Earthquakes can severely damage building structural and nonstructural systems and components, including active and passive fire protection and egress systems. If the occurrence of such damage is not anticipated at the design stage, the impact of a post-earthquake fire could be significant, as building and fire protection systems may not perform as expected. Unfortunately, even though both the seismic and fire engineering communities utilize performance-based approaches for designing well-performing and resilient buildings under earthquake and fire hazards respectively, each discipline carries out their associated building performance analyses independently. As a result, fire protection engineers have little guidance as to how to estimate structural and nonstructural building systems and component damage as inputs to help them develop post-earthquake building fire scenarios. To help bridge this gap, a conceptual framework is developed that illustrates how performance-based approaches for earthquake and fire engineering analysis and design can become more integrated for the development of post-earthquake fire scenarios. Using a fictional building in an earthquake prone area as an example, the conceptual framework is implemented to show (a) how earthquake-induced damage to building fire protection systems could be estimated using an earthquake performance assessment tool, (b) how the damage estimates might be translated into physical damage parameters in a way that is meaningful for developing post-earthquake building fire scenarios, (c) how the damage states might be implemented in terms of fire and egress modeling input parameters, and (d) how this information could be used to and compare post-earthquake building fire safety performance to a normal(undamaged) building fire conditions.

京都大学 / 新制・論文博士 / 博士(工学) / 乙第13406号 / 論工博第4192号 / 新制||工||1762(附属図書館) / (主査)教授 松島 信一, 教授 竹脇 出, 教授 牧 紀男 / 学位規則第4条第2項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

Natural disasters strike anywhere, disrupting local communication and transportation infrastructure, making the process of assessing specific local damage difficult, dangerous, and slow. The goal of Building Damage Assessment (BDA) is to quickly and accurately estimate the location, cause, and severity of the damage to maximize the efficiency of rescuers and saved lives. In current machine learning BDA solutions, attention operators are the most recent innovations adopted by researchers to increase generalizability and overall performances of Convolutional Neural Networks for the BDA task. However, the latter, nowadays exploit attention operators tailored to the specific task and specific neural network architecture, leading them to be hard to apply to other scenarios. In our research, we want to contribute to the BDA literature while also addressing this limitation. We propose Global Voxel Transformer Operators (GVTOs): flexible attention-operators originally proposed for Augmented Microscopy that can replace up-sampling, down-sampling, and size-preserving convolutions within either a U-Net or a general CNN architecture without any limitation. Dissimilar to local operators, like convolutions, GVTOs can aggregate global information and have input-specific weights during inference time, improving generalizability performance, as already proved by recent literature. We applied GVTOs on a state-of-the-art BDA model and named it GVT-BDNet. We trained and evaluated our proposal neural network on the xBD dataset; the largest and most complete dataset for BDA. We compared GVT-BDNet performance with the baseline architecture (BDNet) and observed that the former improves damaged buildings segmentation by a factor of 0.11. Moreover, GVT-BDNet achieves state-of-the-art performance on a 10% split of the xBD training dataset and on the xBD test dataset with an overall F1- score of 0.80 and 0.79, respectively. To evaluate the architecture consistency, we have also tested BDNet’s and GVT-BDNet’s generalizability performance on another segmentation task: Tree & Shadow segmentation. Results showed that both models achieved overall good performances, scoring an F1-score of 0.79 and 0.785, respectively. / Naturkatastrofer sker överallt, stör lokal kommunikations- och transportinfrastruktur, vilket gör bedömningsprocessen av specifika lokala skador svår, farlig och långsam. Målet med Building Damage Assessment (BDA) är att snabbt och precist uppskatta platsen, orsaken och allvarligheten av skadorna för att maximera effektiviteten av räddare och räddade liv. Nuvarande BDA-lösningar använder Convolutional Neural Network (CNN) och ad-hoc Attention Operators för att förbättra generaliseringsprestanda. Nyligen föreslagna attention operators är dock specifikt skräddarsydda för uppgiften och kan sakna flexibilitet för andra scenarier eller neural nätverksarkitektur. I vår forskning bidrar vi till BDA -litteraturen genom att föreslå Global Voxel Transformer Operators (GVTO): flexibla attention operators som kan appliceras på en CNN -arkitektur utan att vara bundna till en viss uppgift. Nyare litteratur visar dessutom att de kan öka utvinningen av global information och därmed generaliseringsprestanda. Vi tillämpade GVTO på en toppmodern CNN-modell för BDA. GVTO: er förbättrade skadessegmenteringsprestandan med en faktor av 0,11. Dessutom förbättrade de den senaste tekniken för xBD-testdatauppsättningen och nådde toppmodern prestanda på en 10% delning av xBD-träningsdatauppsättningen. Vi har också utvärderat generaliserbarheten av det föreslagna neurala nätverket på en annan segmenteringsuppgift (Tree Shadow segmentering), vilket uppnådde över lag bra prestationer.

