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The initial shear modulus, Gmax, of soil is an important parameter for a variety of geotechnical design applications. This modulus is typically associated with shear strain levels about 5*10^-3% and below. The critical role of soil stiffness at small-strains in the design and analysis of geotechnical infrastructure is now widely accepted.
Gmax is a key parameter in small-strain dynamic analyses such as those to predict soil behavior or soil-structure interaction during earthquake, explosions, machine or traffic vibration where it is necessary to know how the shear modulus degrades from its small-strain value as the level of shear strain increases. Gmax can be equally important for small-strain cyclic situations such as those caused by wind or wave loading and for small-strain static situations as well. Gmax may also be used as an indirect indication of various soil parameters, as it, in many cases, correlates well to other soil properties such as density and sample disturbance. In recent years, a technique using bender elements was developed to investigate the small-strain shear modulus Gmax.
The objective of this thesis is to study the initial shear stiffness for various sands with different void ratios, densities, grain size distribution under dry and saturated conditions, then to compare empirical equations to predict Gmax and results from other testing devices with results of bender elements from this study.

Experimentelle Untersuchung eines Verfahrens zur optimalen Positionierung von Referenzsensoren bei der experimentellen Modalanalyse mit output-only Methoden nach Brehm (2011). Untersuchung des Einflusses der Referenzsensorpositionierung, -anzahl und der Positionierung der wandernden Sensoren unter Anwendung des Stochastic-Subspace-Verfahrens zur Auswertung der output-only Messdaten.

The planning process in civil engineering is highly complex and not manageable in its entirety.
The state of the art decomposes complex tasks into smaller, manageable sub-tasks. Due to the close interrelatedness of the sub-tasks, it is essential to couple them. However, from a software engineering point of view, this is quite challenging to do because of the numerous incompatible software applications on the market. This study is concerned with two main objectives: The first is the generic formulation of coupling strategies in order to support engineers in the implementation and selection of adequate coupling strategies. This has been achieved by the use of a coupling pattern language combined with a four-layered, metamodel architecture, whose applicability has been performed on a real coupling scenario. The second one is the quality assessment of coupled software. This has been developed based on the evaluated schema mapping. This approach has been described using mathematical expressions derived from the set theory and graph theory by taking the various mapping patterns into account. Moreover, the coupling quality has been evaluated within the formalization process by considering the uncertainties that arise during mapping and has resulted in global quality values, which can be used by the user to assess the exchange. Finally, the applicability of the proposed approach has been shown using an engineering case study.

Nanostructured materials are extensively applied in many fields of material science for new industrial applications, particularly in the automotive, aerospace industry due to their exceptional physical and mechanical properties. Experimental testing of nanomaterials is expensive, timeconsuming,challenging and sometimes unfeasible. Therefore,computational simulations have been employed as alternative method to predict macroscopic material properties. The behavior of polymeric nanocomposites (PNCs) are highly complex.
The origins of macroscopic material properties reside in the properties and interactions taking place on finer scales. It is therefore essential to use multiscale modeling strategy to properly account for all large length and time scales associated with these material systems, which across many orders of magnitude. Numerous multiscale models of PNCs have been established, however, most of them connect only two scales. There are a few multiscale models for PNCs bridging four length scales (nano-, micro-, meso- and macro-scales). In addition, nanomaterials are stochastic in nature and the prediction of macroscopic mechanical properties are influenced by many factors such as fine-scale features. The predicted mechanical properties obtained by traditional approaches significantly deviate from the measured values in experiments due to neglecting uncertainty of material features. This discrepancy is indicated that the effective macroscopic properties of materials are highly sensitive to various sources of uncertainty, such as loading and boundary conditions and material characteristics, etc., while very few stochastic multiscale models for PNCs have been developed. Therefore, it is essential to construct PNC models within the framework of stochastic modeling and quantify the stochastic effect of the input parameters on the macroscopic mechanical properties of those materials.
This study aims to develop computational models at four length scales (nano-, micro-, meso- and macro-scales) and hierarchical upscaling approaches bridging length scales from nano- to macro-scales. A framework for uncertainty quantification (UQ) applied to predict the mechanical properties
of the PNCs in dependence of material features at different scales is studied. Sensitivity and uncertainty analysis are of great helps in quantifying the effect of input parameters, considering both main and interaction effects, on the mechanical properties of the PNCs. To achieve this major
goal, the following tasks are carried out:
At nano-scale, molecular dynamics (MD) were used to investigate deformation mechanism of glassy amorphous polyethylene (PE) in dependence of temperature and strain rate. Steered molecular dynamics (SMD)were also employed to investigate interfacial characteristic of the PNCs.
At mico-scale, we developed an atomistic-based continuum model represented by a representative volume element (RVE) in which the SWNT’s properties and the SWNT/polymer interphase are modeled at nano-scale, the surrounding polymer matrix is modeled by solid elements. Then, a two-parameter model was employed at meso-scale. A hierarchical multiscale approach has been developed to obtain the structure-property relations at one length scale and transfer the effect to the higher length
scales. In particular, we homogenized the RVE into an equivalent fiber.
The equivalent fiber was then employed in a micromechanical analysis (i.e. Mori-Tanaka model) to predict the effective macroscopic properties of the PNC. Furthermore, an averaging homogenization process was also used to obtain the effective stiffness of the PCN at meso-scale.
Stochastic modeling and uncertainty quantification consist of the following ingredients:
- Simple random sampling, Latin hypercube sampling, Sobol’ quasirandom sequences, Iman and Conover’s method (inducing correlation in Latin hypercube sampling) are employed to generate independent and dependent sample data, respectively.
- Surrogate models, such as polynomial regression, moving least squares (MLS), hybrid method combining polynomial regression and MLS, Kriging regression, and penalized spline regression, are employed as an approximation of a mechanical model. The advantage of the surrogate models is the high computational efficiency and robust as they can be constructed from a limited amount of available data.
- Global sensitivity analysis (SA) methods, such as variance-based methods for models with independent and dependent input parameters, Fourier-based techniques for performing variance-based methods and partial derivatives, elementary effects in the context of local SA, are used to quantify the effects of input parameters and their interactions on the mechanical properties of the PNCs. A bootstrap technique is used to assess the robustness of the global SA methods with respect to their performance.
In addition, the probability distribution of mechanical properties are determined by using the probability plot method. The upper and lower bounds of the predicted Young’s modulus according to 95 % prediction intervals were provided.
The above-mentioned methods study on the behaviour of intact materials. Novel numerical methods such as a node-based smoothed extended finite element method (NS-XFEM) and an edge-based smoothed phantom node method (ES-Phantom node) were developed for fracture problems. These methods can be used to account for crack at macro-scale for future works. The predicted mechanical properties were validated and verified. They show good agreement with previous experimental and simulations results.

