Vision-based Crack Identification on the Concrete Slab Surface using Fuzzy Reasoning Rules and Self-Organizing

Identifying cracks on the surface of concrete slab structure is important for structure stability maintenance. In order to avoid subjective visual inspection, it is necessary to develop an automated identification and measuring system by vision based method. Although there have been some intelligent computerized inspection methods, they are sensitive to noise due to the brightness contrast and objects such as forms and joints of certain size often falsely classified as cracks. In this paper, we propose a new fuzzy logic based image processing method that extracts cracks from concrete slab structure including small cracks that were often neglected as noise. We extract candidate crack areas by applying fuzzy method with three color channel values of concrete slab structure. Then further refinement processes are performed with Self Organizing Map algorithm and density based noise removal process to obtain basic crack characteristic attributes for further analysis. Experimental result verifies that the proposed method is sufficiently identified cracks with various sizes with high accuracy (97.3%) among 1319 ground truth cracks from 30 images

Deep Learning Approaches in Pavement Distress Identification: A Review

This paper presents a comprehensive review of recent advancements in image processing and deep learning techniques for pavement distress detection and classification, a critical aspect in modern pavement management systems. The conventional manual inspection process conducted by human experts is gradually being superseded by automated solutions, leveraging machine learning and deep learning algorithms to enhance efficiency and accuracy. The ability of these algorithms to discern patterns and make predictions based on extensive datasets has revolutionized the domain of pavement distress identification. The paper investigates the integration of unmanned aerial vehicles (UAVs) for data collection, offering unique advantages such as aerial perspectives and efficient coverage of large areas. By capturing high-resolution images, UAVs provide valuable data that can be processed using deep learning algorithms to detect and classify various pavement distresses effectively. While the primary focus is on 2D image processing, the paper also acknowledges the challenges associated with 3D images, such as sensor limitations and computational requirements. Understanding these challenges is crucial for further advancements in the field. The findings of this review significantly contribute to the evolution of pavement distress detection, fostering the development of efficient pavement management systems. As automated approaches continue to mature, the implementation of deep learning techniques holds great promise in ensuring safer and more durable road infrastructure for the benefit of society

Recent advances in intelligent-based structural health monitoring of civil structures

This survey paper deals with the structural health monitoring systems on the basis of methodologies involving intelligent techniques. The intelligent techniques are the most popular tools for damage identification in terms of high accuracy, reliable nature and the involvement of low cost. In this critical survey, a thorough analysis of various intelligent techniques is carried out considering the cases involved in civil structures. The importance and utilization of various intelligent tools to be mention as the concept of fuzzy logic, the technique of genetic algorithm, the methodology of neural network techniques, as well as the approaches of hybrid methods for the monitoring of the structural health of civil structures are illustrated in a sequential manner

Earthquake Engineering

The book Earthquake Engineering - From Engineering Seismology to Optimal Seismic Design of Engineering Structures contains fifteen chapters written by researchers and experts in the fields of earthquake and structural engineering. This book provides the state-of-the-art on recent progress in the field of seimology, earthquake engineering and structural engineering. The book should be useful to graduate students, researchers and practicing structural engineers. It deals with seismicity, seismic hazard assessment and system oriented emergency response for abrupt earthquake disaster, the nature and the components of strong ground motions and several other interesting topics, such as dam-induced earthquakes, seismic stability of slopes and landslides. The book also tackles the dynamic response of underground pipes to blast loads, the optimal seismic design of RC multi-storey buildings, the finite-element analysis of cable-stayed bridges under strong ground motions and the acute psychiatric trauma intervention due to earthquakes

