Design philosophy issues of fiber reinforced polymer reinforced concrete structures

The conventional design philosophy for reinforced concrete (RC) relies heavily on the ductile properties of steel. These ductile properties are used as a "fuse" and conceal the large uncertainty in the determination of modes of failure caused directly by concrete. Current design guidelines for fiber reinforced polymer (FRP) RC structures have inappropriately adopted the same design philosophy used for steel RC, leading either to the adoption of conservative safety factors or reduced structural reliability. A reliability-based analysis of FRP RC beams shows that the current, very conservative partial safety factors for FRP reinforcement on their own do not influence the structural safety of overreinforced concrete elements. Proposals are made for the modification of the material partial safety factors to achieve target safety levels

Failure-mode-hierarchy-based design for reinforced concrete structures

Innovations in concrete construction can be held back by the inability of codes of practice to accommodate new materials. The current design and safety philosophy (DSP) of reinforced concrete relies heavily on the properties of steel reinforcement. The need to embrace new materials, such as fibre-reinforced polymer (FRP) reinforcement, led to an in-depth examination of the DSP of European concrete codes of practice and resulted in a new philosophy, presented in this paper. The basis of the new philosophy remains the limit-state design and achievement of target notional structural reliability levels, but aims at the attainment of a desired failure mode hierarchy. The implementation of the philosophy, through a proposed framework, utilises the concept of average measure of closeness for the determination of appropriate material partial safety factors. An example of the application of the proposed framework is presented for FRP reinforcement. © 2005 Thomas Telford and fib

Interpolating gauge fixing for Chern-Simons theory

Chern-Simons theory is analyzed with a gauge-fixing which allows to discuss the Landau gauge and the light-cone gauge at the same time.Comment: 11 pages, Report TUW-93-2

Probabilistic reconstruction via machine-learning of the Po watershed aquifer system (Italy)

A machine-learning-based methodology is proposed to delineate the spatial distribution of geomaterials across a large-scale three-dimensional subsurface system. The study area spans the entire Po River Basin in northern Italy. As uncertainty quantification is critical for subsurface characterization, the methodology is specifically designed to provide a quantitative evaluation of prediction uncertainty at each location of the reconstructed domain. The analysis is grounded on a unique dataset that encompasses lithostratigraphic data obtained from diverse sources of information. A hyperparameter selection technique based on a stratified cross-validation procedure is employed to improve model prediction performance. The quality of the results is assessed through validation against pointwise information and available hydrogeological cross-sections. The large-scale patterns identified are in line with the main features highlighted by typical hydrogeological surveys. Reconstruction of prediction uncertainty is consistent with the spatial distribution of available data and model accuracy estimates. It enables one to identify regions where availability of new information could assist in the constraining of uncertainty. The comprehensive dataset provided in this study, complemented by the model-based reconstruction of the subsurface system and the assessment of the associated uncertainty, is relevant from a water resources management and protection perspective. As such, it can be readily employed in the context of groundwater availability and quality studies aimed at identifying the main dynamics and patterns associated with the action of climate drivers in large-scale aquifer systems of the kind here analyzed, while fully embedding model and parametric uncertainties that are tied to the scale of investigation

Two-Loop Finiteness of Chern-Simons Field Theory in Background Field Method

We perform two-loop calculation of Chern-Simons in background field method using the hybrid regularization of higher-covariant derivative and dimensional regularization. It is explicitly shown that Chern-Simons field theory is finite at the two-loop level. This finiteness plays an important role in the relation of Chern-Simons theory with two-dimensional conformal field theory and the description of link invariant.Comment: RevTex, 13 pages. The proof of the existence of the large topological mass limit has been proved. Some typewritten mistakes have been correcte

Analysis of Observables in Chern-Simons Perturbation Theory

Chern-Simons Theory with gauge group $SU(N)$ is analyzed from a perturbation theory point of view. The vacuum expectation value of the unknot is computed up to order $g^6$ and it is shown that agreement with the exact result by Witten implies no quantum correction at two loops for the two-point function. In addition, it is shown from a perturbation theory point of view that the framing dependence of the vacuum expectation value of an arbitrary knot factorizes in the form predicted by Witten.Comment: 42page

