• Steel and Composite Structures
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• v.7 no.6
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• pp.487-502
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• 2007

In recent decades there has been a trend towards improved mechanical characteristics of materials used in footbridge construction. It has enabled engineers to design lighter, slender and more aesthetic structures. As a result of these construction trends, many footbridges have become more susceptible to vibrations when subjected to dynamic loads. In addition to this, some inherit modelling uncertainties related to a lack of information on the as-built structure, such as boundary conditions, material properties, and the effects of non-structural elements make difficult to evaluate modal properties of footbridges, analytically. For these purposes, modal testing of footbridges is used to rectify these problems after construction. This paper describes an arch type steel footbridge, its analytical modelling, modal testing and finite element model calibration. A modern steel footbridge which has arch type structural system and located on the Karadeniz coast road in Trabzon, Turkey is selected as an application. An analytical modal analysis is performed on the developed 3D finite element model of footbridge to provide the analytical frequencies and mode shapes. The field ambient vibration tests on the footbridge deck under natural excitation such as human walking and traffic loads are conducted. The output-only modal parameter identification is carried out by using the peak picking of the average normalized power spectral densities in the frequency domain and stochastic subspace identification in the time domain, and dynamic characteristics such as natural frequencies mode shapes and damping ratios are determined. The finite element model of footbridge is calibrated to minimize the differences between analytically and experimentally estimated modal properties by changing some uncertain modelling parameters such as material properties. At the end of the study, maximum differences in the natural frequencies are reduced from 22% to only %5 and good agreement is found between analytical and experimental dynamic characteristics such as natural frequencies, mode shapes by model calibration.

• Journal of Korean Institute of Industrial Engineers
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• v.10 no.1
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• pp.17-25
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• 1984

The use of Box-Jenkins technique is still very limited due to the high level of knowledge required in comprehending the technique and the cumbersome iterative procedure which requires a large amount of cost and time. This paper proposes a method of automating the univariate Box-Jekins modelling to overcome the limitations of subjective identification in iterative procedure by using Variate Difference method, D-statistic and Pattern Recognition algorithm combined with Akaike's Information Criterion. The results of the application to real data show that the average performance of automatic modelling procedure is better or not worse, at least, than those of the existing models which have been manually set up and reported in the literature.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.05
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• pp.184-187
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• 1997

In terms of a system identification, we present a method for autoregressive(AR) modelling of variious biomedical signal. Model order is estimated fly low rank approximation and coefficients are determined by innovation processes of Kalman filter derivation. An application of the method is given for visual evoked potentials.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.772-777
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• 1993

This paper presents a neuro-fuzzy modelling method that determines chemicals dosing model based on historical operation data for effective water quality control in water treatment system and calculates automatically the amount of optimum chemicals dosing against the changes of raw water qualities and flow rate. The structure identification in the modelling by means of neuro-fuzzy reasing is performed by Genetic Algorithm(GA) and Complex Method in which the numbers of hidden layer and its hidden nodes, learning rate and connection pattern between input layer and output layer are identified. The learning network is implemented utilizing Back Propagation(BP) algorithm. The effectiveness of the proposed modelling scheme and the feasibility of the acquired neuro-fuzzy network is evaluated through computer simulation for chemicals dosing control in water treatment system.

• Proceedings of the Korean Powder Metallurgy Institute Conference
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• 2006.09a
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• pp.64-65
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• 2006

Anisotropic constitutive equations for sintering of metal powder compacts have been formulated from a linear viscous transversely-isotropic model in which an anisotropic sintering stress has been introduced to describe free sintering densification kinetics. The identification of material parameters defined in the model, has been achieved from thermomechanical experiments performed on 316L stainless steel warm-compacted powder in a dilatometer allowing controlled compressive loading.

• Speech Sciences
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• v.7 no.3
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• pp.185-195
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• 2000

This paper seeks to compare the feature parameters used in speaker identification systems under noisy environments. The feature parameters compared are LP cepstrum (LPCC), Cepstral mean subtraction(CMS), Pole-filtered CMS(PFCMS), Adaptive component weighted cepstrum(ACW) and Postfilter cepstrum(PF). The GMM-based text independent speaker identification system is designed for this target. Some series of experiments show that the LPCC parameter is adequate for modelling the speaker in the matched environments between train and test stages. But in the mismatched training and testing conditions, modified parameters are preferable the LPCC. Especially CMS and PFCMS parameters are more effective for the microphone mismatching conditions while the ACW and PF parameters are good for more noisy mismatches.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2007.10a
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• pp.197-199
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• 2007

• Smart Structures and Systems
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• v.23 no.2
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• pp.123-139
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• 2019

Impact forces induced by external object collisions can cause serious damages to civil engineering structures. While accurate and prompt identification of such impact forces is a critical task in structural health monitoring, it is not readily feasible for civil structures because the force measurement is extremely challenging and the force location is unpredictable for full-scale field structures. This study proposes a novel approach for identification of impact force including its location and time history using a small number of multi-metric observations. The method combines an augmented Kalman filter (AKF) and Genetic algorithm for accurate identification of impact force. The location of impact force is statistically determined in the way to minimize the AKF response estimate error at measured locations and then time history of the impact force is accurately constructed by optimizing the error co-variances of AKF using Genetic algorithm. The efficacy of proposed approach is numerically demonstrated using a truss and a plate model considering the presence of modelling error and measurement noises.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.17-22
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• 2011

A considerable number of construction disasters occur on pathways. Safety management is usually performed on construction sites to prevent accidents in activity areas. This means that the safety management level of hazards on pathways is relatively minimized. Many researchers have noted that hazard identification is fundamental to safety management. Thus, algorithms for helping safety managers to identify hazardous areas are developed using automated data collection technology. These algorithms primarily search for potential hazardous areas by comparing workers' location logs based on a real-time location system and optimal routes based on BIM. Potential hazardous areas are filtered by identified hazardous areas and activity areas. After that, safety managers are provided with information about potential hazardous areas and can establish proper safety countermeasures. This can help to improve safety on construction sites.

• Wind and Structures
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• v.14 no.3
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• pp.221-238
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• 2011

The estimated response of large-scale engineering structures to severe wind loads is prone to modelling uncertainties that can only ultimately be assessed by full-scale testing. To this end ambient vibration data from full-scale monitoring of the historic Clifton Suspension Bridge has been analysed using a combination of a frequency domain system identification method and a more elaborate stochastic identification technique. There is evidence of incipient coupling action between the first vertical and torsional modes in strong winds, providing unique full-scale data and making this an interesting case study. Flutter derivative estimation, which has rarely previously been attempted on full-scale data, was performed to provide deeper insight into the bridge aerodynamic behaviour, identifying trends towards flutter at higher wind speeds. It is shown that, as for other early suspension bridges with bluff cross-sections, single-degree-of-freedom flutter could potentially occur at wind speeds somewhat below requirements for modern designs. The analysis also demonstrates the viability of system identification techniques for extracting valuable results from full-scale data.