Valeur pronostique de CD73 et des lymphocytes T CD8 et optimisation de la vaccination de type GVAX dans le cancer de la prostate

CD73 est un ecto-enzyme qui a été associé à la suppression de l'immunité anti-tumorale. Ses valeurs pronostiques et thérapeutiques ont été mises de l'avant dans plusieurs types de cancer. La première hypothèse du projet est que l'expression de CD73 dans la tumeur prédit le pronostic des patients atteints du cancer de la prostate. L'expression de CD73 a été étudiée par immunofluorescence dans des échantillons de tumeur. Puis, des analyses univariées et multivariées ont été conduites pour déterminer si l'expression de CD73 permet de prédire la récidive biochimique des patients. Nous avons déterminé que CD73 prédit indépendamment le pronostic des patients atteints du cancer de la prostate. De plus, nous avons déterminé que son expression dans le tissu normal adjacent ou dans la tumeur prédit différemment la survenue de la récidive biochimique. La deuxième hypothèse est que l'inhibition de CD73 permet d'améliorer l'efficacité d'un vaccin thérapeutique contre le cancer de la prostate. L'effet d'un vaccin de type GVAX a été étudié dans des souris CD73KO ou en combinaison avec un anticorps ciblant CD73. Nous avons observé que l'efficacité du vaccin était augmentée dans les souris où CD73 était absent. Cependant, la combinaison avec l'anti-CD73 n'a pas permis d'améliorer l'efficacité.

A III-V channel field effect transistor for non-classical CMOS: process optimisation for improved gate stack function

This thesis describes a collection of studies into the electrical response of a III-V MOS stack comprising metal/GaGdO/GaAs layers as a function of fabrication process variables and the findings of those studies. As a result of this work, areas of improvement in the gate process module of a III-V heterostructure MOSFET were identified. Compared to traditional bulk silicon MOSFET design, one featuring a III-V channel heterostructure with a high-dielectric-constant oxide as the gate insulator provides numerous benefits, for example: the insulator can be made thicker for the same capacitance, the operating voltage can be made lower for the same current output, and improved output characteristics can be achieved without reducing the channel length further. It is known that transistors composed of III-V materials are most susceptible to damage induced by radiation and plasma processing. These devices utilise sub-10 nm gate dielectric films, which are prone to contamination, degradation and damage. Therefore, throughout the course of this work, process damage and contamination issues, as well as various techniques to mitigate or prevent those have been investigated through comparative studies of III-V MOS capacitors and transistors comprising various forms of metal gates, various thicknesses of GaGdO dielectric, and a number of GaAs-based semiconductor layer structures. Transistors which were fabricated before this work commenced, showed problems with threshold voltage control. Specifically, MOSFETs designed for normally-off (VTH > 0) operation exhibited below-zero threshold voltages. With the results obtained during this work, it was possible to gain an understanding of why the transistor threshold voltage shifts as the gate length decreases and of what pulls the threshold voltage downwards preventing normally-off device operation. Two main culprits for the negative VTH shift were found. The first was radiation damage induced by the gate metal deposition process, which can be prevented by slowing down the deposition rate. The second was the layer of gold added on top of platinum in the gate metal stack which reduces the effective work function of the whole gate due to its electronegativity properties. Since the device was designed for a platinum-only gate, this could explain the below zero VTH. This could be prevented either by using a platinum-only gate, or by matching the layer structure design and the actual gate metal used for the future devices. Post-metallisation thermal anneal was shown to mitigate both these effects. However, if post-metallisation annealing is used, care should be taken to ensure it is performed before the ohmic contacts are formed as the thermal treatment was shown to degrade the source/drain contacts. In addition, the programme of studies this thesis describes, also found that if the gate contact is deposited before the source/drain contacts, it causes a shift in threshold voltage towards negative values as the gate length decreases, because the ohmic contact anneal process affects the properties of the underlying material differently depending on whether it is covered with the gate metal or not. In terms of surface contamination; this work found that it causes device-to-device parameter variation, and a plasma clean is therefore essential. This work also demonstrated that the parasitic capacitances in the system, namely the contact periphery dependent gate-ohmic capacitance, plays a significant role in the total gate capacitance. This is true to such an extent that reducing the distance between the gate and the source/drain ohmic contacts in the device would help with shifting the threshold voltages closely towards the designed values. The findings made available by the collection of experiments performed for this work have two major applications. Firstly, these findings provide useful data in the study of the possible phenomena taking place inside the metal/GaGdO/GaAs layers and interfaces as the result of chemical processes applied to it. In addition, these findings allow recommendations as to how to best approach fabrication of devices utilising these layers.

