Yes / In this study, the main emphasis is placed on the development and characterization of alkali-activated binders completely produced by the use of mixed construction and demolition waste (CDW)-based masonry units as aluminosilicate precursors. Combined usage of precursors was aimed to better simulate the real-life cases since in the incident of construction and demolition, these wastes are anticipated to be generated collectively. As different masonry units, red clay brick (RCB), hollow brick (HB) and roof tile (RT) were used in binary combinations by 75-25%, 50-50% and 25-75% of the total weight of the binder. Mixtures were produced with different curing temperature/periods and molarities of NaOH solution as the alkaline activator. Characterization was made by the compressive strength measurements supported by microstructural investigations which included the analyses of X-ray diffraction (XRD) and scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM/EDX). Results clearly showed that completely CDW-based masonry units can be effectively used collectively in producing alkali-activated binders having up to 80 MPa compressive strength provided that the mixture design parameters are optimized. Among different precursors utilized, HB seems to contribute more to the compressive strength. Irrespective of their composition, main reaction products of alkali-activated binders from CDW-based masonry units are sodium aluminosilicate hydrate (N-A-S-H) gels containing different zeolitic polytypes with structure ranging from amorphous to polycrystalline.

Les techniques d'analyse du visage nécessitent généralement une représentation pertinente des images, notamment en passant par des techniques de réduction de la dimension, intégrées dans des schémas plus globaux, et qui visent à capturer les caractéristiques discriminantes des signaux. Dans cette thèse, nous fournissons d'abord une vue générale sur l'état de l'art de ces modèles, puis nous appliquons une nouvelle méthode intégrant une approche non-linéaire, Kernel Similarity Principle Component Analysis (KS-PCA), aux Modèles Actifs d'Apparence (AAMs), pour modéliser l'apparence d'un visage dans des conditions d'illumination variables. L'algorithme proposé améliore notablement les résultats obtenus par l'utilisation d'une transformation PCA linéaire traditionnelle, que ce soit pour la capture des caractéristiques saillantes, produites par les variations d'illumination, ou pour la reconstruction des visages. Nous considérons aussi le problème de la classification automatiquement des poses des visages pour différentes vues et différentes illumination, avec occlusion et bruit. Basé sur les méthodes des représentations parcimonieuses, nous proposons deux cadres d'apprentissage de dictionnaire pour ce problème. Une première méthode vise la classification de poses à l'aide d'une représentation parcimonieuse active (Active Sparse Representation ASRC). En fait, un dictionnaire est construit grâce à un modèle linéaire, l'Incremental Principle Component Analysis (Incremental PCA), qui a tendance à diminuer la redondance intra-classe qui peut affecter la performance de la classification, tout en gardant la redondance inter-classes, qui elle, est critique pour les représentations parcimonieuses. La seconde approche proposée est un modèle des représentations parcimonieuses basé sur le Dictionary-Learning Sparse Representation (DLSR), qui cherche à intégrer la prise en compte du critère de la classification dans le processus d'apprentissage du dictionnaire. Nous faisons appel dans cette partie à l'algorithme K-SVD. Nos résultats expérimentaux montrent la performance de ces deux méthodes d'apprentissage de dictionnaire. Enfin, nous proposons un nouveau schéma pour l'apprentissage de dictionnaire adapté à la normalisation de l'illumination (Dictionary Learning for Illumination Normalization: DLIN). L'approche ici consiste à construire une paire de dictionnaires avec une représentation parcimonieuse. Ces dictionnaires sont construits respectivement à partir de visages illuminées normalement et irrégulièrement, puis optimisés de manière conjointe. Nous utilisons un modèle de mixture de Gaussiennes (GMM) pour augmenter la capacité à modéliser des données avec des distributions plus complexes. Les résultats expérimentaux démontrent l'efficacité de notre approche pour la normalisation d'illumination. / Face analysis techniques commonly require a proper representation of images by means of dimensionality reduction leading to embedded manifolds, which aims at capturing relevant characteristics of the signals. In this thesis, we first provide a comprehensive survey on the state of the art of embedded manifold models. Then, we introduce a novel non-linear embedding method, the Kernel Similarity Principal Component Analysis (KS-PCA), into Active Appearance Models, in order to model face appearances under variable illumination. The proposed algorithm successfully outperforms the traditional linear PCA transform to capture the salient features generated by different illuminations, and reconstruct the illuminated faces with high accuracy. We also consider the problem of automatically classifying human face poses from face views with varying illumination, as well as occlusion and noise. Based on the sparse representation methods, we propose two dictionary-learning frameworks for this pose classification problem. The first framework is the Adaptive Sparse Representation pose Classification (ASRC). It trains the dictionary via a linear model called Incremental Principal Component Analysis (Incremental PCA), tending to decrease the intra-class redundancy which may affect the classification performance, while keeping the extra-class redundancy which is critical for sparse representation. The other proposed work is the Dictionary-Learning Sparse Representation model (DLSR) that learns the dictionary with the aim of coinciding with the classification criterion. This training goal is achieved by the K-SVD algorithm. In a series of experiments, we show the performance of the two dictionary-learning methods which are respectively based on a linear transform and a sparse representation model. Besides, we propose a novel Dictionary Learning framework for Illumination Normalization (DL-IN). DL-IN based on sparse representation in terms of coupled dictionaries. The dictionary pairs are jointly optimized from normally illuminated and irregularly illuminated face image pairs. We further utilize a Gaussian Mixture Model (GMM) to enhance the framework's capability of modeling data under complex distribution. The GMM adapt each model to a part of the samples and then fuse them together. Experimental results demonstrate the effectiveness of the sparsity as a prior for patch-based illumination normalization for face images.

By recycling and reusing materials to close the resource cycle and move towards a circular economy, waste can be significantly reduced. At the same time, cities and regions offer opportunities to practice circular economy. As the main source of waste in the EU, the construction industry needs to both improve the recycling rate and find alternatives with a lower carbon footprint. Mineral wool waste is often considered non-recyclable, but the disposal of landfill will no longer be suitable in the medium to long term. At the same time, mineral wool can react with the alkaline activator to generate alkali activated concrete (AA concrete), which can be used as a substitute for cement concrete. This study is conducted on building typology to track mineral wool from individual building levels in Swiss dwellings over time and space. Afterward, Geographic Information System (GIS) was used to select the suitable location for establishing a mineral wool recycling plant, considering accessibility and transportation distance. Finally, using the life-cycle assessment (LCA) framework, emissions are calculated through the production phase. The results revealed that the mineral wool stock shows an increasing trend from 2020 to 2035, and can provide a stable supply. In addition, using mineral wool waste to produce AA concrete can effectively reduce CO2 emissions. Therefore, mineral wool waste has the potential to become raw material for alkali-activated materials (AAMs). Recycling factories can be set up in Zug, Zurich, and Bern as a priority. The recycling of mineral wool can be seen as a practical application of the circular economy strategy in the framework of urban planning.