Made available in DSpace on 2015-05-14T12:09:11Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 2630968 bytes, checksum: 7ccfc1bc157dd97f0df36f85ac84db1a (MD5) Previous issue date: 2008-06-27 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Currently, urban centres are more and more subject to vibration induced by traffic, because of its increasing volume. Among the vibration effects are damage to buildings. In this study, the effects of the vibration produced by suburban trains in buildings was investigated, by taking a railway line with mixed passenger and cargo traffic as a (typical) case study. Measurements were taken, aiming to identify the vibration levels in buildings adjacent to the railway line and which presented visible cracks in their surfaces. The results obtained revealed that vibration levels produced by passenger trains were below the limits which could potentially cause damage. However, simulations performed in order to estimate the vibration levels induced by freight trains evidenced the occurrence of levels greater than those acceptable for the type of buildings investigated. / Atualmente, os centros urbanos estão mais sujeitos à vibração induzida por tráfego, em virtude do aumento do volume deste. Dentre os efeitos da vibração, estão os danos em edificações. Neste estudo, foram investigados os efeitos da vibração produzida pelo tráfego de trens suburbanos em edificações, tomandose como caso (típico) de estudo uma linha férrea com tráfego misto de passageiros e de carga. Medições foram realizadas, visando identificar os níveis de vibração em edificações adjacentes à linha férrea e que apresentavam fissuração visível em suas fachadas. Os resultados obtidos mostraram que os níveis de vibração produzidos pelo tráfego de trens de passageiros apresentaram-se abaixo dos limites que potencialmente causariam dano. Todavia, simulações realizadas para estimar os níveis de vibração induzidos por trens de carga evidenciaram a ocorrência de níveis superiores aos aceitáveis para o tipo de edificações investigadas.

Floods are natural phenomena which are a threat to human settlements. Flooding can result in costly repairs to buildings, loss of business and, in some cases, loss of life. The forecasts for climate change show a further increased risk of flooding in future years. Accordingly, the flooding of residential property has been observed as on the rise in the UK. It is difficult to prevent floods from occurring, but the effects of flooding can be managed in an attempt to reduce risks and costs of repair. This can be achieved through ensuring a good understanding of the problem, and thereby establishing good management systems which are capable of dealing with all aspects of the flood. The use of an intelligent system for assessment and remediation of buildings subjected to flooding damage can facilitate the management of this problem. Such a system can provide guidance for the assessment of vulnerability and the repair of flood damaged residential buildings; this could save time and money through the use of the advantages and benefits offered by knowledge base systems. A prototype knowledge base system has been developed in this research. The system comprises three subsystems: degree of vulnerability assessment subsystem; remediation options subsystem; and foundation damage assessment subsystem. The vulnerability assessment subsystem is used to calculate the degree of vulnerability, which will then be used by the remediation options subsystem to select remediation options strategy. The vulnerability assessment subsystem can subsequently be used to calculate the degree to which the building is vulnerable to damage by flooding even if it is not flooded. Remediation options subsystem recommended two strategy options: either ordinary remediation options in the case of vulnerability being low or, alternatively, resilience remediation options in the case of vulnerability being high. The foundation damage assessment subsystem is working alone and is used to assess the damage caused by flooding to the building s foundation, and to thereby recommend a repair option based on the damage caused and foundation type. The system has been developed based on the knowledge acquired from different sources and methods, including survey questionnaires, documents, interviews, and workshops. The system is then evaluated by experts and professionals in the industry. The developed system makes a contribution in the management and standardisation of residential building flooded damage and repair.