'Bayesian Optimisation for Nurse Scheduling'

A Bayesian optimisation algorithm for a nurse scheduling problem is presented, which involves choosing a suitable scheduling rule from a set for each nurse's assignment. When a human scheduler works, he normally builds a schedule systematically following a set of rules. After much practice, the scheduler gradually masters the knowledge of which solution parts go well with others. He can identify good parts and is aware of the solution quality even if the scheduling process is not yet completed, thus having the ability to finish a schedule by using flexible, rather than fixed, rules. In this paper, we design a more human-like scheduling algorithm, by using a Bayesian optimisation algorithm to implement explicit learning from past solutions. A nurse scheduling problem from a UK hospital is used for testing. Unlike our previous work that used Genetic Algorithms to implement implicit learning [1], the learning in the proposed algorithm is explicit, i.e. we identify and mix building blocks directly. The Bayesian optimisation algorithm is applied to implement such explicit learning by building a Bayesian network of the joint distribution of solutions. The conditional probability of each variable in the network is computed according to an initial set of promising solutions. Subsequently, each new instance for each variable is generated by using the corresponding conditional probabilities, until all variables have been generated, i.e. in our case, new rule strings have been obtained. Sets of rule strings are generated in this way, some of which will replace previous strings based on fitness. If stopping conditions are not met, the conditional probabilities for all nodes in the Bayesian network are updated again using the current set of promising rule strings. For clarity, consider the following toy example of scheduling five nurses with two rules (1: random allocation, 2: allocate nurse to low-cost shifts). In the beginning of the search, the probabilities of choosing rule 1 or 2 for each nurse is equal, i.e. 50%. After a few iterations, due to the selection pressure and reinforcement learning, we experience two solution pathways: Because pure low-cost or random allocation produces low quality solutions, either rule 1 is used for the first 2-3 nurses and rule 2 on remainder or vice versa. In essence, Bayesian network learns 'use rule 2 after 2-3x using rule 1' or vice versa. It should be noted that for our and most other scheduling problems, the structure of the network model is known and all variables are fully observed. In this case, the goal of learning is to find the rule values that maximize the likelihood of the training data. Thus, learning can amount to 'counting' in the case of multinomial distributions. For our problem, we use our rules: Random, Cheapest Cost, Best Cover and Balance of Cost and Cover. In more detail, the steps of our Bayesian optimisation algorithm for nurse scheduling are: 1. Set t = 0, and generate an initial population P(0) at random; 2. Use roulette-wheel selection to choose a set of promising rule strings S(t) from P(t); 3. Compute conditional probabilities of each node according to this set of promising solutions; 4. Assign each nurse using roulette-wheel selection based on the rules' conditional probabilities. A set of new rule strings O(t) will be generated in this way; 5. Create a new population P(t+1) by replacing some rule strings from P(t) with O(t), and set t = t+1; 6. If the termination conditions are not met (we use 2000 generations), go to step 2. Computational results from 52 real data instances demonstrate the success of this approach. They also suggest that the learning mechanism in the proposed approach might be suitable for other scheduling problems. Another direction for further research is to see if there is a good constructing sequence for individual data instances, given a fixed nurse scheduling order. If so, the good patterns could be recognized and then extracted as new domain knowledge. Thus, by using this extracted knowledge, we can assign specific rules to the corresponding nurses beforehand, and only schedule the remaining nurses with all available rules, making it possible to reduce the solution space. Acknowledgements The work was funded by the UK Government's major funding agency, Engineering and Physical Sciences Research Council (EPSRC), under grand GR/R92899/01. References [1] Aickelin U, "An Indirect Genetic Algorithm for Set Covering Problems", Journal of the Operational Research Society, 53(10): 1118-1126,

'A Bayesian Optimisation Algorithm for the Nurse Scheduling Problem'

Abstract- A Bayesian optimization algorithm for the nurse scheduling problem is presented, which involves choosing a suitable scheduling rule from a set for each nurse's assignment. Unlike our previous work that used GAs to implement implicit learning, the learning in the proposed algorithm is explicit, i.e. eventually, we will be able to identify and mix building blocks directly. The Bayesian optimization algorithm is applied to implement such explicit learning by building a Bayesian network of the joint distribution of solutions. The conditional probability of each variable in the network is computed according to an initial set of promising solutions. Subsequently, each new instance for each variable is generated by using the corresponding conditional probabilities, until all variables have been generated, i.e. in our case, a new rule string has been obtained. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the conditional probabilities for all nodes in the Bayesian network are updated again using the current set of promising rule strings. Computational results from 52 real data instances demonstrate the success of this approach. It is also suggested that the learning mechanism in the proposed approach might be suitable for other scheduling problems.

