Innovative method for automatic shape generation and 3D printing of reduced-scale models of ultra-thin concrete shells

A research and development project has been conducted aiming to design and produce ultra-thin concrete shells. In this paper, the first part of the project is described, consisting of an innovative method for shape generation and the consequent production of reduced-scale models of the selected geometries. First, the shape generation is explained, consisting of a geometrically nonlinear analysis based on the Finite Element Method (FEM) to define the antifunicular of the shell’s deadweight. Next, the scale model production is described, consisting of 3D printing, specifically developed to evaluate the aesthetics and visual impact, as well as to study the aerodynamic behaviour of the concrete shells in a wind tunnel. The goals and constraints of the method are identified and a step-by-step guidelines presented, aiming to be used as a reference in future studies. The printed geometry is validated by high-resolution assessment achieved by photogrammetry. The results are compared with the geometry computed through geometric nonlinear finite-element-based analysis, and no significant differences are recorded. The method is revealed to be an important tool for automatic shape generation and building scale models of shells. The latter enables the performing of wind tunnel tests to obtain pressure coefficients, essential for structural analysis of this type of structures3

Behavior of reinforced concrete frame with masonry infill wall subjected to vertical load

The effectiveness of masonry infill wall on behavior of a Reinforced Concrete (RC) frame subjected to a column failure is studied experimentally. For this reason, one full scale RC frame designed according to Eurocode is statically tested to investigate the behavior of the frame with and without masonry infill wall. The obtained results show that infill wall can significantly increase the load carrying capacity of RC frame and thus serve as an important robustness reserve in the case of unpredictable extreme events (i.e. local impact, blast or earthquake). A photogrammetry analysis is carried out to study the behavior of the structure. Results give valuable information about the alternative load path, transfer of the applied load to the column and beams, and interaction forces between RC frame and infill wall. At the end, the experimental program is simulated by the OpenSees software to study the behavior of the frame. After having demonstrated that this model can predict the load deflection with good accuracy, a parametric study is conducted to evaluate the effect of the percentage of longitudinal reinforcement ratio of beams and columns on the load carrying capacity of the infilled RC frame

Special Issue: “Concrete Structures: Present and Future Trends”

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Monitorização do Estado de Conservação de Estruturas de Betão por Processamento de Imagem e Análise Multi-Espectral