Development of a machine learning based methodology for bridge health monitoring

Tesi en modalitat de compendi de publicacionsIn recent years the scientific community has been developing new techniques in structural health monitoring (SHM) to identify the damages in civil structures specially in bridges. The bridge health monitoring (BHM) systems serve to reduce overall life-cycle maintenance costs for bridges, as their main objective is to prevent catastrophic failures and damages. In the BHM using dynamic data, there are several problems related to the post-processing of the vibration signals such as: (i) when the modal-based dynamic features like natural frequencies, modes shape and damping are used, they present a limitation in relation to damage location, since they are based on a global response of the structure; (ii) presence of noise in the measurement of vibration responses; (iii) inadequate use of existing algorithms for damage feature extraction because of neglecting the non-linearity and non-stationarity of the recorded signals; (iv) environmental and operational conditions can also generate false damage detections in bridges; (v) the drawbacks of traditional algorithms for processing large amounts of data obtained from the BHM. This thesis proposes new vibration-based parameters and methods with focus on damage detection, localization and quantification, considering a mixed robust methodology that includes signal processing and machine learning methods to solve the identified problems. The increasing volume of bridge monitoring data makes it interesting to study the ability of advanced tools and systems to extract useful information from dynamic and static variables. In the field of Machine Learning (ML) and Artificial Intelligence (AI), powerful algorithms have been developed to face problems where the amount of data is much larger (big data). The possibilities of ML techniques (unsupervised algorithms) were analyzed here in bridges taking into account both operational and environmental conditions. A critical literature review was performed and a deep study of the accuracy and performance of a set of algorithms for detecting damage in three real bridges and one numerical model. In the literature review inherent to the vibration-based damage detection, several state-of-the-art methods have been studied that do not consider the nature of the data and the characteristics of the applied excitation (possible non-linearity, non-stationarity, presence or absence of environmental and/or operational effects) and the noise level of the sensors. Besides, most research uses modal-based damage characteristics that have some limitations. A poor data normalization is performed by the majority of methods and both operational and environmental variability is not properly accounted for. Likewise, the huge amount of data recorded requires automatic procedures with proven capacity to reduce the possibility of false alarms. On the other hand, many investigations have limitations since only numerical or laboratory cases are studied. Therefore, a methodology is proposed by the combination of several algorithms to avoid them. The conclusions show a robust methodology based on ML algorithms capable to detect, localize and quantify damage. It allows the engineers to verify bridges and anticipate significant structural damage when occurs. Moreover, the proposed non-modal parameters show their feasibility as damage features using ambient and forced vibrations. Hilbert-Huang Transform (HHT) in conjunction with Marginal Hilbert Spectrum and Instantaneous Phase Difference shows a great capability to analyze the nonlinear and nonstationary response signals for damage identification under operational conditions. The proposed strategy combines algorithms for signal processing (ICEEMDAN and HHT) and ML (k-means) to conduct damage detection and localization in bridges by using the traffic-induced vibration data in real-time operation.En los últimos años la comunidad científica ha desarrollado nuevas técnicas en monitoreo de salud estructural (SHM) para identificar los daños en estructuras civiles especialmente en puentes. Los sistemas de monitoreo de puentes (BHM) sirven para reducir los costos generales de mantenimiento del ciclo de vida, ya que su principal objetivo es prevenir daños y fallas catastróficas. En el BHM que utiliza datos dinámicos, existen varios problemas relacionados con el procesamiento posterior de las señales de vibración, tales como: (i) cuando se utilizan características dinámicas modales como frecuencias naturales, formas de modos y amortiguamiento, presentan una limitación en relación con la localización del daño, ya que se basan en una respuesta global de la estructura; (ii) presencia de ruido en la medición de las respuestas de vibración; (iii) uso inadecuado de los algoritmos existentes para la extracción de características de daño debido a la no linealidad y la no estacionariedad de las señales registradas; (iv) las condiciones ambientales y operativas también pueden generar falsas detecciones de daños en los puentes; (v) los inconvenientes de los algoritmos tradicionales para procesar grandes cantidades de datos obtenidos del BHM. Esta tesis propone nuevos parámetros y métodos basados en vibraciones con enfoque en la detección, localización y cuantificación de daños, considerando una metodología robusta que incluye métodos de procesamiento de señales y aprendizaje automático. El creciente volumen de datos de monitoreo de puentes hace que sea interesante estudiar la capacidad de herramientas y sistemas avanzados para extraer información útil de variables dinámicas y estáticas. En el campo del Machine Learning (ML) y la Inteligencia Artificial (IA) se han desarrollado potentes algoritmos para afrontar problemas donde la cantidad de datos es mucho mayor (big data). Aquí se analizaron las posibilidades de las técnicas ML (algoritmos no supervisados) teniendo en cuenta tanto las condiciones operativas como ambientales. Se realizó una revisión crítica de la literatura y se llevó a cabo un estudio profundo de la precisión y el rendimiento de un conjunto de algoritmos para la detección de daños en tres puentes reales y un modelo numérico. En la revisión de literatura se han estudiado varios métodos que no consideran la naturaleza de los datos y las características de la excitación aplicada (posible no linealidad, no estacionariedad, presencia o ausencia de efectos ambientales y/u operativos) y el nivel de ruido de los sensores. Además, la mayoría de las investigaciones utilizan características de daño modales que tienen algunas limitaciones. Estos métodos realizan una normalización deficiente de los datos y no se tiene en cuenta la variabilidad operativa y ambiental. Asimismo, la gran cantidad de datos registrados requiere de procedimientos automáticos para reducir la posibilidad de falsas alarmas. Por otro lado, muchas investigaciones tienen limitaciones ya que solo se estudian casos numéricos o de laboratorio. Por ello, se propone una metodología mediante la combinación de varios algoritmos. Las conclusiones muestran una metodología robusta basada en algoritmos de ML capaces de detectar, localizar y cuantificar daños. Permite a los ingenieros verificar puentes y anticipar daños estructurales. Además, los parámetros no modales propuestos muestran su viabilidad como características de daño utilizando vibraciones ambientales y forzadas. La Transformada de Hilbert-Huang (HHT) junto con el Espectro Marginal de Hilbert y la Diferencia de Fase Instantánea muestran una gran capacidad para analizar las señales de respuesta no lineales y no estacionarias para la identificación de daños en condiciones operativas. La estrategia propuesta combina algoritmos para el procesamiento de señales (ICEEMDAN y HHT) y ML (k-means) para detectar y localizar daños en puentes mediante el uso de datos de vibraciones inducidas por el tráfico en tiempo real.Postprint (published version

