• Structural Engineering and Mechanics
• /
• v.35 no.2
• /
• pp.127-140
• /
• 2010

This paper focuses on a number of criteria that enable controlling the influence of geometric simplification on the quality of finite element (FE) computations. To perform the mechanical simulation of a component, the corresponding geometric model typically needs to be simplified in accordance with hypotheses adopted regarding the component's mechanical behaviour. The method presented herein serves to compute an a posteriori indicator for the purpose of estimating the significance of each feature removal. This method can be used as part of an adaptive process of geometric simplification. If a shape detail removed during the shape simplification process proves to be influential on mechanical behaviour, the particular detail can then be reinserted into the simplified model, thus making it possible to readapt the initial simulation model. The fields of application for such a method are: static problems involving linear elastic behaviour, and linear thermal problems with stationary conduction.

• Smart Structures and Systems
• /
• v.16 no.1
• /
• pp.213-222
• /
• 2015

Early detection and precise location of leakage is of great importance for life-cycle maintenance and management of municipal pipeline system. In the past few years, acoustic emission (AE) techniques have demonstrated to be an excellent tool for on-line leakage detection. Regarding the multi-mode and frequency dispersion characteristics of AE signals propagating along a pipeline, the direct cross-correlation technique that assumes the constant AE propagation velocity does not perform well in practice for acoustic leak location. This paper presents an improved cross-correlation method based on wavelet transform, with due consideration of the frequency dispersion characteristics of AE wave and the contribution of different mode. Laboratory experiments conducted to simulate pipeline gas leakage and investigate the frequency spectrum signatures of AE leak signals. By comparing with the other methods for leak location identification, the feasibility and superiority of the proposed method are verified.

• International Journal of Control, Automation, and Systems
• /
• v.2 no.1
• /
• pp.125-133
• /
• 2004

In this paper, we present a new method that combines the characteristics of edge in-formation and second-order neural networks for the classification of structural textures. The edges of a texture are extracted using an edge detection approach. From this edge information, classification features called second-order features are obtained. These features are fed into a second-order neural network for training and subsequent classification. It will be shown that the main disadvantage of using structural methods in texture classifications, namely, the difficulty of the extraction of texels, is overcome by the proposed method.

• Structural Monitoring and Maintenance
• /
• v.3 no.3
• /
• pp.259-276
• /
• 2016

Historically the UK has been a pioneer and early adopter of experimental investigation techniques on new and operation structures, a technology that would now be descried as 'structural health monitoring' (SHM), yet few of these investigations have been enduring or carried out on the long span or tall structures that feature in flagship SHM applications in the Far East.

• Journal of Conservation Science
• /
• v.23
• /
• pp.53-65
• /
• 2008

This research investigated and analyzed the structure and material feature of the wall behind the Buddha of main temple in An-dong Bongjeonsa through applying the natural scientific method, in order to closely examine its production technique. As a result of the research, the structural and material feature of the wall has been clarified and its production technique applied to the structure has been understood in a comprehensive sense. The target sample basically adopted the two-layer wall system, which showed a symmetric structure to the center made with the wooden material, and is estimated to follow the structural tendency of a general wall which is organized with the first layer, the midterm layer, and the painting wall layer. Each layer formed by the production procedure showed difference in the material and production method according to its characteristics and roles. And it was identified that, in general, the higher a layer lies, the finer grains it has. Combination of the main materials and the additives, used for the wall forming, was presumed to contribute to improving its durability and conservativeness. Also interaction between the materials generating the conservativeness and the producer's technical effect seemed to fortify solidity of the wall.