Optimisation of a crossdocking distribution centre simulation model

This paper reports on continuing research into the modelling of an order picking process within a Crossdocking distribution centre using Simulation Optimisation. The aim of this project is to optimise a discrete event simulation model and to understand factors that affect finding its optimal performance. Our initial investigation revealed that the precision of the selected simulation output performance measure and the number of replications required for the evaluation of the optimisation objective function through simulation influences the ability of the optimisation technique. We experimented with Common Random Numbers, in order to improve the precision of our simulation output performance measure, and intended to use the number of replications utilised for this purpose as the initial number of replications for the optimisation of our Crossdocking distribution centre simulation model. Our results demonstrate that we can improve the precision of our selected simulation output performance measure value using Common Random Numbers at various levels of replications. Furthermore, after optimising our Crossdocking distribution centre simulation model, we are able to achieve optimal performance using fewer simulations runs for the simulation model which uses Common Random Numbers as compared to the simulation model which does not use Common Random Numbers.

Continuous Quality Assurance and Optimisation in Cloud-Based Virtual Enterprises

Part 18: Optimization in Collaborative Networks

An evolutionary squeaky wheel optimisation approach to personnel scheduling

The quest for robust heuristics that are able to solve more than one problem is ongoing. In this paper, we present, discuss and analyse a technique called Evolutionary Squeaky Wheel Optimisation and apply it to two different personnel scheduling problems. Evolutionary Squeaky Wheel Optimisation improves the original Squeaky Wheel Optimisation’s effectiveness and execution speed by incorporating two additional steps (Selection and Mutation) for added evolution. In the Evolutionary Squeaky Wheel Optimisation, a cycle of Analysis-Selection-Mutation-Prioritization-Construction continues until stopping conditions are reached. The aim of the Analysis step is to identify below average solution components by calculating a fitness value for all components. The Selection step then chooses amongst these underperformers and discards some probabilistically based on fitness. The Mutation step further discards a few components at random. Solutions can become incomplete and thus repairs may be required. The repair is carried out by using the Prioritization step to first produce priorities that determine an order by which the following Construction step then schedules the remaining components. Therefore, improvements in the Evolutionary Squeaky Wheel Optimisation is achieved by selective solution disruption mixed with iterative improvement and constructive repair. Strong experimental results are reported on two different domains of personnel scheduling: bus and rail driver scheduling and hospital nurse scheduling.

FOCUS : frailty management optimisation through EIPAHA commitments and utilisation of stakeholders’ input – an innovative European project in elderly care

The goal of FOCUS, which stands for Frailty Management Optimization through EIPAHA Commitments and Utilization of Stakeholders’ Input, is to reduce the burden of frailty in Europe. The partners are working on advancing knowledge of frailty detection, assessment, and management, including biological, clinical, cognitive and psychosocial markers, in order to change the paradigm of frailty care from acute intervention to prevention. FOCUS partners are working on ways to integrate the best available evidence from frailty-related screening tools, epidemiological and interventional studies into the care of frail people and their quality of life. Frail citizens in Italy, Poland and the UK and their caregivers are being called to express their views and their experiences with treatments and interventions aimed at improving quality of life. The FOCUS Consortium is developing pathways to leverage the knowledge available and to put it in the service of frail citizens. In order to reach out to the broadest audience possible, the FOCUS Platform for Knowledge Exchange and the platform for Scaling Up are being developed with the collaboration of stakeholders. The FOCUS project is a development of the work being done by the European Innovation Partnership on Active and Healthy Ageing (EIPAHA), which aims to increase the average healthy lifespan in Europe by 2020 while fostering sustainability of health/social care systems and innovation in Europe. The knowledge and tools developed by the FOCUS project, with input from stakeholders, will be deployed to all EIPAHA participants dealing with frail older citizens to support activities and optimize performance.