Internationales Kolloquium über Anwendungen der Informatik und Mathematik in Architektur und Bauwesen : 04. bis 06.07. 2012, Bauhaus-Universität Weimar

The 19th International Conference on the Applications of Computer Science and Mathematics in Architecture and Civil Engineering will be held at the Bauhaus University Weimar from 4th till 6th July 2012. Architects, computer scientists, mathematicians, and engineers from all over the world will meet in Weimar for an interdisciplinary exchange of experiences, to report on their results in research, development and practice and to discuss. The conference covers a broad range of research areas: numerical analysis, function theoretic methods, partial differential equations, continuum mechanics, engineering applications, coupled problems, computer sciences, and related topics. Several plenary lectures in aforementioned areas will take place during the conference. We invite architects, engineers, designers, computer scientists, mathematicians, planners, project managers, and software developers from business, science and research to participate in the conference

Image Restoration

This book represents a sample of recent contributions of researchers all around the world in the field of image restoration. The book consists of 15 chapters organized in three main sections (Theory, Applications, Interdisciplinarity). Topics cover some different aspects of the theory of image restoration, but this book is also an occasion to highlight some new topics of research related to the emergence of some original imaging devices. From this arise some real challenging problems related to image reconstruction/restoration that open the way to some new fundamental scientific questions closely related with the world we interact with

Atas das 8as Jornadas de Segurança aos Incêndios Urbanos e as 3as Jornadas de Proteção Civil (8JORNINC-3JORPROCIV)