• Steel and Composite Structures
• /
• v.21 no.6
• /
• pp.1183-1210
• /
• 2016

Viscoelastic sandwich structures (VSSs) are widely used in mechanical equipment, but in the service process, they always suffer from aging which affect the whole performance of equipment. Therefore, aging state recognition of VSSs is significant to monitor structural state and ensure the reliability of equipment. However, non-stationary vibration response signals and weak state change characteristics make this task challenging. This paper proposes a novel method for this task based on adaptive second generation wavelet packet transform (ASGWPT) and multiwavelet support vector machine (MWSVM). For obtaining sensitive feature parameters to different structural aging states, the ASGWPT, its wavelet function can adaptively match the frequency spectrum characteristics of inspected vibration response signal, is developed to process the vibration response signals for energy feature extraction. With the aim to improve the classification performance of SVM, based on the kernel method of SVM and multiwavelet theory, multiwavelet kernel functions are constructed, and then MWSVM is developed to classify the different aging states. In order to demonstrate the effectiveness of the proposed method, different aging states of a VSS are created through the hot oxygen accelerated aging of viscoelastic material. The application results show that the proposed method can accurately and automatically recognize the different structural aging states and act as a promising approach to aging state recognition of VSSs. Furthermore, the capability of ASGWPT in processing the vibration response signals for feature extraction is validated by the comparisons with conventional second generation wavelet packet transform, and the performance of MWSVM in classifying the structural aging states is validated by the comparisons with traditional wavelet support vector machine.

• Smart Structures and Systems
• /
• v.29 no.1
• /
• pp.129-140
• /
• 2022

The effectiveness of system identification, damage detection, condition assessment and other structural analyses relies heavily on the accuracy and reliability of the measured data in structural health monitoring (SHM) systems. However, data anomalies often occur in SHM systems, leading to inaccurate and untrustworthy analysis results. Therefore, anomalies in the raw data should be detected and cleansed before further analysis. Previous studies on data anomaly detection mainly focused on just single type of data anomaly for denoising or removing outliers, meanwhile, the existing methods of detecting multiple data anomalies are usually time consuming. For these reasons, recognising multiple anomaly patterns for real-time alarm and analysis in field monitoring remains a challenge. Aiming to achieve an efficient and accurate detection for multi-type data anomalies for field SHM, this study proposes a pattern-recognition-based data anomaly detection method that mainly consists of three steps: the feature extraction from the long time-series data samples, the training of a pattern recognition neural network (PRNN) using the features and finally the detection of data anomalies. The feature extraction step remarkably reduces the time cost of the network training, making the detection process very fast. The performance of the proposed method is verified on the basis of the SHM data of two practical long-span bridges. Results indicate that the proposed method recognises multiple data anomalies with very high accuracy and low calculation cost, demonstrating its applicability in field monitoring.

• Journal of Computational Design and Engineering
• /
• v.2 no.4
• /
• pp.253-260
• /
• 2015

Feature-based NC machining, which requires high quality of 3D CAD model, is widely used in machining aircraft structural part. However, there has been little research on how to automatically detect the CAD model errors. As a result, the user has to manually check the errors with great effort before NC programming. This paper proposes an automatic CAD model errors detection approach for aircraft structural part. First, the base faces are identified based on the reference directions corresponding to machining coordinate systems. Then, the CAD models are partitioned into multiple local regions based on the base faces. Finally, the CAD model error types are evaluated based on the heuristic rules. A prototype system based on CATIA has been developed to verify the effectiveness of the proposed approach.

• Smart Structures and Systems
• /
• v.15 no.2
• /
• pp.259-281
• /
• 2015

In this study, a structural identification method is proposed to assess the integrity of gravity-type caisson structures by analyzing vibration features. To achieve the objective, the following approaches are implemented. Firstly, a simplified structural model with a few degrees-of-freedom (DOFs) is formulated to represent the gravity-type caisson structure that corresponds to the sensors' DOFs. Secondly, a structural identification algorithm based on the use of vibration characteristics of the limited DOFs is formulated to fine-tune stiffness and damping parameters of the structural model. Finally, experimental evaluation is performed on a lab-scaled gravity-type caisson structure in a 2-D wave flume. For three structural states including an undamaged reference, a water-level change case, and a foundation-damage case, their corresponding structural integrities are assessed by identifying structural parameters of the three states by fine-tuning frequency response functions, natural frequencies and damping factors.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.31 no.1
• /
• pp.63-69
• /
• 2018

Bearing is an essential component in many rotary machineries. To prevent its unpredicted failures and undesired downtime cost, many researches have been made in the field of Prognostics and Health Management(PHM), in which the key issue is to establish a proper feature reflecting its current health state properly at the early stage. However, conventional features have shown some limitations that make them less useful for early diagnostics and prognostics because it tends to increase abruptly at the end of life. This paper proposes a new feature extraction method using the envelope analysis and weighted sum with correlation coefficient. The developed method is demonstrated using the IMS bearing data given by NASA Ames Prognostics Data Repository. Results by the proposed feature are compared with those by conventional approach.