La rénovation et la réhabilitation énergétique du parc de bâtiment est un des grands défis identifiés pour les décennies à venir. Devant cet impératif d'une rénovation d'ampleur du parc construit, les solutions techniques utilisant des ossatures rapportées sont employées de plus en plus fréquemment car elles permettent de combiner performance thermique, renouvellement esthétique et ajouts fonctionnels. Une des difficultés dans l'amélioration et l'automatisation des projets de rénovation est liée à la connaissance de la géométrie du bâti existant. Des plans de l'état existant ne sont pas toujours disponibles, et dans le cas échéant, ne sont pas forcément exacts en raison de modifications apportées au bâti et non documentées ou d'écarts initiaux entre les plans et la réalisation. Après une étude bibliographique des méthodes existantes détaillées dans le chapitre 1. Les travaux menés dans le cadre d'une collaboration entre le CRITT Bois et le CRAN ont permis de développer une méthode automatique de reconstruction 3D du modèle de bâtiments à partir de nuages de points obtenus par scanner LASER terrestre. La méthode proposée se décompose en trois phases principales. La première phase détaillée dans le deuxième chapitre, consiste à segmenter le nuage de points en plusieurs plans représentants les façades du bâtiment. L'exploitation de la colorimétrie durant la phase de segmentation du nuage de points permet une réduction importante de la complexité de l'algorithme de segmentation géométrique. L'approche consiste à effectuer dans un premier temps une classification préalable du nuage de points en se basant sur les informations colorimétriques de chaque point. Puis, dans un second temps, il s'agit d'effectuer une segmentation géométrique du nuage de points en utilisant un algorithme de segmentation robuste (RANSAC). Le chapitre 3 présente la deuxième phase qui consiste à modéliser le pas d'échantillonnage de la surface à partir duquel on définit le seuil d'extraction des points de contours. Le but étant d'améliorer la fiabilité d'extraction des points de contours, ainsi que l'approximation de l'erreur sur le modèle. Le chapitre 4 détaille les principales étapes de la reconstruction d'un modèle filaire. Dans un premier temps les régions définies par les points de contours sont classées en Régions d'Irrégularités (RI), Régions d'Eléments Architecturaux (REA) comme les fenêtres et Régions de Façade (RF) représentées par les contours extérieurs de la façade. La deuxième étape consiste à modéliser les différentes régions, par un maillage de Delaunay pour les RI, et des polyèdres pour les RF et REA. La dernière étape calcule une approximation de l'erreur sur le modèle. Les tests de fiabilité de la méthode ont été réalisés sur des chantiers réels conduits par des industriels de la construction et de la rénovation. Il en ressort que la qualité de la reconstruction 3D reste fortement dépendante des facteurs d'acquisition ainsi que de la surface numérisée. L'approximation de l'erreur de modélisation permet ainsi de prévoir à l'avance les erreurs sur le modèle CAO. / The renovation and the improvement of the energy efficiency of existing housing stock is one of big challenges identified for coming decades. In front of this imperative, timber based elements for building renovation are more and more used due to their substantial improvement of the building insulation, aesthetic renewal and functional additions. However, this technology faces some difficulties, one of them is the improvement of the renovation projects automation, which is bound to the knowledge of the existing built geometry. The plans representing the existing state of the building are not always available, and if so, they may be not exact, because the modifications made on the building are usually undocumented. After a literature review of existing methods which are detailed the first chapter. The work within the framework of cooperation between the CRITTBois and CRAN have allowed to develop an automatic method for 3D building CAD model reconstruction from point clouds acquired by a terrestrial LASER scanner. The proposed method is composed of three main phases. The first one detailed in the second chapter, consists in segmenting the point cloud into planar patches representing the building facades. To decrease the segmentation algorithm complexity, the colorimetric information is also considered. The approach consists in making a colorimetric classification of the point cloud in a first step, then a geometrical segmentation of the point cloud using a robust segmentation algorithm (RANSAC). The third chapter presents the second phase of our approach consists in surface sampling steps modeling and boundary point extraction. Here, we consider a local threshold defined according to the approximated surface sampling steps. The aim of considering local threshold is to improve the reliability of the boundary point extraction algorithm and approximating the CAD model error. The last chapter presents the main three steps of the boundary model reconstruction method. The first step consists in classifying the regions defined by their boundary points into three types of regions: Irregularity Region (IR), Architectural Element Region (AER) as windows, and Facades Regions (FR) which represent the building facades defined by their outer boundaries. The second step consists in modeling these regions considering a Delaunay triangulation for the IR and a polyhedral model for the AER and the FR. The third step consists in making an approximation of the error in the model. The method reliability tests were conducted on real projects; they were performed by industrial construction and renovation professionals. The tests show that the quality of the 3D reconstruction remains strongly dependent to the acquisition factors and the scanned surface properties. Also, the approximation of the modeling error can predict in advance the errors on the CAD model.

