• Wind and Structures
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• v.26 no.1
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• pp.45-56
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• 2018

Wind damage of urban trees arises to be a serious issue especially in the typhoon-prone areas. As a family of tree species widely-planted in Southeast China, the structural behaviors of Plane tree is investigated. In order to accommodate the complexities of tree morphology, a fractal theory based finite element modeling method is proposed. On-site measurement of Plane trees is performed for physical definition of structural parameters. It is revealed that modal frequencies of Plane trees distribute in a manner of grouped dense-frequencies; bending is the main mode of structural failure. In conjunction with the probability density evolution method, the fragility assessment of urban trees subjected to wind excitations is then proceeded. Numerical results indicate that small-size segments such as secondary branches feature a relatively higher failure risk in a low wind level, and a relatively lower failure risk in a high wind level owing to windward shrinks. Besides, the trunk of Plane tree is the segment most likely to be damaged than other segments in case of high winds. The failure position tends to occur at the connection between trunk and primary branches, where the logical protections and reinforcement measures can be implemented for mitigating the wind damage.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.33 no.4
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• pp.209-216
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• 2020

In this paper, parametric structural analysis is presented utilizing the co-rotational(CR) shell analysis utilizing EDISON. CR shell analysis shows faster convergence than the commercial software, NASTRAN, does. The 1st natural frequency of the rear frame is obtained, which is close to that of the engine during high speed cruise. Three cases under two design variables are presented and analyzed. Gusset is shown to be more effective among those which feature the same weight change. The results presented in this paper will be applicable for further researches to improve the durability of a motorcycle rear frame.

• Smart Structures and Systems
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• v.17 no.3
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• pp.445-470
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• 2016

In this study, the Bayesian probabilistic framework is investigated for modal identification and modal identifiability based on the field measurements provided in the structural health monitoring benchmark problem of an instrumented cable-stayed bridge named Ting Kau Bridge (TKB). The comprehensive structural health monitoring system on the cable-stayed TKB has been operated for more than ten years and it is recognized as one of the best test-beds with readily available field measurements. The benchmark problem of the cable-stayed bridge is established to stimulate investigations on modal identifiability and the present paper addresses this benchmark problem from the Bayesian prospective. In contrast to deterministic approaches, an appealing feature of the Bayesian approach is that not only the optimal values of the modal parameters can be obtained but also the associated estimation uncertainty can be quantified in the form of probability distribution. The uncertainty quantification provides necessary information to evaluate the reliability of parametric identification results as well as modal identifiability. Herein, the Bayesian spectral density approach is conducted for output-only modal identification and the Bayesian model class selection approach is used to evaluate the significance of different modes in modal identification. Detailed analysis on the modal identification and modal identifiability based on the measurements of the bridge will be presented. Moreover, the advantages and potentials of Bayesian probabilistic framework on structural health monitoring will be discussed.

• Smart Structures and Systems
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• v.6 no.3
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• pp.335-347
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• 2010

Recent advances in hardware and instrumentation technology have allowed the possibility of deploying very large sensor arrays on structures. Exploiting the huge amount of data that can result in order to perform vibration-based structural health monitoring (SHM) is not a trivial task and requires research into a number of specific problems. In terms of pressing problems of interest, this paper discusses: the design and optimisation of appropriate sensor networks, efficient data reduction techniques, efficient and automated feature extraction methods, reliable methods to deal with environmental and operational variability, efficient training of machine learning techniques and multi-scale approaches for dealing with very local damage. The paper is a result of the ESF-S3T Eurocores project "Smart Sensing For Structural Health Monitoring" (S3HM) in which a consortium of academic partners from across Europe are attempting to address issues in the design of automated vibration-based SHM systems for structures.

• Smart Structures and Systems
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• v.15 no.3
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• pp.699-715
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• 2015

A modal parameter based damage sensitive feature (DSF) is defined to mimic the relative change in any diagonal element of the stiffness matrix of a model of a structure. The damage assessment is performed in a statistical pattern recognition framework using empirical complementary cumulative distribution functions (ECCDFs) of the DSFs extracted from measured operational vibration response data. Methods are discussed to perform probabilistic structural health assessment with respect to the following questions: (a) "Is there a change in the current state of the structure compared to the baseline state?", (b) "Does the change indicate a localized stiffness reduction or increase?", with the latter representing a situation of retrofitting operations, and (c) "What is the severity of the change in a probabilistic sense?". To identify a range of normal structural variations due to environmental and operational conditions, lower and upper bound ECCDFs are used to define the baseline structural state. Such an approach attempts to decouple "non-damage" related variations from damage induced changes, and account for the unknown environmental/operational conditions of the current state. The damage assessment procedure is discussed using numerical simulations of ambient vibration testing of a bridge deck system, as well as shake table experimental data from a 4-story steel frame.