Theory and optimisation of 1.3 and 1.55 μm (Al)InGaAs metamorphic quantum well lasers

The use of InGaAs metamorphic buffer layers (MBLs) to facilitate the growth of lattice-mismatched heterostructures constitutes an attractive approach to developing long-wavelength semiconductor lasers on GaAs substrates, since they offer the improved carrier and optical confinement associated with GaAs-based materials. We present a theoretical study of GaAs-based 1.3 and 1.55 μm (Al)InGaAs quantum well (QW) lasers grown on InGaAs MBLs. We demonstrate that optimised 1.3 μm metamorphic devices offer low threshold current densities and high differential gain, which compare favourably with InP-based devices. Overall, our analysis highlights and quantifies the potential of metamorphic QWs for the development of GaAs-based long-wavelength semiconductor lasers, and also provides guidelines for the design of optimised devices.

Amélioration de la représentation des processus stochastiques pour l'optimisation appliquée à la gestion des systèmes hydriques

Dans un contexte de pression toujours plus grande sur les ressources naturelles, une gestion rationnelle des ressources hydriques s'impose. La principale difficulté de leur gestion provient du caractère aléatoire des apports en eau dans le système. Le sujet de cette recherche consiste à développer des méthodes d'optimisation stochastique capable de bien représenter les processus aléatoires. Le cas de Kemano, située en Colombie-Britannique (Canada), illustre les travaux de recherche. L'importante accumulation de neige sur les bassins versants engendre une hydrologie complexe, rendant la gestion du système délicate. La programmation dynamique stochastique est la méthode la plus utilisée pour déterminer la politique de gestion des réservoirs. Mais, son étude fait ressortir que cette méthode ne peut gérer que des modèles simplifiés des processus stochastiques, ne rendant pas compte des complexes corrélations spatio-temporelles des apports hydriques. Ainsi, la politique obtenue peut être de mauvaise qualité. Cette méthode est comparée avec la recherche directe de politique qui n'utilise pas de modèles pour représenter les processus stochastiques, mais évalue la politique sur des scénarios d'apports. Ainsi la recherche directe de politique se révèle globalement plus performante en prenant bien en considération la complexité des apports, mais est limitée par la forme prédéterminée de la politique. De plus, l'optimisation des paramètres en utilisant un algorithme évolutionnaire s'avère lente. La conception d'un algorithme de descente par gradient, combinée à une architecture "acteur-critique" appropriée, permet de réduire notablement le temps d'optimisation. Combinée à une fonction plus complexe employée pour la paramétrisation de la politique de gestion, la méthode permet d'obtenir une politique de qualité significativement supérieure à celle obtenue avec la programmation dynamique stochastique. Les travaux effectués dans le cadre de cette thèse ouvrent la voie à une application opérationnelle de la méthode de recherche directe de politique. L'évaluation en simulation devrait être appréciée des opérateurs en permettant une bonne représentation du système et des apports.

Retrofit de systèmes de revalorisation de chaleur industrielle à basse température par optimisation exergo-économique