Strut-and-Tie Modeling of Reinforced Concrete Deep Beams

Strut-and-tie models (STMs) are often used for the design of shear critical deep members because they can rationalize the shear transfer within discontinuous or disturbed regions in reinforced concrete structural elements. Most current codes of practice adopt the strut-and-tie method but provide very little guidance on how to select appropriate strut-and-tie layout and dimensions. Furthermore, the effectiveness factors used to account for the biaxial state of stresses in struts of deep beams are not reliable. This paper reviews the application of STMs for the design of RC deep beams and evaluates current formulations of the effectiveness factor. Experimental and numerical studies are used to assess how the effectiveness factor is influenced by different parameters, including concrete compressive strength, shear span:depth ratio, and shear reinforcement ratio, and to arrive at a more reliable strain-based effectiveness factor. Various effectiveness factors are examined against an extensive database of experimental results on RC deep beams with and without shear reinforcement. The results show that the proposed effectiveness factor yields the most-reliable and most-accurate predictions and can lead to more-economical and safer design guidelines

SciKit-GStat Uncertainty: A software extension to cope with uncertain geostatistical estimates

This study is focused on an extension of a well established geostatistical software to enable one to effectively and interactively cope with uncertainty in geostatistical applications. The extension includes a rich component library, pre-built interfaces and an online application. We discuss the concept of replacing the empirical variogram with its uncertainty bound. This enables one to acknowledge uncertainties characterizing the underlying geostatistical datasets and typical methodological approaches. This allows for a probabilistic description of the variogram and its parameters at the same time. Our approach enables (1) multiple interpretations of a sample and (2) a multi-model context for geostatistical applications. We focus the sample application on propagating observation uncertainties into manual variogram parametrization and analyze its effects. Using two different datasets, we show how insights on uncertainty can be used to reject variogram models, thus constraining the space of formally equally probable models to tackle the issue of parameter equifinality

Gauss Law Constraints in Chern-Simons Theory From BRST Quantization

The physical state condition in the BRST quantization of Chern-Simons field theory is used to derive Gauss law constraints in the presence of Wilson loops, which play an important role in explicitly establishing the connection of Chern-Simons field theory with 2-dimensional conformal field theory.Comment: Some typetwritten errors have been corrected. A few formulas have been modified to make the arguments clear. A little English have been re-edite

A Gaussian-Mixture based stochastic framework for the interpretation of spatial heterogeneity in multimodal fields

We provide theoretical formulations enabling characterization of spatial distributions of variables (such as, e.g., conductivity/permeability, porosity, vadose zone hydraulic parameters, and reaction rates) that are typical of hydrogeological and/or geochemical scenarios associated with randomly heterogeneous geomaterials and are organized on various scales of heterogeneity. Our approach and ensuing formulations embed the joint assessment of the probability distribution of a target variable and its associated spatial increments, DY, taken between locations separated by any given distance (or lag). The spatial distribution of Y is interpreted through a bimodal Gaussian mixture model. The modes of the latter correspond to an indicator random field which is in turn related to the occurrence of different processes and/or geomaterials within the domain of observation. The distribution of each component of the mixture is governed by a given length scale driving the strength of its spatial correlation. Our model embeds within a unique theoretical framework the main traits arising in a stochastic analysis of these systems. These include (i) a slight to moderate asymmetry in the distribution of Y and (ii) the occurrence of a dominant peak and secondary peaks in the distribution of DY whose importance changes with lag together with the moments of the distribution. This causes the probability distribution of increments to scale with lag in way that is consistent with observed experimental patterns. We analyze the main features of the modeling and parameter estimation framework through a set of synthetic scenarios. We then consider two experimental datasets associated with different processes and observation scales. We start with an original dataset comprising microscale reaction rate maps taken at various observation times. These are evaluated from AFM imaging of the surface of a calcite crystal in contact with a fluid and subject to dissolution. Such recent high resolution imaging techniques are key to enhance our knowledge of the processes driving the reaction. The second dataset is a well established collection of Darcy-scale air-permeability data acquired by Tidwell and Wilson (1999) [Water Resour Res, 35, 3375-3387] on a block of volcanic tuff through minipermeameters associated with various measurement scales