Rheology of natural hydraulic lime grouts for masonry consolidation

Tese para obtenção do Grau de Doutor em Engenharia Civil, Especialidade Ciências da Construção

Natural hydraulic lime (nhl3.5) mortars with scrap tire rubber

The use of wastes and industrial by-products as building materials is an important issue in order to decrease costs with waste management and the embodied energy of building products. In this study scrap tire rubber was used as additional aggregate of mortars based on natural hydraulic lime NHL 3.5 and natural sand. Different particle size fractions and proportions of scrap tire rubber were used: a mix obtained directly from industry and separated fine, medium and coarse fractions; 0 %, 18 %, 36 % and 54 % of the weight of binder, corresponding to 2.5 %, 5 % and 7.5 % of the weight of sand. As mortars based on NHL specifications became stricter with the current version of EN 459–1:2015, the influence of the rubber’s additions on the mortars’ fresh state, mechanical and physical performance is presented in this work: flow table consistency, water retention, dynamic elasticity modulus, flexural and compressive strength, open porosity and bulk density, capillary absorption, drying and thermal conductivity are studied. The use of the rubber mix coming from the waste tire industry seems advantageous and may open possibilities for use as raw material by the mortars industry.

Dynamic modelling and analysis of hydraulic forces in radial blood pumps

Dissertação de mestrado integrado em Biomedical Engineering Biomaterials, Biomechanics and Rehabilitation

Personal redistribution and the regional allocation of public investment

How should an equity-motivated policy-marker allocate public capital (infrastructure) across regions. Should it aim at reducing interregional differences in per capita output, or at maximizing total output? Such a normative question is examined in a model where the policy-marker is exclusively concerned about personal inequality and has access to two policy instruments. (i) a personal tax-transfer system (taxation is distortionary), and (ii) the regional allocation of public investment. I show that the case for public investment as a significant instrument for interpersonal redistribution is rather weak. In the most favorable case, when the tax code is constrained to be uniform across regions, it is optimal to distort the allocation of public investment in favor of the poor regions, but only to a limited extent. The reason is that poor individuals are relatively more sensitive to public trans fers, which are maximized by allocating public investment efficiently. If! the tax code can vary across regions then the optimal policy may involve an allocation of public investment distorted in favor of the rich regions.

Redistribution and subsidies for higher education

The financing of higher education through public spending imposes a transfer of resources from taxpayers to university students and their parents. We provide an explanation for this phenomenon. Those who attend higher education will earn more income in the future and will pay more taxes. People whose children do not attend higher education, however should agree to help pay the cost of such education, providing that the taxes are sufficiently high to ensure that there will be an adequate redistribution in favor of their own children at some time in the future.

Comparison of time-lapse GPR data collected under natural and forced infiltration conditions to estimate vadose zone hydraulic parameters

Time-lapse crosshole ground-penetrating radar (GPR) data, collected while infiltration occurs, can provide valuable information regarding the hydraulic properties of the unsaturated zone. In particular, the stochastic inversion of such data provides estimates of parameter uncertainties, which are necessary for hydrological prediction and decision making. Here, we investigate the effect of different infiltration conditions on the stochastic inversion of time-lapse, zero-offset-profile, GPR data. Inversions are performed using a Bayesian Markov-chain-Monte-Carlo methodology. Our results clearly indicate that considering data collected during a forced infiltration test helps to better refine soil hydraulic properties compared to data collected under natural infiltration conditions

Inter-regional redistribution through infrastructure investment: tactical or programmatic?

In this paper we analyze the effects of both tactical and programmatic politics on the inter-regional allocation of infrastructure investment. We use a panel of data for the Spanish electoral districts during the period 1964-2004 to estimate an equation where investment depends both on economic and political variables. The results show that tactical politics do matter since, after controlling for economic traits, the districts with more ‘Political power’ still receive more investment. These districts are those where the incumbents’ Vote margin of victory/ defeat in the past election is low, where the Marginal seat price is low, where there is Partisan alignment between the executives at the central and regional layers of government, and where there are Pivotal regional parties which are influential in the formation of the central executive. However, the results also show that programmatic politics matter, since inter-regional redistribution (measured as the elasticity of investment to per capita income) is shown to increase with the arrival of the Democracy and EU Funds, with Left governments, and to decrease the higher is the correlation between a measure of ‘Political power’ and per capita income.

Work values, endogenous sentiments and redistribution

We examine the interactions between individual behavior, sentiments and the social contract in a model of rational voting over redistribution. Agents have moral "work values". Individuals' self-esteem and social consideration of others are endogenously determined comparing behaviors to moral standards. Attitudes toward redistribution depend on self-interest and social preferences. We characterize the politico-economic equilibria in which sentiments, labor supply and redistribution are determined simultaneously. The equilibria feature different degrees of "social cohesion" and redistribution depending on pre-tax income inequality. In clustered equilibria the poor are held partly responsible for their low income since they work less than the moral standard and hence redistribution is low. The paper proposes a novel explanation for the emergence of different sentiments and social contracts across countries. The predictions appear broadly in line with well-documented differences between the United States and Europe.