Ce projet porte, dans un souci d’efficacité énergétique, sur la récupération d’énergie des rejets thermiques à basse température. Une analyse d’optimisation des technologies dans le but d’obtenir un système de revalorisation de chaleur rentable fait objet de cette recherche. Le but sera de soutirer la chaleur des rejets thermiques et de la réappliquer à un procédé industriel. Réduire la consommation énergétique d’une usine entre habituellement en conflit avec l’investissement requis pour les équipements de revalorisation de chaleur. Ce projet de maitrise porte sur l’application d’optimisations multiobjectives par algorithme génétique (GA) pour faciliter le design en retrofit des systèmes de revalorisation de chaleur industrielle. L’originalité de cette approche consiste à l’emploi du «fast non-dominant sorting genetic algorithm» ou NSGA-II dans le but de trouver les solutions optimales entre la valeur capitale et les pertes exergétiques des réseaux d’échangeurs de chaleur et de pompes à chaleur. Identifier les solutions optimales entre le coût et l’efficacité exergétique peut ensuite aider dans le processus de sélection d’un design approprié en considérant les coûts énergétiques. Afin de tester cette approche, une étude de cas est proposée pour la récupération de chaleur dans une usine de pâte et papier. Ceci inclut l’intégration d’échangeur de chaleur Shell&tube, d’échangeur à contact direct et de pompe à chaleur au réseau thermique existant. Pour l’étude de cas, le projet en collaboration avec Cascades est constitué de deux étapes, soit de ciblage et d’optimisation de solutions de retrofit du réseau d’échangeur de chaleur de l’usine de tissus Cascades à Kinsley Falls. L’étape de ciblage, basée sur la méthode d’analyse du pincement, permet d’identifier et de sélectionner les modifications de topologie du réseau d’échangeurs existant en y ajoutant de nouveaux équipements. Les scénarios résultants passent ensuite à l’étape d’optimisation où les modèles mathématiques pour chaque nouvel équipement sont optimisés afin de produire une courbe d’échange optimal entre le critère économique et exergétique. Pourquoi doubler l’analyse économique d’un critère d’exergie? D’abord, parce que les modèles économiques sont par définition de nature imprécise. Coupler les résultats des modèles économiques avec un critère exergétique permet d’identifier des solutions de retrofit plus efficaces sans trop s’éloigner d’un optimum économique. Ensuite, le rendement exergétique permet d’identifier les designs utilisant l’énergie de haute qualité, telle que l’électricité ou la vapeur, de façon plus efficace lorsque des sources d’énergie de basse qualité, telles que les effluents thermiques, sont disponibles. Ainsi en choisissant un design qui détruit moins d’exergie, il demandera un coût énergétique moindre. Les résultats de l’étude de cas publiés dans l’article montrent une possibilité de réduction des coûts en demande de vapeur de 89% tout en réduisant la destruction d’exergie de 82%. Dans certains cas de retrofit, la solution la plus justifiable économiquement est également très proche de la solution à destruction d’exergie minimale. L’analyse du réseau d’échangeurs et l’amélioration de son rendement exergétique permettront de justifier l’intégration de ces systèmes dans l’usine. Les diverses options pourront ensuite être considérées par Cascades pour leurs faisabilités technologiques et économiques sachant qu’elles ont été optimisées.

Optimisation thermodynamique d’un procédé solaire utilisant un système de réfrigération à éjecto-compression pour la production du froid

L’objectif essentiel de cette thèse est de développer un système industriel de réfrigération ou de climatisation qui permet la conversion du potentiel de l’énergie solaire en production du froid. Ce système de réfrigération est basé sur la technologie de l’éjecto-compression qui propose la compression thermique comme alternative économique à la compression mécanique coûteuse. Le sous-système de réfrigération utilise un appareil statique fiable appelé éjecteur actionné seulement par la chaleur utile qui provient de l’énergie solaire. Il est combiné à une boucle solaire composée entre autres de capteurs solaires cylindro-paraboliques à concentration. Cette combinaison a pour objectif d’atteindre des efficacités énergétiques et exergétiques globales importantes. Le stockage thermique n’est pas considéré dans ce travail de thèse mais sera intégré au système dans des perspectives futures. En première étape, un nouveau modèle numérique et thermodynamique d’un éjecteur monophasique a été développé. Ce modèle de design applique les conditions d’entrée des fluides (pression, température et vitesse) et leur débit. Il suppose que le mélange se fait à pression constante et que l’écoulement est subsonique à l’entrée du diffuseur. Il utilise un fluide réel (R141b) et la pression de sortie est imposée. D’autre part, il intègre deux innovations importantes : il utilise l'efficacité polytropique constante (plutôt que des efficacités isentropiques constantes utilisées souvent dans la littérature) et n’impose pas une valeur fixe de l'efficacité du mélange, mais la détermine à partir des conditions d'écoulement calculées. L’efficacité polytropique constante est utilisée afin de quantifier les irréversibilités au cours des procédés d’accélérations et de décélération comme dans les turbomachines. La validation du modèle numérique de design a été effectuée à l’aide d’une étude expérimentale présente dans la littérature. La seconde étape a pour but de proposer un modèle numérique basé sur des données expérimentales de la littérature et compatible à TRNSYS et un autre modèle numérique EES destinés respectivement au capteur solaire cylindro-parabolique et au sous-système de réfrigération à éjecteur. En définitive et après avoir développé les modèles numériques et thermodynamiques, une autre étude a proposé un modèle pour le système de réfrigération solaire à éjecteur intégrant ceux de ses composantes. Plusieurs études paramétriques ont été entreprises afin d’évaluer les effets de certains paramètres (surchauffe du réfrigérant, débit calorifique du caloporteur et rayonnement solaire) sur sa performance. La méthodologie proposée est basée sur les lois de la thermodynamique classique et sur les relations de la thermodynamique aux dimensions finies. De nouvelles analyses exergétiques basées sur le concept de l’exergie de transit ont permis l'évaluation de deux indicateurs thermodynamiquement importants : l’exergie produite et l’exergie consommée dont le rapport exprime l’efficacité exergétique intrinsèque. Les résultats obtenus à partir des études appliquées à l’éjecteur et au système global montrent que le calcul traditionnel de l’efficacité exergétique selon Grassmann n’est désormais pas un critère pertinent pour l'évaluation de la performance thermodynamique des éjecteurs pour les systèmes de réfrigération.

