• Structural Engineering and Mechanics
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• v.43 no.3
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• pp.371-394
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• 2012

Accurate finite element (FE) models are needed in many applications of Civil Engineering such as health monitoring, damage detection, structural control, structural evaluation and assessment. Model accuracy depends on both the model structure (the form of the equations) and the model parameters (the coefficients of the equations), and can be generally improved through that process of experimental reconciliation known as model updating. However, modelling errors, including (i) errors in the model structure and (ii) errors in parameters excluded from adjustment, may bias the solution, leading to an updated model which replicates measurements but lacks physical meaning. In this paper, an application of ambient-vibration-based model updating to a large-scale benchmark prototype of a building structure is reported in which both types of error are met. The error in the model structure, originating from unmodelled secondary structural elements unexpectedly working as resonant appendages, is faced through a reduction of the experimental modal model. The error in the model parameters, due to the inevitable constraints imposed on parameters to avoid ill-conditioning and under-determinacy, is faced through a multi-model parameterization approach consisting in the generation and solution of a multitude of models, each characterized by a different set of updating parameters. Results show that modelling errors may significantly impair updating even in the case of seemingly simple systems and that multi-model reasoning, supported by physical insight, may effectively improve the accuracy and robustness of calibration.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.25 no.2
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• pp.202-210
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• 2015

Objectives: The purpose of this study was to examine clean room(C/R) structure, air conditioning and contamination control systems and to provide basic information for identifying a correlation between the semiconductor work environment and workers' disease. Methods: This study was conducted at 200 mm and 300 mm semiconductor wafer fabrication facilities. The C/R structure and air conditioning method were investigated using basic engineering data from documentation for C/R construction. Furthermore, contamination parameters such as airborne particles, temperature, humidity, acids, ammonia, organic compounds, and vibration in the C/R were based on the International Technology Roadmap for Semiconductors(ITRS). The properties of contamination control systems and the current status of monitoring of various contaminants in the C/R were investigated. Results: 200 mm and 300 mm wafer fabrication facilities were divided into fab(C/R) and sub fab(Plenum), and fab, clean sub fab and facility sub fab, respectively. Fresh air(FA) is supplied in the plenum or clean sub fab by the outdoor air handling unit system which purifies outdoor air. FA supply or contaminated indoor air ventilation rates in the 200 mm and 300 mm wafer fabrication facilities are approximately 10-25%. Furthermore, semiconductor clean rooms strictly controlled airborne particles(${\leq}1,000{\sharp}/ft^3$), temperature($23{\pm}0.5^{\circ}C$), humidity($45{\pm}5%$), air velocity(0.4 m/s), air change(60-80 cycles/hr), vibration(${\leq}1cm/s^2$), and differential pressure(atmospheric pressure$+1.0-2.5mmH_2O$) through air handling and contamination control systems. In addition, acids, alkali and ozone are managed at less than internal criteria by chemical filters. Conclusions: Semiconductor clean rooms can be a pleasant environment for workers as well as semiconductor devices. However, based on the precautionary principle, it may be necessary to continuously improve semiconductor processes and the work environment.

• Structural Monitoring and Maintenance
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• v.2 no.1
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• pp.77-93
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• 2015

The idea of using measured dynamic characteristics for damage detection is attractive because it allows for a global evaluation of the structural health and condition. However, vibration-based damage detection for complex structures such as long-span cable-supported bridges still remains a challenge. As a suspension or cable-stayed bridge involves in general thousands of structural components, the conventional damage detection methods based on model updating and/or parameter identification might result in ill-conditioning and non-uniqueness in the solution of inverse problems. Alternatively, methods that utilize, to the utmost extent, information from forward problems and avoid direct solution to inverse problems would be more suitable for vibration-based damage detection of long-span cable-supported bridges. The auto-associative neural network (ANN) technique and the probabilistic neural network (PNN) technique, that both eschew inverse problems, have been proposed for identifying and locating damage in suspension and cable-stayed bridges. Without the help of a structural model, ANNs with appropriate configuration can be trained using only the measured modal frequencies from healthy structure under varying environmental conditions, and a new set of modal frequency data acquired from an unknown state of the structure is then fed into the trained ANNs for damage presence identification. With the help of a structural model, PNNs can be configured using the relative changes of modal frequencies before and after damage by assuming damage at different locations, and then the measured modal frequencies from the structure can be presented to locate the damage. However, such formulated ANNs and PNNs may still be incompetent to identify damage occurring at the deck members of a cable-supported bridge because of very low modal sensitivity to the damage. The present study endeavors to enhance the damage identification capability of ANNs and PNNs when being applied for identification of damage incurred at deck members. Effort is first made to construct combined modal parameters which are synthesized from measured modal frequencies and modal shape components to train ANNs for damage alarming. With the purpose of improving identification accuracy, effort is then made to configure PNNs for damage localization by adapting the smoothing parameter in the Bayesian classifier to different values for different pattern classes. The performance of the ANNs with their input being modal frequencies and the combined modal parameters respectively and the PNNs with constant and adaptive smoothing parameters respectively is evaluated through simulation studies of identifying damage inflicted on different deck members of the double-deck suspension Tsing Ma Bridge.