Is it redistribution or centralization? On the determinants of government investment in infrastructure

The dilemma efficiency versus equity, together with political partisan interests, has received increasing attention to explain the territorial allocation of investments. However, centralization intended to introduce or reinforce hierarchization in the political system has not been object as of now of empirical analysis. Our main contribution to the literature is providing evidence that meta-political objectives related to the ordering of political power and administration influence regional investment. In this way, we find evidence that network mode’s (roads and railways) investment programs are influenced by the centralization strategy of investing near to the political capital, while investment effort in no-network modes (airports and ports) appears to be positively related to distance. Since investment in surface transportation infrastructures is much higher than that in airports and ports, and taken into account that regions surrounding the political capital are poorer than the average, we suggest that centralization rather than redistribution has been the driver for the concentration of public investment on these regions.

Quantitative integration of geophysical and hydraulic data : from the local towards the regional scale range

Des progrès significatifs ont été réalisés dans le domaine de l'intégration quantitative des données géophysique et hydrologique l'échelle locale. Cependant, l'extension à de plus grandes échelles des approches correspondantes constitue encore un défi majeur. Il est néanmoins extrêmement important de relever ce défi pour développer des modèles fiables de flux des eaux souterraines et de transport de contaminant. Pour résoudre ce problème, j'ai développé une technique d'intégration des données hydrogéophysiques basée sur une procédure bayésienne de simulation séquentielle en deux étapes. Cette procédure vise des problèmes à plus grande échelle. L'objectif est de simuler la distribution d'un paramètre hydraulique cible à partir, d'une part, de mesures d'un paramètre géophysique pertinent qui couvrent l'espace de manière exhaustive, mais avec une faible résolution (spatiale) et, d'autre part, de mesures locales de très haute résolution des mêmes paramètres géophysique et hydraulique. Pour cela, mon algorithme lie dans un premier temps les données géophysiques de faible et de haute résolution à travers une procédure de réduction déchelle. Les données géophysiques régionales réduites sont ensuite reliées au champ du paramètre hydraulique à haute résolution. J'illustre d'abord l'application de cette nouvelle approche dintégration des données à une base de données synthétiques réaliste. Celle-ci est constituée de mesures de conductivité hydraulique et électrique de haute résolution réalisées dans les mêmes forages ainsi que destimations des conductivités électriques obtenues à partir de mesures de tomographic de résistivité électrique (ERT) sur l'ensemble de l'espace. Ces dernières mesures ont une faible résolution spatiale. La viabilité globale de cette méthode est testée en effectuant les simulations de flux et de transport au travers du modèle original du champ de conductivité hydraulique ainsi que du modèle simulé. Les simulations sont alors comparées. Les résultats obtenus indiquent que la procédure dintégration des données proposée permet d'obtenir des estimations de la conductivité en adéquation avec la structure à grande échelle ainsi que des predictions fiables des caractéristiques de transports sur des distances de moyenne à grande échelle. Les résultats correspondant au scénario de terrain indiquent que l'approche d'intégration des données nouvellement mise au point est capable d'appréhender correctement les hétérogénéitées à petite échelle aussi bien que les tendances à gande échelle du champ hydraulique prévalent. Les résultats montrent également une flexibilté remarquable et une robustesse de cette nouvelle approche dintégration des données. De ce fait, elle est susceptible d'être appliquée à un large éventail de données géophysiques et hydrologiques, à toutes les gammes déchelles. Dans la deuxième partie de ma thèse, j'évalue en détail la viabilité du réechantillonnage geostatique séquentiel comme mécanisme de proposition pour les méthodes Markov Chain Monte Carlo (MCMC) appliquées à des probmes inverses géophysiques et hydrologiques de grande dimension . L'objectif est de permettre une quantification plus précise et plus réaliste des incertitudes associées aux modèles obtenus. En considérant une série dexemples de tomographic radar puits à puits, j'étudie deux classes de stratégies de rééchantillonnage spatial en considérant leur habilité à générer efficacement et précisément des réalisations de la distribution postérieure bayésienne. Les résultats obtenus montrent que, malgré sa popularité, le réechantillonnage séquentiel est plutôt inefficace à générer des échantillons postérieurs indépendants pour des études de cas synthétiques réalistes, notamment pour le cas assez communs et importants où il existe de fortes corrélations spatiales entre le modèle et les paramètres. Pour résoudre ce problème, j'ai développé un nouvelle approche de perturbation basée sur une déformation progressive. Cette approche est flexible en ce qui concerne le nombre de paramètres du modèle et lintensité de la perturbation. Par rapport au rééchantillonage séquentiel, cette nouvelle approche s'avère être très efficace pour diminuer le nombre requis d'itérations pour générer des échantillons indépendants à partir de la distribution postérieure bayésienne. - Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale. However, extending corresponding approaches beyond the local scale still represents a major challenge, yet is critically important for the development of reliable groundwater flow and contaminant transport models. To address this issue, I have developed a hydrogeophysical data integration technique based on a two-step Bayesian sequential simulation procedure that is specifically targeted towards larger-scale problems. The objective is to simulate the distribution of a target hydraulic parameter based on spatially exhaustive, but poorly resolved, measurements of a pertinent geophysical parameter and locally highly resolved, but spatially sparse, measurements of the considered geophysical and hydraulic parameters. To this end, my algorithm links the low- and high-resolution geophysical data via a downscaling procedure before relating the downscaled regional-scale geophysical data to the high-resolution hydraulic parameter field. I first illustrate the application of this novel data integration approach to a realistic synthetic database consisting of collocated high-resolution borehole measurements of the hydraulic and electrical conductivities and spatially exhaustive, low-resolution electrical conductivity estimates obtained from electrical resistivity tomography (ERT). The overall viability of this method is tested and verified by performing and comparing flow and transport simulations through the original and simulated hydraulic conductivity fields. The corresponding results indicate that the proposed data integration procedure does indeed allow for obtaining faithful estimates of the larger-scale hydraulic conductivity structure and reliable predictions of the transport characteristics over medium- to regional-scale distances. The approach is then applied to a corresponding field scenario consisting of collocated high- resolution measurements of the electrical conductivity, as measured using a cone penetrometer testing (CPT) system, and the hydraulic conductivity, as estimated from electromagnetic flowmeter and slug test measurements, in combination with spatially exhaustive low-resolution electrical conductivity estimates obtained from surface-based electrical resistivity tomography (ERT). The corresponding results indicate that the newly developed data integration approach is indeed capable of adequately capturing both the small-scale heterogeneity as well as the larger-scale trend of the prevailing hydraulic conductivity field. The results also indicate that this novel data integration approach is remarkably flexible and robust and hence can be expected to be applicable to a wide range of geophysical and hydrological data at all scale ranges. In the second part of my thesis, I evaluate in detail the viability of sequential geostatistical resampling as a proposal mechanism for Markov Chain Monte Carlo (MCMC) methods applied to high-dimensional geophysical and hydrological inverse problems in order to allow for a more accurate and realistic quantification of the uncertainty associated with the thus inferred models. Focusing on a series of pertinent crosshole georadar tomographic examples, I investigated two classes of geostatistical resampling strategies with regard to their ability to efficiently and accurately generate independent realizations from the Bayesian posterior distribution. The corresponding results indicate that, despite its popularity, sequential resampling is rather inefficient at drawing independent posterior samples for realistic synthetic case studies, notably for the practically common and important scenario of pronounced spatial correlation between model parameters. To address this issue, I have developed a new gradual-deformation-based perturbation approach, which is flexible with regard to the number of model parameters as well as the perturbation strength. Compared to sequential resampling, this newly proposed approach was proven to be highly effective in decreasing the number of iterations required for drawing independent samples from the Bayesian posterior distribution.