The focus of the thesis is to process measurements acquired from a continuous
monitoring system at a railway bridge. Temperature, strain and ambient vibration
records are analysed and two main directions of investigation are pursued.
The first and the most demanding task is to develop processing routines able to extract modal parameters from ambient vibration measurements. For this purpose, reliable experimental models are achieved on the basis of a stochastic system identification(SSI) procedure. A fully automated algorithm based on a three-stage clustering is implemented to perform a modal parameter estimation for every single measurement. After selecting a baseline of modal parameters, the evolution of eigenfrequencies is
studied and correlated to environmental and operational factors.
The second aspect deals with the structural response to passing trains. Corresponding
triggered records of strain and temperature are processed and their assessment is
accomplished using the average strains induced by each train as the reference parameter.
Three influences due to speed, temperature and loads are distinguished and treated individually. An attempt to estimate the maximum response variation due to each factor is also carried out.

Environmental and operational variables and their impact on structural responses have been acknowledged as one of the most important challenges for the application of the ambient vibration-based damage identification in structures. The damage detection procedures may yield poor results, if the impacts of loading and environmental conditions of the structures are not considered.
The reference-surface-based method, which is proposed in this thesis, is addressed to overcome this problem. In the proposed method, meta-models are used to take into account significant effects of the environmental and operational variables. The usage of the approximation models, allows the proposed method to simply handle multiple non-damaged variable effects simultaneously, which for other methods seems to be very complex. The input of the meta-model are the multiple non-damaged variables while the output is a damage indicator.
The reference-surface-based method diminishes the effect of the non-damaged variables to the vibration based damage detection results. Hence, the structure condition that is assessed by using ambient vibration data at any time would be more reliable. Immediate reliable information regarding the structure condition is required to quickly respond to the event, by means to take necessary actions concerning the future use or further investigation of the structures, for instance shortly after extreme events such as earthquakes.
The critical part of the proposed damage detection method is the learning phase, where the meta-models are trained by using input-output relation of observation data. Significant problems that may encounter during the learning phase are outlined and some remedies to overcome the problems are suggested.
The proposed damage identification method is applied to numerical and experimental models. In addition to the natural frequencies, wavelet energy and stochastic subspace damage indicators are used.

Increasing structural robustness is the goal which is of interest for structural engineering community. The partial collapse of RC buildings is subject of this dissertation. Understanding the robustness of RC buildings will guide the development of safer structures against abnormal loading scenarios such as; explosions, earthquakes, fine, and/or long-term accumulation effects leading to deterioration or fatigue. Any of these may result in local immediate structural damage, that can propagate to the rest of the structure causing what is known by the disproportionate collapse.
This work handels collapse propagation through various analytical approaches which simplifies the mechanical description of damaged reinfoced concrete structures due to extreme acidental event.

Polymer-modified cement concrete (PCC) is a heterogeneous building material with a hierarchically organized microstructure. Therefore, continuum micromechanics-based multiscale models represent a promising method to estimate the mechanical properties. By means of a bottom-up approach, homogenized properties at the macroscopic scale are derived considering microstructural characteristics. The extension of existing multiscale models for the application to PCC is the main objective of this work. For that, cross-scale experimental studies are required. Both macroscopic and microscopic mechanical tests are performed to characterize the elastic and viscoelastic properties of different PCC. The comparison between experiment and model prediction illustrates the success of the modeling approach.