Volvo CE is going through a transformation leaving diesel driven machines in favor of batteryn and fuel cell driven machines. The earthmoving operations in the constructing industry are constantly evolving, improving productivity, safety, operation, maintenance, and management of equipment. For instance, real-time tracking and localization of earthmoving equipment provide numerous advantages in construction and mining. Therefore, it is of interest to investigate the circumstances of the location where the vehicles operates, to find solutions for optimizing productivity and minimizing emissions. The aim of this study is to approximate length and slope of existing hills in the area. The aim is answered by reconstructing and defining a continuous surface from data points describing the area, along with attempting the construction of potential paths which the machines experiences. To achieve the central objectives, the study employs various techniques including interpolation methods and processes for producing paths of different characteristics. The constructing of paths also includes clustering of data, to identify subregions and roads or intersections. These processes are discussed in the theoretical background and their detailed implementation is presented in the methodology section. The results indicates that the Radial Basis Function interpolation provides a smooth, continuous surface reconstruction, bounded by the implementation of alpha shape. However, in accuracy performance tests, the cubic splines interpolation indicates marginally better results. The formed path algorithm enables a heuristic approximation of machine paths. Comparative measures in slope distributions to machine tilt data, indicates that slope from linear distance paths showed the highest similarities.  The bounded interpolated surface describes the area's characteristics, allowing investigation regarding the circumstances of the location where the machines operates. The approximation of potential paths provides insights into the properties of the terrain, revealing length and slopes of hills. The distribution of slope and hill length can be leveraged to optimize battery selection for electrified vehicles.

Models are important tools for understanding the hydrological processes that govern water transport in the landscape and for prediction at times and places where no observations are available. The degree of trust placed on models, however, should not exceed the quality of the data they are fed with. The overall aim of this thesis was to tune the modeling process to account for the uncertainty in the data, by identifying robust parameter values using methods from computational geometry. The methods were developed and tested on data from the Choluteca River basin in Honduras. Quality control of precipitation and discharge data resulted in a rejection of 22% percent of daily raingage data and the complete removal of one out of the seven discharge stations analyzed. The raingage network was not found sufficient to capture the spatial and temporal variability of precipitation in the Choluteca River basin. The temporal variability of discharge was evaluated through a Monte Carlo assessment of the rating-equation parameter values over a moving time window of stage-discharge measurements. Al hydrometric stations showed considerable temporal variability in the stage-discharge relationship, which was largest for low flows, albeit with no common trend. The problem with limited data quality was addressed by identifying robust model parameter values within the set of well-performing (behavioral) parameter-value vectors with computational-geometry methods. The hypothesis that geometrically deep parameter-value vectors within the behavioral set were hydrologically robust was tested, and verified, using two depth functions. Deep parameter-value vectors tended to perform better than shallow ones, were less sensitive to small changes in their values, and were better suited to temporal transfer. Depth functions rank multidimensional data. Methods to visualize the multivariate distribution of behavioral parameters based on the ranked values were developed. It was shown that, by projecting along a common dimension, the multivariate distribution of behavioral parameters for models of varying complexity could be compared using the proposed visualization tools. This has a potential to aid in the selection of an adequate model structure considering the uncertainty in the data. These methods allowed to quantify observational uncertainties. Geometric methods have only recently begun to be used in hydrology. It was shown that they can be used to identify robust parameter values, and some of their potential uses were highlighted. / Modeller är viktiga verktyg för att förstå de hydrologiska processer som bestämmer vattnets transport i landskapet och för prognoser för tider och platser där det saknas mätdata. Graden av tillit till modeller bör emellertid inte överstiga kvaliteten på de data som de matas med. Det övergripande syftet med denna avhandling var att anpassa modelleringsprocessen så att den tar hänsyn till osäkerheten i data och identifierar robusta parametervärden med hjälp av metoder från beräkningsgeometrin. Metoderna var utvecklade och testades på data från Cholutecaflodens avrinningsområde i Honduras. Kvalitetskontrollen i nederbörds- och vattenföringsdata resulterade i att 22 % av de dagliga nederbördsobservationerna måste kasseras liksom alla data från en av sju analyserade vattenföringsstationer. Observationsnätet för nederbörd befanns otillräckligt för att fånga upp den rumsliga och tidsmässiga variabiliteten i den övre delen av Cholutecaflodens avrinningsområde. Vattenföringens tidsvariation utvärderades med en Monte Carlo-skattning av värdet på parametrarna i avbördningskurvan i ett rörligt tidsfönster av vattenföringsmätningar. Alla vattenföringsstationer uppvisade stor tidsvariation i avbördningskurvan som var störst för låga flöden, dock inte med någon gemensam trend. Problemet med den måttliga datakvaliteten bedömdes med hjälp av robusta modellparametervärden som identifierades med hjälp av beräkningsgeometriska metoder. Hypotesen att djupa parametervärdesuppsättningar var robusta testades och verifierades genom två djupfunktioner. Geometriskt djupa parametervärdesuppsättningar verkade ge bättre hydrologiska resultat än ytliga, var mindre känsliga för små ändringar i parametervärden och var bättre lämpade för förflyttning i tiden. Metoder utvecklades för att visualisera multivariata fördelningar av välpresterande parametrar baserade på de rangordnade värdena. Genom att projicera längs en gemensam dimension, kunde multivariata fördelningar av välpresterande parametrar hos modeller med varierande komplexitet jämföras med hjälp av det föreslagna visualiseringsverktyget. Det har alltså potentialen att bistå vid valet av en adekvat modellstruktur som tar hänsyn till osäkerheten i data. Dessa metoder möjliggjorde kvantifiering av observationsosäkerheter. Geometriska metoder har helt nyligen börjat användas inom hydrologin. I studien demonstrerades att de kan användas för att identifiera robusta parametervärdesuppsättningar och några av metodernas potentiella användningsområden belystes.