In vitro heat exposure induces a redistribution of nuclear matrix proteins in human K562 erythroleukemia cells.

By using both conventional and confocal laser scanning microscopy with three monoclonal antibodies recognizing nuclear matrix proteins we have investigated by means of indirect fluorescence whether an incubation of isolated nuclei at the physiological temperature of 37 degrees C induces a redistribution of nuclear components in human K562 erythroleukemia cells. Upon incubation of isolated nuclei for 45 min at 37 degrees C, we have found that two of the antibodies, directed against proteins of the inner matrix network (M(r) 125 and 160 kDa), gave a fluorescent pattern different from that observed in permeabilized cells. By contrast, the fluorescent pattern did not change if nuclei were kept at 0 degrees C. The difference was more marked in case of the 160-kDa polypeptide. The fluorescent pattern detected by the third antibody, which recognizes the 180-kDa nucleolar isoform of DNA topoisomerase II, was unaffected by heat exposure of isolated nuclei. When isolated nuclear matrices prepared from heat-stabilized nuclei were stained by means of the same three antibodies, it was possible to see that the distribution of the 160-kDa matrix protein no longer corresponded to that observable in permeabilized cells, whereas the fluorescent pattern given by the antibody to the 125-kDa polypeptide resembled that detectable in permeabilized cells. The 180-kDa isoform of topoisomerase II was still present in the matrix nucleolar remnants. We conclude that a 37 degrees C incubation of isolated nuclei induces a redistribution of some nuclear matrix antigens and cannot prevent the rearrangement in the spatial organization of one of these antigens that takes place during matrix isolation in human erythroleukemia cells. The practical relevance of these findings is discussed.