Tunnelling in urban areas causes short-term and long-term ground movements under existing buildings. Finite element analysis provides a useful option for assessing the likely extent of damage induced in these buildings. Although finite element analysis is suggested to be used in the later phases of the building damage assessment procedures employed in practice, only the effect of short-term ground movements is typically considered and there are no detailed guidelines on the specification and complexity of the modelling. This thesis addresses the tunnel-soil-building interaction problem and the effect of long-term consolidation, as well as demonstrating the application of 3D finite element analysis with appropriate simplifications for practical assessment purposes. Finite element models are developed to quantify the effect of shallow tunnelling on an example masonry building founded on strip footings, considering both single- and twin-tunnel scenarios in a typical London soil profile. Total stress and effective stress analyses are adopted with specific modelling procedures to focus on the short-term and long-term response respectively. The analyses use a non-linear model for the masonry, and allow slippage and gapping at the soil-footing interface. Two advanced constitutive models for the soil (the extended Mohr-Coulomb model and the modified two-surface kinematic hardening model) are implemented with customized stress update schemes. The finite element results present the interaction between the soil and the building by comparing with the greenfield ground response. The horizontal coupling between the foundation and the ground is shown to be relatively weak. The dominant deformation mode of the building varies with the tunnel configuration (i.e. single or twin tunnels) and the tunnel eccentricity. Strain localization is found around the explicitly modelled window and door openings. The long-term consolidation is sensitive to the permeability of the tunnel lining. The building response to long-term ground movements is further affected by the tunnel-tunnel interaction in the case of twin-tunnel configuration. Performing 3D analysis of a single facade and foundation provides useful damage predictions, without the need to model a complete building. The proposed result processing methods such as characteristic strain and damage bar chart are practical tools for assessment. The study highlights some limitations of the elastic beam assessment method, which is often adopted in the early phase of the damage assessment process.

京都大学 / 新制・課程博士 / 博士(工学) / 甲第23188号 / 工博第4832号 / 新制||工||1755(附属図書館) / 京都大学大学院工学研究科建築学専攻 / (主査)教授 松島 信一, 教授 竹脇 出, 教授 林 康裕 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

Current disaster response strategies are based on damage assessments carried out on the ground, which can be dangerous following a ä destructive event. Damage assessments can also be performed remotely using satellite imagery, but are usually carried out through visual interpretation, which can take a lot of time. This thesis explored a way of using artificial intelligence to automate remote damage assessment. We implemented a dual-task U-Net deep learning model, trained it with the xBD dataset for assessing building damage, and applied the model to pre- and post-event very high resolution satellite imagery of the February 6, 2023 earthquakes in Turkey. The results were compared to damage maps produced using a traditional object based method by calculating the F1 scores associated with the outputs of each method and ground truth data that we compiled. The study areas were parts of the two cities Kahramanmaraş and Antakya. The deep learning model almost only correctly identified undamaged buildings, achieving F1 scores of 0.95 during training as well as 0.93 and 0.83 in the damage assessments of Kahramanmaras and Antakya, respectively. For the other damage classes, the best result was the classification of destroyed buildings, both in training and in the study areas, with a F1-score of 0.45 in training and 0.16 in Kahramanmaraş. The deep learning model performed similarly to the object based method. Although the thesis did not yield good damage maps in the areas of interest, it had many limitations, and there is still a lot of potential for deep learning models to be useful in building damage assessment.

