• Computers and Concrete
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• v.15 no.5
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• pp.771-794
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• 2015

This paper proposes an innovative method for selection of measurement sets in static parameter identification of concrete or steel bridges. This method is proved as a systematic tool to address the first steps of Structural System Identification procedures by observability techniques: the selection of adequate measurement sets. The observability trees show graphically how the unknown estimates are successively calculated throughout the recursive process of the observability analysis. The observability trees can be proved as an intuitive and powerful tool for measurement selection in beam bridges that can also be applied in complex structures, such as cable-stayed bridges. Nevertheless, in these structures, the strong link among structural parameters advises to assume a set of simplifications to increase the tree intuitiveness. In addition, a set of guidelines are provided to facilitate the representation of the observability trees in this kind of structures. These guidelines are applied in bridges of growing complexity to explain how the characteristics of the geometry of the structure (e.g. deck inclination, type of pylon-deck connection, or the existence of stay cables) affect the observability trees. The importance of the observability trees is justified by a statistical analysis of measurement sets randomly selected. This study shows that, in the analyzed structure, the probability of selecting an adequate measurement set with a minimum number of measurements at random is practically negligible. Furthermore, even bigger measurement sets might not provide adequate SSI of the unknown parameters. Finally, to show the potential of the observability trees, a large-scale concrete cable-stayed bridge is also analyzed. The comparison with the number of measurements required in the literature shows again the advantages of using the proposed method.

• Korean Journal of Chemical Engineering
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• v.35 no.12
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• pp.2327-2335
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• 2018

Fluid catalytic cracking (FCC) is an important chemical process that is widely used to produce valuable petrochemical products by cracking heavier components. However, many difficulties exist in modeling the FCC process due to its complexity. In this study, a dynamic process model of a FCC process is suggested and its structural observability is analyzed. In the process modeling, yield function for the kinetic model of the riser reactor was applied to explain the product distribution. Hydrodynamics, mass balance and energy balance equations of the riser reactor and the regenerator were used to complete the modeling. The process model was tested in steady-state simulation and dynamic simulation, which gives dynamic responses to the change of process variables. The result was compared with the measured data from operating plaint. In the structural analysis, the system was analyzed using the process model and the process design to identify the structural observability of the system. The reactor and regenerator unit in the system were divided into six nodes based on their functions and modeling relationship equations were built based on nodes and edges of the directed graph of the system. Output-set assignment algorithm was demonstrated on the occurrence matrix to find observable nodes and variables. Optimal locations for minimal addition of measurements could be found by completing the whole output-set assignment algorithm of the system. The result of this study can help predict the state more accurately and improve observability of a complex chemical process with minimal cost.

• Steel and Composite Structures
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• v.32 no.6
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• pp.731-741
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• 2019

Shear deformation effects are neglected in most structural system identification methods. This assumption might lead to important errors in some structures like built up steel or composite deep beams. Recently, the observability techniques were presented as one of the first methods for the inverse analysis of structures including the shear effects. In this way, the mechanical properties of the structures could be obtained from the nodal movements measured on static tests. One of the main controversial features of this procedure is the fact that the measurement set must include rotations. This characteristic might be especially problematic in those structures where rotations cannot be measured. To solve this problem and to increase its applicability, this paper proposes an update of the observability method to enable the structural identification including shear effects by measuring only vertical deflections. This modification is based on the introduction of a numerical optimization method. With this aim, the inverse analysis of several examples of growing complexity are presented to illustrate the validity and potential of the updated method.

• Smart Structures and Systems
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• v.30 no.4
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• pp.339-351
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• 2022

Evaluating the current condition of existing structures is of primary importance for economic and safety reasons. This can be addressed by Structural System Identification (SSI). A reliable static SSI depends on well-designed sensor configuration and loading cases, as well as efficient parameter estimation algorithms. Static SSI by the Measurement Error-Minimizing Observability Method (MEMOM) is a model-based deterministic static SSI method that could estimate structural parameters from static responses. In the current state of the art, this method is only applicable when structures are subjected to one loading case. This might lead to lack of information in some local regions of the structure (such as the null curvatures zones). To address this issue, the SSI by MEMOM using multiple loading cases is proposed in this work. Observability equations obtained from different loading cases are concatenated simultaneously and an optimization procedure is introduced to obtain the estimations by minimizing the discrepancy between the predicted response and the measured one. In addition, a Genetic-Algorithm (GA)-based Optimal Sensor Placement (OSP) method is proposed to tackle the OSP problem under multiple static loading cases for the very first time. In this approach, the Fisher Information Matrix (FIM)'s determinant is used as the metric of the goodness of sensor configurations. The numerical examples of a 3-span continuous bridge and a 13-story frame, are analyzed to validate the applicability of the extended SSI by MEMOM and the GA-based OSP method.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2004.10a
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• pp.461-468
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• 2004

Modal participation factor(MPF) is essential to analyze structural response under earthquake load. MPF of real structure differs from that of analytic mathematical model due to the error induced from analytical assumption and construction. In this study, a identification method is proposed to calculate the MPF of real structure based on H∞ optimal model reduction. The MPF is obtained from the relationship between observability, controllability matrix of realized from S.I. and typical 2-degree state space model. The proposed method is verified thorough examples.