Optimisation de l’extraction de lumière de scintillation dans les matrices de détecteurs pixellisés pour des applications en tomographie d’émission par positrons (TEP) et en tomodensitométrie (TDM)

Résumé : Les performances de détecteurs à scintillation, composés d’un cristal scintillateur couplé à un photodétecteur, dépendent de façon critique de l’efficacité de la collecte et de l’extraction des photons de scintillation du cristal vers le capteur. Dans les systèmes d’imagerie hautement pixellisés (e.g. TEP, TDM), les scintillateurs doivent être arrangés en matrices compactes avec des facteurs de forme défavorables pour le transport des photons, au détriment des performances du détecteur. Le but du projet est d’optimiser les performances de ces détecteurs pixels par l'identification des sources de pertes de lumière liées aux caractéristiques spectrales, spatiales et angulaires des photons de scintillation incidents sur les faces des scintillateurs. De telles informations acquises par simulation Monte Carlo permettent une pondération adéquate pour l'évaluation de gains atteignables par des méthodes de structuration du scintillateur visant à une extraction de lumière améliorée vers le photodétecteur. Un plan factoriel a permis d'évaluer la magnitude de paramètres affectant la collecte de lumière, notamment l'absorption des matériaux adhésifs assurant l'intégrité matricielle des cristaux ainsi que la performance optique de réflecteurs, tous deux ayant un impact considérable sur le rendement lumineux. D'ailleurs, un réflecteur abondamment utilisé en raison de ses performances optiques exceptionnelles a été caractérisé dans des conditions davantage réalistes par rapport à une immersion dans l'air, où sa réflectivité est toujours rapportée. Une importante perte de réflectivité lorsqu'il est inséré au sein de matrices de scintillateurs a été mise en évidence par simulations puis confirmée expérimentalement. Ceci explique donc les hauts taux de diaphonie observés en plus d'ouvrir la voie à des méthodes d'assemblage en matrices limitant ou tirant profit, selon les applications, de cette transparence insoupçonnée.

Développement des bétons autoplaçants à faible teneur en poudre, Éco-BAP: formulation et performance

Abstract : Although concrete is a relatively green material, the astronomical volume of concrete produced worldwide annually places the concrete construction sector among the noticeable contributors to the global warming. The most polluting constituent of concrete is cement due to its production process which releases, on average, 0.83 kg CO[subscript 2] per kg of cement. Self-consolidating concrete (SCC), a type of concrete that can fill in the formwork without external vibration, is a technology that can offer a solution to the sustainability issues of concrete industry. However, all of the workability requirements of SCC originate from a higher powder content (compared to conventional concrete) which can increase both the cost of construction and the environmental impact of SCC for some applications. Ecological SCC, Eco-SCC, is a recent development combing the advantages of SCC and a significantly lower powder content. The maximum powder content of this concrete, intended for building and commercial construction, is limited to 315 kg/m[superscript 3]. Nevertheless, designing Eco-SCC can be challenging since a delicate balance between different ingredients of this concrete is required to secure a satisfactory mixture. In this Ph.D. program, the principal objective is to develop a systematic design method to produce Eco-SCC. Since the particle lattice effect (PLE) is a key parameter to design stable Eco-SCC mixtures and is not well understood, in the first phase of this research, this phenomenon is studied. The focus in this phase is on the effect of particle-size distribution (PSD) on the PLE and stability of model mixtures as well as SCC. In the second phase, the design protocol is developed, and the properties of obtained Eco-SCC mixtures in both fresh and hardened states are evaluated. Since the assessment of robustness is crucial for successful production of concrete on large-scale, in the final phase of this work, the robustness of one the best-performing mixtures of Phase II is examined. It was found that increasing the volume fraction of a stable size-class results in an increase in the stability of that class, which in turn contributes to a higher PLE of the granular skeleton and better stability of the system. It was shown that a continuous PSD in which the volume fraction of each size class is larger than the consecutive coarser class can increase the PLE. Using such PSD was shown to allow for a substantial increase in the fluidity of SCC mixture without compromising the segregation resistance. An index to predict the segregation potential of a suspension of particles in a yield stress fluid was proposed. In the second phase of the dissertation, a five-step design method for Eco-SCC was established. The design protocol started with the determination of powder and water contents followed by the optimization of sand and coarse aggregate volume fractions according to an ideal PSD model (Funk and Dinger). The powder composition was optimized in the third step to minimize the water demand while securing adequate performance in the hardened state. The superplasticizer (SP) content of the mixtures was determined in next step. The last step dealt with the assessment of the global warming potential of the formulated Eco-SCC mixtures. The optimized Eco-SCC mixtures met all the requirements of self-consolidation in the fresh state. The 28-day compressive strength of such mixtures complied with the target range of 25 to 35 MPa. In addition, the mixtures showed sufficient performance in terms of drying shrinkage, electrical resistivity, and frost durability for the intended applications. The eco-performance of the developed mixtures was satisfactory as well. It was demonstrated in the last phase that the robustness of Eco-SCC is generally good with regards to water content variations and coarse aggregate characteristics alterations. Special attention must be paid to the dosage of SP during batching.

