970 resultados para Structural assessment


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La idea básica de detección de defectos basada en vibraciones en Monitorización de la Salud Estructural (SHM), es que el defecto altera las propiedades de rigidez, masa o disipación de energía de un sistema, el cual, altera la respuesta dinámica del mismo. Dentro del contexto de reconocimiento de patrones, esta tesis presenta una metodología híbrida de razonamiento para evaluar los defectos en las estructuras, combinando el uso de un modelo de la estructura y/o experimentos previos con el esquema de razonamiento basado en el conocimiento para evaluar si el defecto está presente, su gravedad y su localización. La metodología involucra algunos elementos relacionados con análisis de vibraciones, matemáticas (wavelets, control de procesos estadístico), análisis y procesamiento de señales y/o patrones (razonamiento basado en casos, redes auto-organizativas), estructuras inteligentes y detección de defectos. Las técnicas son validadas numérica y experimentalmente considerando corrosión, pérdida de masa, acumulación de masa e impactos. Las estructuras usadas durante este trabajo son: una estructura tipo cercha voladiza, una viga de aluminio, dos secciones de tubería y una parte del ala de un avión comercial.

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Two-dimensional (2D) crystallisation of Membrane proteins reconstitutes them into their native environment, the lipid bilayer. Electron crystallography allows the structural analysis of these regular protein–lipid arrays up to atomic resolution. The crystal quality depends on the protein purity, ist stability and on the crystallisation conditions. The basics of 2D crystallisation and different recent advances are reviewed and electron crystallography approaches summarised. Progress in 2D crystallisation, sample preparation, image detectors and automation of the data acquisition and processing pipeline makes 2D electron crystallography particularly attractive for the structural analysis of membrane proteins that are too small for single-particle analyses and too unstable to form three-dimensional (3D) crystals.

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We propose a general mean field model of ligand-protein interactions to determine the thermodynamic equilibrium of a system at finite temperature. The method is employed in structural assessments of two human immuno-deficiency virus type 1 protease complexes where the gross effects of protein flexibility are incorporated by utilizing a data base of crystal structures. Analysis of the energy spectra for these complexes has revealed that structural and thermo-dynamic aspects of molecular recognition can be rationalized on the basis of the extent of frustration in the binding energy landscape. In particular, the relationship between receptor-specific binding of these ligands to human immunodeficiency virus type 1 protease and a minimal frustration principle is analyzed.

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In this paper, we describe how the pathfinder algorithm converts relatedness ratings of concept pairs to concept maps; we also present how this algorithm has been used to develop the Concept Maps for Learning website (www.conceptmapsforlearning.com) based on the principles of effective formative assessment. The pathfinder networks, one of the network representation tools, claim to help more students memorize and recall the relations between concepts than spatial representation tools (such as Multi- Dimensional Scaling). Therefore, the pathfinder networks have been used in various studies on knowledge structures, including identifying students’ misconceptions. To accomplish this, each student’s knowledge map and the expert knowledge map are compared via the pathfinder software, and the differences between these maps are highlighted. After misconceptions are identified, the pathfinder software fails to provide any feedback on these misconceptions. To overcome this weakness, we have been developing a mobile-based concept mapping tool providing visual, textual and remedial feedback (ex. videos, website links and applets) on the concept relations. This information is then placed on the expert concept map, but not on the student’s concept map. Additionally, students are asked to note what they understand from given feedback, and given the opportunity to revise their knowledge maps after receiving various types of feedback.

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Structural damage detection using measured dynamic data for pattern recognition is a promising approach. These pattern recognition techniques utilize artificial neural networks and genetic algorithm to match pattern features. In this study, an artificial neural network–based damage detection method using frequency response functions is presented, which can effectively detect nonlinear damages for a given level of excitation. The main objective of this article is to present a feasible method for structural vibration–based health monitoring, which reduces the dimension of the initial frequency response function data and transforms it into new damage indices and employs artificial neural network method for detecting different levels of nonlinearity using recognized damage patterns from the proposed algorithm. Experimental data of the three-story bookshelf structure at Los Alamos National Laboratory are used to validate the proposed method. Results showed that the levels of nonlinear damages can be identified precisely by the developed artificial neural networks. Moreover, it is identified that artificial neural networks trained with summation frequency response functions give higher precise damage detection results compared to the accuracy of artificial neural networks trained with individual frequency response functions. The proposed method is therefore a promising tool for structural assessment in a real structure because it shows reliable results with experimental data for nonlinear damage detection which renders the frequency response function–based method convenient for structural health monitoring.