Este livro de ATAS contém os artigos apresentados às 8as Jornadas de Segurança aos Incêndios Urbanos e às 3as Jornadas de Proteção Civil (8JORNINC-3JORPROCIV), que decorreram no Porto, Portugal. Na presente edição das 8JORNINC-3JORPROCIV foram submetidos 50 trabalhos, tendo sido aceites 42. Os trabalhos foram distribuídos em 8 sessões paralelas temáticas, em adição a 2 sessões plenárias, apresentados no dia 2 de junho de 2023. O evento foi iniciado e promovido em Portugal, sob a organização do professor Doutor João Paulo Rodrigues, com as 1as Jornadas de Segurança aos Incêndios Urbanos, em 2005, na Universidade de Coimbra, bem como nos anos seguintes, as 2as Jornadas de Segurança aos Incêndios Urbanos em 2011 e as 3as Jornadas de Segurança aos Incêndios Urbanos em 2013, também na Universidade de Coimbra. As edições seguintes aconteceram em diferentes locais: as 4as Jornadas de Segurança aos Incêndios Urbanos em 2014, no Instituto Politécnico de Bragança; as 5as Jornadas de Segurança aos Incêndios Urbanos em 2016, no Laboratório Nacional de Engenharia Civil em Lisboa; as 6as Jornadas de Segurança aos Incêndios Urbanos e 1as Jornadas de Proteção Civil em 2018, na Universidade de Coimbra; e as 7as Jornadas de Segurança aos Incêndios Urbanos e 2as Jornadas de Proteção Civil, em 2021, no Instituto Politécnico de Castelo Branco. As 8JORNINC apresentam grande importância num contexto atual de vários e graves incêndios urbanos, florestais e de interface em Portugal. A pertinência do tema da segurança na prevenção e no combate a incêndios, quer pelas consequências emergentes deste tipo de acidentes, quer pela necessidade de redução das ocorrências, do número de vítimas mortais, feridos, prejuízos materiais, patrimoniais, ambientais e sociais, leva a que as Jornadas se destinem a um leque alargado de profissionais e público em geral. As 3JORPROCIV têm como objetivo promover conhecimentos nas áreas da prevenção civil, de riscos e planos de emergência. Pretendem assim, contribuir para a atualização dos conhecimentos técnicos e científicos da segurança e proteção civil, no âmbito do planeamento e prevenção perante cenários de crise e emergência. As Jornadas de Proteção Civil permitem partilhar um leque de conhecimentos multidisciplinares suscetíveis de impulsionar uma intervenção por parte de técnicos, especialistas e dos agentes da proteção civil. A visão interdisciplinar e integradora dos problemas e desafios que a proteção civil apresenta será refletida na prevenção e mitigação dos riscos inerentes a uma situação de acidente. Os trabalhos apresentados nesta edição, permitirão o avanço das 8JORNINC-3JORPROCIV, através da divulgação dos recentes desenvolvimentos e do conhecimento nos domínios da segurança ao incêndio e proteção civil. Por fim, a comissão organizadora das 8JORNINC-3JORPROCIV gostaria de agradecer: - o apoio dos patrocinadores e das instituições do Sistema Científico e Tecnológico; - a todos os autores que partilharam os seus excelentes trabalhos; - e aos elementos da Comissão Científica que auxiliaram no processo de revisão. Todos em conjunto, tornaram possível a realização destas Jornadas.info:eu-repo/semantics/publishedVersio

Análise do risco de incêndio edifício: remodelado em Castelo Branco

As notícias dão-nos com frequência informação de incêndios urbanos, por vezes em prédios antigos, alguns devolutos e que se alastram aos prédios contíguos, levando a grandes prejuízos, quer materiais, quer em perdas de vidas humanas, ou mesmo danos em património cultural. É, por isso, essencial identificar os riscos de incêndio nos edifícios. Assim, existem metodologias próprias que nos permitem conhecer e controlar os problemas detetados, de modo a conseguir assegurar uma segurança contra incêndio em edifícios, o mais eficaz possível. Nesse sentido, o presente trabalho pretende avaliar a segurança de um edifício, segundo métodos de análise de risco como o método ARICA, o método de Gretener e o método de FRAME e também segundo o Regime Jurídico de Segurança Contra Incêndio em Edifícios.info:eu-repo/semantics/publishedVersio