L’objectif de cette thèse est, d’une part, de proposer une amélioration des méthodes d’estimation des cuvettes d’affaissement et des méthodes d’évaluation des dommages susceptibles de se produire sous leurs effets et de l’autre, de développer des outils basés sur ces méthodes pour étudier les affaissements et les dommages sur des cas pratiques. L’étude de l'influence de la topographie sur les cuvettes d'affaissement dans des conditions d’exploitation simplifiées grâce à des modèles numériques avec des profondeurs d'exploitation et des pentes du sol variables a permis de proposer une nouvelle fonction d’influence basée sur une densité de probabilité normale asymétrique lorsque la surface du sol est non-plane. Une modélisation simplifiée des habitations en maçonnerie sous la forme de deux modèles de structures bidimensionnels croisés, alignés avec les axes d’inertie de la structure étudiée et dans lesquels la méthode des déplacements est mise en œuvre pour calculer les efforts internes et les déformations sous l’effet de déplacements imposées des fondations. Ces modèles simplifiés dont les caractéristiques géométriques et mécaniques sont définis pour chaque type de bâtiment étudié permettent d’estimer les efforts appliqués à chaque bâtiment d’une ville exposée à un affaissement de terrain et de fournir de nouveaux critères d’évaluation des dommages prenant en compte davantage d’informations que les méthodes habituelles. Une estimation des dommages dans la ville de Joeuf sur la base des nouvelles méthodes proposées, tant pour le calcul de l’affaissement que pour l’estimation des dommages, a été réalisée / The objective of this thesis is to improve the methods of subsidence computation and building damage evaluation, and to develop some tools based on these methods to study the mining subsidence and building damage cases in Lorraine. By investigating the topography influence on subsidence under simplified mining conditions, and using numerical models with varying mining depths and ground surface angles, a new influence function method, which is based on a probability density function of a skew normal distribution, to simulate the element subsidence, was firstly developed and can be used to compute the mining subsidence caused by the excavation under non-flat surface. Then, plane framed structural models were chosen to study the mechanical behavior of 3D buildings. For each building, two plane models located in the vertical sections passing through the principle inertia axes of the building’s projective polygon were considered. Their geometry and mechanical characteristics were chosen according to the construction type and used materials of the building under consideration. Then, by using the matrix displacement method with some modifications, the internal forces and displacements for the entire structure could be computed. The achieved internal forces could then be compared to damage grade criteria to determine the extent of building damage.Finally, by using the improved methods of subsidence computation and building damage evaluation, a real case application was performed in Joeuf city (France). The subsidence was computed and applied to the defined structural models as support displacements, and then the damage extents of the buildings in Joeuf were predicted

Die vorliegende Arbeit beschäftigt sich mit der Entwicklung einer Methode zur Wertermittlung von bergbaubeeinflussten Immobilien, welche die wesentlichen Bergbaurisiken berücksichtigt. Die Untersuchungen ergaben, dass das Risiko von Investitionen in bergbaubeeinflusste Immobilien durchschnittlich dreimal größer ist als das Risiko von Investitionen in Immobilien ohne Bergbaueinflüsse. Für die Ermittlung des Risikos kamen eine immobilien- und eine bergbauspezifische Komponente zum Einsatz. Letztere umfasst Prognosen zu potentiellen Oberflächenvernässungen, zur Sickerwassergefahr und zu bergbaubedingten Wertminderungen von Gebäuden unter besonderer Berücksichtigung der Gebäudeempfindlichkeit gegenüber bergbauinduzierten Bodenbewegungen. Für die Wertermittlung von Immobilien kam das Discounted Cash Flow-Verfahren zur Anwendung. Der Diskontfaktor wurde mit Hilfe eines speziell entwickelten Fuzzy-Modells bestimmt. Die erstellte Bewertungsmethode kann als relevante Entscheidungshilfe für Investitionen in Bergbaugebiete dienen.