Numerical simulation of fresh SCC flow in wall and beam elements using flow dynamics models

Abstract : Recently, there is a great interest to study the flow characteristics of suspensions in different environmental and industrial applications, such as snow avalanches, debris flows, hydrotransport systems, and material casting processes. Regarding rheological aspects, the majority of these suspensions, such as fresh concrete, behave mostly as non-Newtonian fluids. Concrete is the most widely used construction material in the world. Due to the limitations that exist in terms of workability and formwork filling abilities of normal concrete, a new class of concrete that is able to flow under its own weight, especially through narrow gaps in the congested areas of the formwork was developed. Accordingly, self-consolidating concrete (SCC) is a novel construction material that is gaining market acceptance in various applications. Higher fluidity characteristics of SCC enable it to be used in a number of special applications, such as densely reinforced sections. However, higher flowability of SCC makes it more sensitive to segregation of coarse particles during flow (i.e., dynamic segregation) and thereafter at rest (i.e., static segregation). Dynamic segregation can increase when SCC flows over a long distance or in the presence of obstacles. Therefore, there is always a need to establish a trade-off between the flowability, passing ability, and stability properties of SCC suspensions. This should be taken into consideration to design the casting process and the mixture proportioning of SCC. This is called “workability design” of SCC. An efficient and non-expensive workability design approach consists of the prediction and optimization of the workability of the concrete mixtures for the selected construction processes, such as transportation, pumping, casting, compaction, and finishing. Indeed, the mixture proportioning of SCC should ensure the construction quality demands, such as demanded levels of flowability, passing ability, filling ability, and stability (dynamic and static). This is necessary to develop some theoretical tools to assess under what conditions the construction quality demands are satisfied. Accordingly, this thesis is dedicated to carry out analytical and numerical simulations to predict flow performance of SCC under different casting processes, such as pumping and tremie applications, or casting using buckets. The L-Box and T-Box set-ups can evaluate flow performance properties of SCC (e.g., flowability, passing ability, filling ability, shear-induced and gravitational dynamic segregation) in casting process of wall and beam elements. The specific objective of the study consists of relating numerical results of flow simulation of SCC in L-Box and T-Box test set-ups, reported in this thesis, to the flow performance properties of SCC during casting. Accordingly, the SCC is modeled as a heterogeneous material. Furthermore, an analytical model is proposed to predict flow performance of SCC in L-Box set-up using the Dam Break Theory. On the other hand, results of the numerical simulation of SCC casting in a reinforced beam are verified by experimental free surface profiles. The results of numerical simulations of SCC casting (modeled as a single homogeneous fluid), are used to determine the critical zones corresponding to the higher risks of segregation and blocking. The effects of rheological parameters, density, particle contents, distribution of reinforcing bars, and particle-bar interactions on flow performance of SCC are evaluated using CFD simulations of SCC flow in L-Box and T-box test set-ups (modeled as a heterogeneous material). Two new approaches are proposed to classify the SCC mixtures based on filling ability and performability properties, as a contribution of flowability, passing ability, and dynamic stability of SCC.