Tese de doutoramento em Engenharia Civil, na especialidade de Mecânica das Estruturas e dos Materiais, apresentada à Faculdade de Ciências e Tecnologia da Universidade de CoimbraO betão é, a partir da segunda metade no século XX, o material de construção mais utilizado à escala do planeta. A degradação das estruturas de betão tem diversas causas. O programa de manutenção de determinadas infra-estruturas em betão inclui a realização de inspecções visuais periódicas para detecção de anomalias e, quando se justifica, a realização de ensaios não destrutivos para apoio ao diagnóstico e definição da intervenção mais adequada. Esta metodologia apresenta como principais inconvenientes a obtenção de uma avaliação por amostragem (e não global) do estado de conservação da estrutura, e a necessidade de realização de ensaios, calibrados de forma empírica, trabalhosos, demorados e sujeitos a erro humano. No âmbito desta tese, foi desenvolvido um método inovador tendo como principais objectivos monitorizar o comportamento de elementos estruturais de betão submetidos a carregamento, e detectar e caracterizar anomalias superficiais em estruturas de betão à vista. No primeiro caso, considerou-se relevante registar a evolução dos seguintes parâmetros: campo de deslocamentos, campo de deformações e padrão de fissuração. Na segunda situação, definiu-se como meta conseguir identificar e mapear automaticamente algumas anomalias visíveis a olho nu, como fissuras, manchas de humidade, sujidade, colonização biológica ou materiais de reparação. O método desenvolvido foi designado „Monitorização Automática do Estado de Conservação do Betão‟ – ACHM (Automatic Concrete Health Monitoring). Inclui a utilização de fotogrametria, processamento digital de imagem e análise multi-espectral de imagem, considerando o espectro visível e o infravermelho próximo, para avaliar a patologia do betão. O método incorpora vários módulos, a seguir indicados, os quais podem ser utilizados isoladamente ou em conjunto: (1) ‘Photo-Displacements’, permite determinar o campo de deslocamentos de estruturas sujeitas a carregamento; (2) ‘MCrack’ – Image Processing of Concrete Surfaces, possibilita a detecção, caracterização e análise de fissuras em superfícies de betão; (3) ‘Visual-DSC’ – Visualisation of Displacements, Strains and Cracks, permite a determinação de campos de deslocamentos e de deformações, assim como a medição da abertura de fissuras; (4) ‘SurfMap-DSC’ – Mapping Displacements, Strains and Cracks in Concrete Surfaces, integra os dois últimos módulos, agrupando as potencialidades de ambos; (5) ‘SurfCrete’ – Multi-Spectral Image Analysis of Concrete Surfaces, permite detectar, analisar e medir áreas com colonização biológica, fissuras e materiais de reparação em superfícies de betão; e (6) ‘Aesthetic-CCS’ – Aesthetic Characterization of Concrete Surfaces, visa apoiar a definição do tipo mais adequado de reparação localizada (patch repair) do ponto de vista estético (i.e., em termos de cor e de textura). O ACHM demonstrou ser capaz de caracterizar, de forma automática e contínua (monitorização), a patologia do betão. Assim, o ACHM é uma ferramenta importante: (1) no apoio à componente laboratorial de trabalhos de investigação científica na área do betão estrutural; (2) na inspecção e diagnóstico de estruturas de betão; e (3) na definição de técnicas de restauro de „Património em Betão‟.Concrete is, from the second half of the XXth century, the most widely used building material in the world. The degradation of concrete structures has several causes. The maintenance program for concrete infrastructure includes periodic visual inspections to detect anomalies and, when justified, non destructive tests are performed to support the diagnosis and defining the most appropriate interventions. The main disadvantages of this methodology are the following: is obtained an evaluation per sample (not global) from the state of conservation of the structure; and requires tests, usually empirically calibrated, time consuming and subject to human error. In the scope of this thesis, a new method was developed to monitor the behaviour of structural concrete elements subjected to load, and to detect and characterize surface damages in exposed concrete structures. In the first case, it was considered important to record the evolution of the following parameters: displacement field, strain field and cracking pattern. In the second situation, it was defined as a goal to automatically identify and map anomalies visible to human eye in concrete surfaces, such as, cracks, moisture and dirt stains, biological colonization and repair materials. The new method, named „Automatic Concrete Health Monitoring‟ – ACHM, includes the use of p hotogrammetry, digital image processing and multi-spectral image analysis, considering both visible and near infrared spectrum, to evaluate concrete pathology. The method incorporates several modules which can be used separately or together: (1) 'Photo-Displacements', developed to obtain the displacement fields of structures subject to load; (2) 'MCrack' – Image Processing of Concrete Surfaces, enables the detection, characterization and analysis of cracks in concrete surfaces: (3) 'Visual-DSC' – Visualisation of Displacements, Strains and Cracks, developed to determine displacements and deformations fields, as well as measure the cracks‟ width; (4) 'SurfMap-DSC' – Mapping Displacements, Strains and Cracks in Concrete Surfaces, integrates the last two modules, merging the potential of both; (5) 'SurfCrete' – Multi- Spectral Image Analysis of Concrete Surfaces, developed to detect, analyze and measure areas of biological colonization, cracks and repair materials in concrete surfaces; and (6) 'Aesthetic-CCS' – Aesthetic Characterization of Concrete Surfaces, aims to support the definition of appropriate patch repairs, in an aesthetic point of view (i.e., in terms of colour and texture). The ACHM confirmed the ability to characterize, automatic and continuously (monitoring), the concrete pathology. Thus, the ACHM is an important tool to: (1) support the laboratory tasks of scientific research projects in the field of structural concrete, (2) inspection and diagnosis of concrete structures, and (3) definition of restoration techniques and guidelines for 'Concrete Heritage'

Innovative Method for Automatic Shape Generation and 3D Printing of Reduced-Scale Models of Ultra-Thin Concrete Shells

A research and development project has been conducted aiming to design and produce ultra-thin concrete shells. In this paper, the first part of the project is described, consisting of an innovative method for shape generation and the consequent production of reduced-scale models of the selected geometries. First, the shape generation is explained, consisting of a geometrically nonlinear analysis based on the Finite Element Method (FEM) to define the antifunicular of the shell’s deadweight. Next, the scale model production is described, consisting of 3D printing, specifically developed to evaluate the aesthetics and visual impact, as well as to study the aerodynamic behaviour of the concrete shells in a wind tunnel. The goals and constraints of the method are identified and a step-by-step guidelines presented, aiming to be used as a reference in future studies. The printed geometry is validated by high-resolution assessment achieved by photogrammetry. The results are compared with the geometry computed through geometric nonlinear finite-element-based analysis, and no significant differences are recorded. The method is revealed to be an important tool for automatic shape generation and building scale models of shells. The latter enables the performing of wind tunnel tests to obtain pressure coefficients, essential for structural analysis of this type of structures

An Image-Based Framework for Measuring the Prestress Level in CFRP Laminates: Experimental Validation

