interpretable. The nonlinear part can be identified based on, e.g., measured data computing the error between the linear approximation and the real system. Some well-known kernel models, widely used in Machine Learning applications, could be adopted for its construction, e.g., exponential, square exponential, Matern with parameter 3/2 or 5/2 kernels.Led by: Udo NackenhorstTeam:Year: 2022
![Scheme: Hybrid physics-based and data-driven synamical systems identification]()
![Scheme: Hybrid physics-based and data-driven synamical systems identification]()
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Stochastic two-scale approach on fatigue simulation of concrete specimenLed by: Udo NackenhorstTeam:Year: 2021
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Fast parametric investigations for bone-implant surgery planningLed by: Udo NackenhorstTeam:Year: 2021
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Efficient stochastic finite element method for high-dimensional nonlinear stochastic problemsLed by: Udo NackenhorstTeam:Year: 2021
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Surrogate modelling for the monitoring of implantsHigh-fidelity computational simulations can be used to predict the long-term stability and possible failure of implants. Furthermore, the patient’s individual conditions can be considered to optimise the monitoring of the implantation. However, these models require a high computational effort due to...Led by: Udo NackenhorstTeam:Year: 2021Funding: DFG-funded collaborative research centre/transregio 298 “Safety-Integrated and Infection-Reactive Implants” (SIIRI)
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Stochastic calculations in connection with FE-simulationsThis projects investigates non-linear finite element (FE) calculations involving random variables and random fields. For this purpose, elasto-plastic calculations and damage calculations are performed using the FE software Abaqus. In order to model the dependence of damage evolution on inhomogeneities in the material more realistically, random fields are used to model material properties. Thus, the material properties vary not only from realisation to realisation, but also in space within the model.Led by: Udo NackenhorstTeam:Year: 2021
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Development of a Coupled BCHM-Model for Numerical investigations of MICP treatment of soilMicrobially induced calcite precipitation (MICP) offers the potential for the development of environmentally friendly and cost-effective solutions to a wide range of geotechnical engineering problems, from “improvement of the soft underground” to “control of groundwater contamination”.Led by: Udo NackenhorstTeam:Year: 2020Funding: German Research Foundation (DFG)Duration: 2020-2022
![Figures: Schematic view of the relevant processes in MICP (left) and the BCHM couplings (right)]()
![Figures: Schematic view of the relevant processes in MICP (left) and the BCHM couplings (right)]()
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Anisotropic damage modelling of concrete at the mesoscaleWithin the scope of this project, the mechanism of concrete damage under cyclic loading conditions will be invistigated at the meso-scale. At this scale, concrete will be considered as non-homogeneous three-phase composite material which consists of cement matrix (mortar), aggregates and interfacial transition zone (ITZ).Led by: Udo NackenhorstTeam:Year: 2018Funding: DAAD (German Academic Exchange Service)
![a) Evolution of damage hardening threshold (B). B) Damage hardening variable (beta)]()
![a) Evolution of damage hardening threshold (B). B) Damage hardening variable (beta)]()
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Imprecise random fields within non-linear finite element analysisRegarding climate change, nowadays research focus lays more and more on sustainability and resource saving approaches. Quantifying and considering uncertainties within the engineering design process can help to reduce both, ecological and economical costs. Instead of conservative safety and knockdown factors, a stochastic finite element (FE) analysis enables an optimized design. For this purpose, input variables such as material or load properties can be considered uncertain.Led by: Udo Nackenhorst (former also Amélie Fau)Team:Year: 2016Funding: Priority Programme SPP 1886 of German Research Foundation (DFG), State of Lower Saxony
![Imprecise random fields within non-linear finite element analysis]()
![Imprecise random fields within non-linear finite element analysis]()
Current Research Projects at the IBNM
Last Change: 19.11.24; Webredaktion IBNM