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Background: Tuberculosis still remains one of the largest killer infectious diseases, warranting the identification of newer targets and drugs. Identification and validation of appropriate targets for designing drugs are critical steps in drug discovery, which are at present major bottle-necks. A majority of drugs in current clinical use for many diseases have been designed without the knowledge of the targets, perhaps because standard methodologies to identify such targets in a high-throughput fashion do not really exist. With different kinds of 'omics' data that are now available, computational approaches can be powerful means of obtaining short-lists of possible targets for further experimental validation. Results: We report a comprehensive in silico target identification pipeline, targetTB, for Mycobacterium tuberculosis. The pipeline incorporates a network analysis of the protein-protein interactome, a flux balance analysis of the reactome, experimentally derived phenotype essentiality data, sequence analyses and a structural assessment of targetability, using novel algorithms recently developed by us. Using flux balance analysis and network analysis, proteins critical for survival of M. tuberculosis are first identified, followed by comparative genomics with the host, finally incorporating a novel structural analysis of the binding sites to assess the feasibility of a protein as a target. Further analyses include correlation with expression data and non-similarity to gut flora proteins as well as 'anti-targets' in the host, leading to the identification of 451 high-confidence targets. Through phylogenetic profiling against 228 pathogen genomes, shortlisted targets have been further explored to identify broad-spectrum antibiotic targets, while also identifying those specific to tuberculosis. Targets that address mycobacterial persistence and drug resistance mechanisms are also analysed. Conclusion: The pipeline developed provides rational schema for drug target identification that are likely to have high rates of success, which is expected to save enormous amounts of money, resources and time in the drug discovery process. A thorough comparison with previously suggested targets in the literature demonstrates the usefulness of the integrated approach used in our study, highlighting the importance of systems-level analyses in particular. The method has the potential to be used as a general strategy for target identification and validation and hence significantly impact most drug discovery programmes.

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The main objective of this study is to evaluate selected geophysical, structural and topographic methods on regional, local, and tunnel and borehole scales, as indicators of the properties of fracture zones or fractures relevant to groundwater flow. Such information serves, for example, groundwater exploration and prediction of the risk of groundwater inflow in underground construction. This study aims to address how the features detected by these methods link to groundwater flow in qualitative and semi-quantitative terms and how well the methods reveal properties of fracturing affecting groundwater flow in the studied sites. The investigated areas are: (1) the Päijänne Tunnel for water-conveyance whose study serves as a verification of structures identified on regional and local scales; (2) the Oitti fuel spill site, to telescope across scales and compare geometries of structural assessment; and (3) Leppävirta, where fracturing and hydrogeological environment have been studied on the scale of a drilled well. The methods applied in this study include: the interpretation of lineaments from topographic data and their comparison with aeromagnetic data; the analysis of geological structures mapped in the Päijänne Tunnel; borehole video surveying; groundwater inflow measurements; groundwater level observations; and information on the tunnel s deterioration as demonstrated by block falls. The study combined geological and geotechnical information on relevant factors governing groundwater inflow into a tunnel and indicators of fracturing, as well as environmental datasets as overlays for spatial analysis using GIS. Geophysical borehole logging and fluid logging were used in Leppävirta to compare the responses of different methods to fracturing and other geological features on the scale of a drilled well. Results from some of the geophysical measurements of boreholes were affected by the large diameter (gamma radiation) or uneven surface (caliper) of these structures. However, different anomalies indicating more fractured upper part of the bedrock traversed by well HN4 in Leppävirta suggest that several methods can be used for detecting fracturing. Fracture trends appear to align similarly on different scales in the zone of the Päijänne Tunnel. For example, similarities of patterns were found between the regional magnetic trends, correlating with orientations of topographic lineaments interpreted as expressions of fracture zones. The same structural orientations as those of the larger structures on local or regional scales were observed in the tunnel, even though a match could not be made in every case. The size and orientation of the observation space (patch of terrain at the surface, tunnel section, or borehole), the characterization method, with its typical sensitivity, and the characteristics of the location, influence the identification of the fracture pattern. Through due consideration of the influence of the sampling geometry and by utilizing complementary fracture characterization methods in tandem, some of the complexities of the relationship between fracturing and groundwater flow can be addressed. The flow connections demonstrated by the response of the groundwater level in monitoring wells to pressure decrease in the tunnel and the transport of MTBE through fractures in bedrock in Oitti, highlight the importance of protecting the tunnel water from a risk of contamination. In general, the largest values of drawdown occurred in monitoring wells closest to the tunnel and/or close to the topographically interpreted fracture zones. It seems that, to some degree, the rate of inflow shows a positive correlation with the level of reinforcement, as both are connected with the fracturing in the bedrock. The following geological features increased the vulnerability of tunnel sections to pollution, especially when several factors affected the same locations: (1) fractured bedrock, particularly with associated groundwater inflow; (2) thin or permeable overburden above fractured rock; (3) a hydraulically conductive layer underneath the surface soil; and (4) a relatively thin bedrock roof above the tunnel. The observed anisotropy of the geological media should ideally be taken into account in the assessment of vulnerability of tunnel sections and eventually for directing protective measures.