Advances in nanotechnology lead to the development of nano-electro-mechanical systems (NEMS) such as nanomechanical resonators with ultra-high resonant frequencies. The ultra-high-frequency resonators have recently received significant attention for wide-ranging applications such as molecular separation, molecular transportation, ultra-high sensitive sensing, high-frequency signal processing, and biological imaging. It is well known that for micrometer length scale, first-principles technique, the most accurate approach, poses serious limitations for comparisons with experimental studies. For such larger size, classical molecular dynamics (MD) simulations are desirable, which require interatomic potentials. Additionally, a mesoscale method such as the coarse-grained (CG) method is another useful method to support simulations for even larger system sizes.
Furthermore, quasi-two-dimensional (Q2D) materials have attracted intensive research interest due to their many novel properties over the past decades. However, the energy dissipation mechanisms of nanomechanical resonators based on several Q2D materials are still unknown. In this work, the addressed main issues include the development of the CG models for molybdenum disulphide (MoS2), investigation of the mechanism effects on black phosphorus (BP) nanoresonators and the application of graphene nanoresonators. The primary coverage and results of the dissertation are as follows:
Method development. Firstly, a two-dimensional (2D) CG model for single layer MoS2 (SLMoS2) is analytically developed. The Stillinger-Weber (SW) potential for this 2D CG model is further parametrized, in which all SW geometrical parameters are determined analytically according to the equilibrium condition for each individual potential term, while the SW energy parameters are derived analytically based on the valence force field model. Next, the 2D CG model is further simplified to one-dimensional (1D) CG model, which describes the 2D SLMoS2 structure using a 1D chain model. This 1D CG model is applied to investigate the relaxed configuration and the resonant oscillation of the folded SLMoS2. Owning to the simplicity nature of the 1D CG model, the relaxed configuration of the folded SLMoS2 is determined analytically, and the resonant oscillation frequency is derived analytically. Considering the increasing interest in studying the properties of other 2D layered materials, and in particular those in the semiconducting transition metal dichalcogenide class like MoS2, the CG models proposed in current work provide valuable simulation approaches.
Mechanism understanding. Two energy dissipation mechanisms of BP nanoresonators are focused exclusively, i.e. mechanical strain effects and defect effects (including vacancy and oxidation). Vacancy defect is intrinsic damping factor for the quality (Q)-factor, while mechanical strain and oxidation are extrinsic damping factors. Intrinsic dissipation (induced by thermal vibrations) in BP resonators (BPRs) is firstly investigated. Specifically, classical MD simulations are performed to examine the temperature dependence for the Q-factor of the single layer BPR (SLBPR) along the armchair and zigzag directions, where two-step fitting procedure is used to extract the frequency and Q-factor from the kinetic energy time history. The Q-factors of BPRs are evaluated through comparison with those of graphene and MoS2 nanoresonators. Next, effects of mechanical strain, vacancy and oxidation on BP nanoresonators are investigated in turn. Considering the increasing interest in studying the properties of BP, and in particular the lack of theoretical study for the BPRs, the results in current work provide a useful reference.
Application. A novel application for graphene nanoresonators, using them to self-assemble small nanostructures such as water chains, is proposed. All of the underlying physics enabling this phenomenon is elucidated. In particular, by drawing inspiration from macroscale self-assembly using the higher order resonant modes of Chladni plates, classical MD simulations are used to investigate the self-assembly of water molecules using
graphene nanoresonators. An analytic formula for the critical resonant frequency based on the interaction between water molecules and graphene is provided. Furthermore, the properties of the water chains assembled by the graphene nanoresonators are studied.

This dissertation is devoted to the theoretical development and experimental laboratory verification of a new damage localization method: The state projection estimation error (SP2E). This method is based on the subspace identification of mechanical structures, Krein space based H-infinity estimation and oblique projections. To explain method SP2E, several theories are discussed and laboratory experiments have been conducted and analysed.
A fundamental approach of structural dynamics is outlined first by explaining mechanical systems based on first principles. Following that, a fundamentally different approach, subspace identification, is comprehensively explained. While both theories, first principle and subspace identification based mechanical systems, may be seen as widespread methods, barely known and new techniques follow up. Therefore, the indefinite quadratic estimation theory is explained. Based on a Popov function approach, this leads to the Krein space based H-infinity theory. Subsequently, a new method for damage identification, namely SP2E, is proposed. Here, the introduction of a difference process, the analysis by its average process power and the application of oblique projections is discussed in depth.
Finally, the new method is verified in laboratory experiments. Therefore, the identification of a laboratory structure at Leipzig University of Applied Sciences is elaborated. Then structural alterations are experimentally applied, which were localized by SP2E afterwards. In the end four experimental sensitivity studies are shown and discussed. For each measurement series the structural alteration was increased, which was successfully tracked by SP2E. The experimental results are plausible and in accordance with the developed theories. By repeating these experiments, the applicability of SP2E for damage localization is experimentally proven.