• Smart Structures and Systems
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• v.12 no.3_4
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• pp.345-361
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• 2013

Locating and assessing the severity of damage in large or complex structures is one of the most challenging problems in the field of civil engineering. Considering that the wavelet packet transform (WPT) has the ability to clearly reflect the damage characteristics of structural response signals and the artificial neural network (ANN) is capable of learning in an unsupervised manner and of forming new classes when the structural exhibits change, this paper investigates a multi-stage structural damage diagnosis method by using the WPT and ANN based on "energy-damage" theory, in which, the wavelet packet component energies are first extracted to be damage sensitive feature and then adopted as input into an improved back propagation (BP) neural network model for damage diagnosis in a step by step mode. To validate the efficacy of the presented approach of the damage diagnosis, the benchmark structure of the American Society of Civil Engineers (ASCE) is employed in the case study. The results of damage diagnosis indicate that the method herein is computationally efficient and is able to detect the existence of different damage patterns in the simulated experiment where minor, moderate and severe damages corresponds to involving in the loss of stiffness on braces or the removal bracing in various combinations.

• Smart Structures and Systems
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• v.4 no.6
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• pp.759-777
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• 2008

A wireless sensing system is designed for application to structural monitoring and damage detection applications. Embedded in the wireless monitoring module is a two-tier prediction model, the auto-regressive (AR) and the autoregressive model with exogenous inputs (ARX), used to obtain damage sensitive features of a structure. To validate the performance of the proposed wireless monitoring and damage detection system, two near full scale single-story RC-frames, with and without brick wall system, are instrumented with the wireless monitoring system for real time damage detection during shaking table tests. White noise and seismic ground motion records are applied to the base of the structure using a shaking table. Pattern classification methods are then adopted to classify the structure as damaged or undamaged using time series coefficients as entities of a damage-sensitive feature vector. The demonstration of the damage detection methodology is shown to be capable of identifying damage using a wireless structural monitoring system. The accuracy and sensitivity of the MEMS-based wireless sensors employed are also verified through comparison to data recorded using a traditional wired monitoring system.

• East Asian Economic Review
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• v.18 no.1
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• pp.55-81
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• 2014

This study aims to answer two questions using input-output decomposition analysis: 1) Have emerging Asian economies decoupled? 2) What are the sources of structural changes in gross outputs and value-added of emerging Asian economies related to the first question? The main findings of the study are as follows: First, since 1990, there has been a trend of increasing dependence on exports to extra-regions such as G3 and the ROW, indicating no sign of "decoupling", but rather an increasing integration of emerging Asian countries into global trade. Second, there is a contrasting feature in the sources of structural changes between non-China emerging Asia and China. Dependence of non-China emerging Asia on intra-regional trade has increased in line with strengthening economic integration in East Asia, whereas China has disintegrated from the region. Therefore, it can be said that China has contributed to no sign of decoupling of emerging Asia as a whole.

• Smart Structures and Systems
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• v.23 no.3
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• pp.279-293
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• 2019

To ensure high quality data being used for data mining or feature extraction in the bridge structural health monitoring (SHM) system, a practical sensor fault diagnosis methodology has been developed based on the similarity of symmetric structure responses. First, the similarity of symmetric response is discussed using field monitoring data from different sensor types. All the sensors are initially paired and sensor faults are then detected pair by pair to achieve the multi-fault diagnosis of sensor systems. To resolve the coupling response issue between structural damage and sensor fault, the similarity for the target zone (where the studied sensor pair is located) is assessed to determine whether the localized structural damage or sensor fault results in the dissimilarity of the studied sensor pair. If the suspected sensor pair is detected with at least one sensor being faulty, field test could be implemented to support the regression analysis based on the monitoring and field test data for sensor fault isolation and reconstruction. Finally, a case study is adopted to demonstrate the effectiveness of the proposed methodology. As a result, Dasarathy's information fusion model is adopted for multi-sensor information fusion. Euclidean distance is selected as the index to assess the similarity. In conclusion, the proposed method is practical for actual engineering which ensures the reliability of further analysis based on monitoring data.

• Korean Journal of Social Welfare
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• v.61 no.3
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• pp.283-305
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• 2009

The purpose of this study is to develop a scale for the social network of college students in Korea. The social network scale for college students in this paper was developed through 1) literature review and item development 2) focus-group meeting 3) depth-interview with college students 4) pilot-studies and 5) study. The scale of 23 items has been constructed with three features : The first is 'relational feature' with 2 items; the second 'structural feature' with 7 items; and the third 'functional feature' with 14 items. The functional feature in this social network scale has consisted of emotional, economic, spiritual, and socio-relational aspects and has been revealed to the validity and the reliability of Cronbach's ${\alpha}$ 0.716. This study would contribute for college students to solve the many problems in their own lives through the social networks.