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Financial innovations have emerged globally to close the gap between the rising global demand for infrastructures and the availability of financing sources offered by traditional financing mechanisms such as fuel taxation, tax-exempt bonds, and federal and state funds. The key to sustainable innovative financing mechanisms is effective policymaking. This paper discusses the theoretical framework of a research study whose objective is to structurally and systemically assess financial innovations in global infrastructures. The research aims to create analysis frameworks, taxonomies and constructs, and simulation models pertaining to the dynamics of the innovation process to be used in policy analysis. Structural assessment of innovative financing focuses on the typologies and loci of innovations and evaluates the performance of different types of innovative financing mechanisms. Systemic analysis of innovative financing explores the determinants of the innovation process using the System of Innovation approach. The final deliverables of the research include propositions pertaining to the constituents of System of Innovation for infrastructure finance which include the players, institutions, activities, and networks. These static constructs are used to develop a hybrid Agent-Based/System Dynamics simulation model to derive propositions regarding the emergent dynamics of the system. The initial outcomes of the research study are presented in this paper and include: (a) an archetype for mapping innovative financing mechanisms, (b) a System of Systems-based analysis framework to identify the dimensions of Systems of Innovation analyses, and (c) initial observations regarding the players, institutions, activities, and networks of the System of Innovation in the context of the U.S. transportation infrastructure financing.

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The purpose of this paper is to examine the promising contributions of the Concept Maps for Learning (CMfL) website to assessment for learning practices. The CMfL website generates concept maps from relatedness degree of concepts pairs through the Pathfinder Scaling Algorithm. This website also confirms the established principles of effective assessment for learning, for it is capable of automatically assessing students' higher order knowledge, simultaneously identifying strengths and weaknesses, immediately providing useful feedback and being user-friendly. According to the default assessment plan, students first create concept maps on a particular subject and then they are given individualized visual feedback followed by associated instructional material (e.g., videos, website links, examples, problems, etc.) based on a comparison of their concept map and a subject matter expert's map. After studying the feedback and instructional material, teachers can monitor their students' progress by having them create revised concept maps. Therefore, we claim that the CMfL website may reduce the workload of teachers as well as provide immediate and delayed feedback on the weaknesses of students in different forms such as graphical and multimedia. For the following study, we will examine whether these promising contributions to assessment for learning are valid in a variety of subjects.