Image-based methods have been applied to support structural monitoring, product and material testing, and quality control. Lately, deep learning for compute vision is the trend, requiring large and labelled datasets for training and validation, which is often difficult to obtain. The use of synthetic datasets is often applying for data augmentation in different fields. An architecture based on computer vision was proposed to measure strain during prestressing in CFRP laminates. The contact-free architecture was fed by synthetic image datasets and benchmarked for machine learning and deep learning algorithms. The use of these data for monitoring real applications will contribute towards spreading the new monitoring approach, increasing the quality control of the material and application procedure, as well as structural safety. In this paper, the best architecture was validated during experimental tests, to evaluate the performance in real applications from pre-trained synthetic data. The results demonstrate that the architecture implemented enables estimating intermediate strain values, i.e., within the range of training dataset values, but it does not allow for estimating strain values outside those range. The architecture allowed for estimating the strain in real images with an error ∼0.5%, higher than that obtained with synthetic images. Finally, it was not possible to estimate the strain in real cases from the training performed with the synthetic dataset

The Maintenance of Monuments as the Main Trigger to Negative Feelings in Tourists

Tourists’ perceptions of monuments influence their feelings about the country and the possibility of returning or recommending their visit to other tourists. TripAdvisor is one of the most popular websites for sharing travelling experiences and plays an important role when choosing a travel destination. But what are the factors that can provoke negative feelings in tourists? The maintenance of monuments is essential for their conservation; however, active maintenance can trigger negative feelings in tourists, compromising their connection with the cultural heritage of the country. This study reveals how some maintenance actions can influence tourists’ expectations regarding two relevant architectural monuments in the Iberian Peninsula by applying VADER (Valence Aware Dictionary for sEntiment Reasoning) to 13,000 TripAdvisor reviews written in the last decade and in three languages. Other variables, such as weather conditions and changes in climate, tourists’ country of origin and their style of travel, are evaluated to eliminate the possible mediating effects of these variables. This study reveals that the maintenance status of monuments seems to be the variable with the greatest impact on tourists’ perceptions and on their evaluations on TripAdvisor, propagating negative feelings towards the monument, from which it takes some time to recover

Design and Durability Assessment of Restoring Mortar for Concrete Heritage

Interventions in concrete heritage deal with challenges related to conservation, and must be performed from an integrated restoration perspective. In addition to the material technical performance, the aesthetic compatibility between the repair and the structure, in terms of colour and texture, needs to be ensured. Therefore, the characterisation of the restoration mortar concerning colour match and aging, and the mechanical and durability performances, is essential. In this article, the long-term behaviour of restoration mortar, previously designed and produced by the addition of pigments to white and grey cement-based reference mortar, is evaluated. The durability properties, colour change due to aging, and service life are estimated and analysed. An experimental program is performed to characterise the following properties: (i) water capillary absorption; (ii) accelerated carbonation; (iii) migration of chloride ions; (iv) electrical resistivity; and (v) shrinkage. The colour evolution, when exposed to carbonation, is measured through image processing. The obtained results allow the establishment of a correlation between durability and design parameters. Finally, service life considering deterioration due to steel corrosion is estimated, considering the carbonation resistance and the chloride diffusion values. It is concluded that the W/C ratio influences not only most of the characterised parameters, but also the type and content of the pigment. Furthermore, no colour variation due to carbonation is detected

Benchmarking for Strain Evaluation in CFRP Laminates Using Computer Vision: Machine Learning versus Deep Learning

The strengthening of concrete structures with laminates of Carbon-Fiber-Reinforced Polymers (CFRP) is a widely adopted technique. retained The application is more effective if pre-stressed CFRP laminates are adopted. The measurement of the strain level during the pre-stress application usually involves laborious and time-consuming applications of instrumentation. Thus, the development of expedited approaches to accurately measure the pre-stressed application in the laminates represents an important contribution to the field. This paper proposes and benchmarks contact-free architecture for measuring the strain level of CFRP laminate based on computer vision. The main objective is to provide a solution that might be economically feasible, automated, easy to use, and accurate. The architecture is fed by digitally deformed synthetic images, generated based on a low-resolution camera. The adopted methods range from traditional machine learning to deep learning. Furthermore, dropout and cross-validation methods for quantifying traditional machine learning algorithms and neural networks are used to efficiently provide uncertainty estimates. ResNet34 deep learning architecture provided the most accurate results, reaching a root mean square error (RMSE) of 0.057‰ for strain prediction. Finally, it is important to highlight that the architecture presented is contact-free, automatic, cost-effective, and measures directly on the laminate surfaces, which allows them to be widely used in the application of pre-stressed laminates