• Smart Structures and Systems
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• v.8 no.2
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• pp.191-204
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• 2011

A technique for damage localization and assessment based on measurements of both eigenvectors curvatures and eigenfrequencies is proposed. The procedure is based on two successive steps: a model independent localization, based on changes of modal curvatures, and the solution of a one-dimensional minimization problem to evaluate damage intensity. The observability properties of damage parameters is discussed and, accordingly, a suitable change of coordinates is introduced. The proposed technique is illustrated with reference to a cantilever Euler beam endowed with a set of piezoelectric transducers. To assess the robustness of the algorithm, a parametric study of the identification errors with respect to the number of transducers and to the number of considered modal quantities is carried out with both clean and noise-corrupted data.

• International Journal of Contents
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• v.14 no.3
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• pp.32-38
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• 2018

This study examined the factors influencing the intention to use smartwatches using the integrated model of technology acceptance model (TAM) and innovation diffusion theory (IDT). An online survey was conducted and the data were analyzed using the structural equation modeling (SEM). The results showed that the research model had an acceptable fit, and all paths, except for the one from the perceived ease of use to the intention to use, were supported. Regarding paths from IDT to TAM, it was observed that higher the compatibility, the users perceived greater usefulness. Additionally, both observability and trialability influenced the perceived ease of use. However, perceived ease of use affected the intention only through the mediated effect of perceived usefulness. The implication of the study lies on the major focus on the effects of users' perceptions regarding innovative characteristics of smartwatches on the intention to adopt and attempted to increase the explanatory power of the TAM and IDT by combining both.

• Proceedings of the Korea Concrete Institute Conference
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• 2006.05a
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• pp.506-509
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• 2006

Extended Kalman Filter iterate the prediction and the filtering based on Initial state for the next time step. EKF method for the estimation of nonlinear parameters of a structural dynamic system is necessary that initial of state vector and error covariance matrix. Because those are unknown exactly, generally selected random values. That occasion observability problem appear because of unknown initial values. In this study, for the estimation of the nonlinear parameters, a simple one degree of Freedom example is carried out by Extended Kalman Filter. And initial value assumption for Parameter Estimation of Dynamic System are developed. The result of analysis is compared with calculated standard values.

• Smart Structures and Systems
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• v.7 no.3
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• pp.229-240
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• 2011

Recent developments in Smart Structures with very large scale embedded sensors and actuators have introduced new challenges in terms of data processing and sensor fusion. These smart structures are dynamically classified as a large-scale system with thousands of sensors and actuators that form the musculoskeletal of the structure, analogous to human body. In order to develop structural health monitoring and diagnostics with data provided by thousands of sensors, new sensor informatics has to be developed. The focus of our on-going research is to develop techniques and algorithms that would utilize this musculoskeletal system effectively; thus creating the intelligence for such a large-scale autonomous structure. To achieve this level of intelligence, three major research tasks are being conducted: development of a Bio-Inspired data analysis and information extraction from thousands of sensors; development of an analytical technique for Optimal Sensory System using Structural Observability; and creation of a bio-inspired decision-making and control system. This paper is focused on the results of our effort on the first task, namely development of a Neuro-Morphic Engineering approach, using a neuro-symbolic data manipulation, inspired by the understanding of human information processing architecture, for sensor fusion and structural diagnostics.

• Journal of the Earthquake Engineering Society of Korea
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• v.9 no.1 s.41
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• pp.25-32
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• 2005

Seismic load is distributed to modes of a structure through the modal participation factor(MPF). The modal participation factor is essential to analyze structural response under earthquake load. MPF of a real structure differs from that of analytical mathematical model due to the error induced from analytical assumptions and during the construction. In this study, an identification method is proposed to calculate the 1st MPF of real structure based on $H^{\infty}$ optimal model reduction. The MPF is obtained from the relationship between observability and controllability matrices realized from system identification and those of a prototype 2-degree state space model. The proposed method is verified thorough numerical examples.