Examining the information content of time-lapse crosshole GPR data collected under different infiltration conditions to estimate unsaturated soil hydraulic properties

Time-lapse geophysical data acquired during transient hydrological experiments are being increasingly employed to estimate subsurface hydraulic properties at the field scale. In particular, crosshole ground-penetrating radar (GPR) data, collected while water infiltrates into the subsurface either by natural or artificial means, have been demonstrated in a number of studies to contain valuable information concerning the hydraulic properties of the unsaturated zone. Previous work in this domain has considered a variety of infiltration conditions and different amounts of time-lapse GPR data in the estimation procedure. However, the particular benefits and drawbacks of these different strategies as well as the impact of a variety of key and common assumptions remain unclear. Using a Bayesian Markov-chain-Monte-Carlo stochastic inversion methodology, we examine in this paper the information content of time-lapse zero-offset-profile (ZOP) GPR traveltime data, collected under three different infiltration conditions, for the estimation of van Genuchten-Mualem (VGM) parameters in a layered subsurface medium. Specifically, we systematically analyze synthetic and field GPR data acquired under natural loading and two rates of forced infiltration, and we consider the value of incorporating different amounts of time-lapse measurements into the estimation procedure. Our results confirm that, for all infiltration scenarios considered, the ZOP GPR traveltime data contain important information about subsurface hydraulic properties as a function of depth, with forced infiltration offering the greatest potential for VGM parameter refinement because of the higher stressing of the hydrological system. Considering greater amounts of time-lapse data in the inversion procedure is also found to help refine VGM parameter estimates. Quite importantly, however, inconsistencies observed in the field results point to the strong possibility that posterior uncertainties are being influenced by model structural errors, which in turn underlines the fundamental importance of a systematic analysis of such errors in future related studies.

Bayesian Markov chain Monte Carlo inversion of geophysical data for the purpose of estimating the hydraulic properties of the unsaturated zone

Ground-penetrating radar (GPR) has the potential to provide valuable information on hydrological properties of the vadose zone because of their strong sensitivity to soil water content. In particular, recent evidence has suggested that the stochastic inversion of crosshole GPR data within a coupled geophysical-hydrological framework may allow for effective estimation of subsurface van-Genuchten-Mualem (VGM) parameters and their corresponding uncertainties. An important and still unresolved issue, however, is how to best integrate GPR data into a stochastic inversion in order to estimate the VGM parameters and their uncertainties, thus improving hydrological predictions. Recognizing the importance of this issue, the aim of the research presented in this thesis was to first introduce a fully Bayesian inversion called Markov-chain-Monte-carlo (MCMC) strategy to perform the stochastic inversion of steady-state GPR data to estimate the VGM parameters and their uncertainties. Within this study, the choice of the prior parameter probability distributions from which potential model configurations are drawn and tested against observed data was also investigated. Analysis of both synthetic and field data collected at the Eggborough (UK) site indicates that the geophysical data alone contain valuable information regarding the VGM parameters. However, significantly better results are obtained when these data are combined with a realistic, informative prior. A subsequent study explore in detail the dynamic infiltration case, specifically to what extent time-lapse ZOP GPR data, collected during a forced infiltration experiment at the Arrenaes field site (Denmark), can help to quantify VGM parameters and their uncertainties using the MCMC inversion strategy. The findings indicate that the stochastic inversion of time-lapse GPR data does indeed allow for a substantial refinement in the inferred posterior VGM parameter distributions. In turn, this significantly improves knowledge of the hydraulic properties, which are required to predict hydraulic behaviour. Finally, another aspect that needed to be addressed involved the comparison of time-lapse GPR data collected under different infiltration conditions (i.e., natural loading and forced infiltration conditions) to estimate the VGM parameters using the MCMC inversion strategy. The results show that for the synthetic example, considering data collected during a forced infiltration test helps to better refine soil hydraulic properties compared to data collected under natural infiltration conditions. When investigating data collected at the Arrenaes field site, further complications arised due to model error and showed the importance of also including a rigorous analysis of the propagation of model error with time and depth when considering time-lapse data. Although the efforts in this thesis were focused on GPR data, the corresponding findings are likely to have general applicability to other types of geophysical data and field environments. Moreover, the obtained results allow to have confidence for future developments in integration of geophysical data with stochastic inversions to improve the characterization of the unsaturated zone but also reveal important issues linked with stochastic inversions, namely model errors, that should definitely be addressed in future research.