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The increasing stock of aging office buildings will see a significant growth in retrofitting projects in Australian capital cities. Stakeholders of refitting works will also need to take on the sustainability challenge and realize tangible outcomes through project delivery. Traditionally, decision making for aged buildings, when facing the alternatives, is typically economically driven and on ad hoc basis. This leads to the tendency to either delay refitting for as long as possible thus causing building conditions to deteriorate, or simply demolish and rebuild with unjust financial burden. The technologies involved are often limited to typical strip-clean and repartition with dry walls and office cubicles. Changing business operational patterns, the efficiency of office space, and the demand on improved workplace environment, will need more innovative and intelligent approaches to refurbishing office buildings. For example, such projects may need to respond to political, social, environmental and financial implications. There is a need for the total consideration of buildings structural assessment, modeling of operating and maintenance costs, new architectural and engineering designs that maximise the utility of the existing structure and resulting productivity improvement, specific construction management procedures including procurement methods, work flow and scheduling and occupational health and safety. Recycling potential and conformance to codes may be other major issues. This paper introduces examples of Australian research projects which provided a more holistic approach to the decision making of refurbishing office space, using appropriate building technologies and products, assessment of residual service life, floor space optimisation and project procurement in order to bring about sustainable outcomes. The paper also discusses a specific case study on critical factors that influence key building components for these projects and issues for integrated decision support when dealing with the refurbishment, and indeed the “re-life”, of office buildings.

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Finite element method (FEM) relies on an approximate function to fit into a governing equation and minimizes the residual error in the integral sense in order to generate solutions for the boundary value problems (nodal solutions). Because of this FEM does not show simultaneous capacities for accurate displacement and force solutions at node and along an element, especially when under the element loads, which is of much ubiquity. If the displacement and force solutions are strictly confined to an element’s or member’s ends (nodal response), the structural safety along an element (member) is inevitably ignored, which can definitely hinder the design of a structure for both serviceability and ultimate limit states. Although the continuous element deflection and force solutions can be transformed into the discrete nodal solutions by mesh refinement of an element (member), this setback can also hinder the effective and efficient structural assessment as well as the whole-domain accuracy for structural safety of a structure. To this end, this paper presents an effective, robust, applicable and innovative approach to generate accurate nodal and element solutions in both fields of displacement and force, in which the salient and unique features embodies its versatility in applications for the structures to account for the accurate linear and second-order elastic displacement and force solutions along an element continuously as well as at its nodes. The significance of this paper is on shifting the nodal responses (robust global system analysis) into both nodal and element responses (sophisticated element formulation).

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The current procedures in post-earthquake safety and structural assessment are performed manually by a skilled triage team of structural engineers/certified inspectors. These procedures, and particularly the physical measurement of the damage properties, are time-consuming and qualitative in nature. This paper proposes a novel method that automatically detects spalled regions on the surface of reinforced concrete columns and measures their properties in image data. Spalling has been accepted as an important indicator of significant damage to structural elements during an earthquake. According to this method, the region of spalling is first isolated by way of a local entropy-based thresholding algorithm. Following this, the exposure of longitudinal reinforcement (depth of spalling into the column) and length of spalling along the column are measured using a novel global adaptive thresholding algorithm in conjunction with image processing methods in template matching and morphological operations. The method was tested on a database of damaged RC column images collected after the 2010 Haiti earthquake, and comparison of the results with manual measurements indicate the validity of the method.

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The focus of this paper is to outline a method for consolidating and implementing the work on performance-based specification and testing. First part of the paper will review the mathematical significance of the variables used in common service life models. The aim is to identify a set of significant variables that influence the ingress of chloride ions into concrete. These variables are termed as Key Performance Indicators (KPI’s). This will also help to reduce the complexity of some of the service life models and make them more appealing for practicing engineers. The second part of the paper presents a plan for developing a database based on these KPI’s so that relationships can then be drawn between common concrete mix parameters and KPI’s. This will assist designers in specifying a concrete with adequate performance for a particular environment. This, collectively, is referred to as the KPI based approach and the concluding remarks will outline how the authors envisage the KPI theory to relate to performance assessment and monitoring.

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)