• Smart Structures and Systems
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• v.14 no.5
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• pp.981-1004
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• 2014

Structural control is a very broad field combining the areas of automatic control and structural engineering, with applications ranging from aerospace and mechanical engineering to building and civil infrastructure systems. In this paper, the focus is placed on civil engineering applications only. The goal is to address the issues concurring to form the scientific paradigm. As a resut, possible future directions of research into this field are identified.

• Advances in Computational Design
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• v.2 no.2
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• pp.121-131
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• 2017

Collapse was one of the typical common geological hazards during the construction of tunnels. The risk assessment of collapse was an effective way to ensure the safety of tunnels. We established a prediction model of collapse based on Bayesian Network. 76 large or medium collapses in China were analyzed. The variable set and range of the model were determined according to the statistics. A collapse prediction software was developed and its veracity was also evaluated. At last the software was used to predict tunnel collapses. It effectively evaded the disaster. Establishing the platform can be subsequent perfect. The platform can also be applied to the risk assessment of other tunnel engineering.

• Structural Monitoring and Maintenance
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• v.7 no.2
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• pp.109-124
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• 2020

Benefiting from the massive monitoring data collected by the Structural health monitoring (SHM) system, scholars can grasp the complex environmental effects and structural state during structure operation. However, the monitoring data is often missing due to sensor faults and other reasons. It is necessary to study the recovery method of missing monitoring data. Taking the structural temperature monitoring data of Nanjing Dashengguan Yangtze River Bridge as an example, the long short-term memory (LSTM) network-based recovery method for missing structural temperature data is proposed in this paper. Firstly, the prediction results of temperature data using LSTM network, support vector machine (SVM), and wavelet neural network (WNN) are compared to verify the accuracy advantage of LSTM network in predicting time series data (such as structural temperature). Secondly, the application of LSTM network in the recovery of missing structural temperature data is discussed in detail. The results show that: the LSTM network can effectively recover the missing structural temperature data; incorporating more intact sensor data as input will further improve the recovery effect of missing data; selecting the sensor data which has a higher correlation coefficient with the data we want to recover as the input can achieve higher accuracy.

• Journal of Aerospace System Engineering
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• v.17 no.5
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• pp.58-62
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• 2023

In this work, the structural integrity was investigated of the structural design results of internal components for the aircraft engine. The radiator and intercooler were combined with the internal components of the engine. Therefore, the safety of the radiator and intercooler was investigated during flight conditions. The structural integrity was evaluated through structural analysis, using the finite element analysis method. The acceleration load for structural design and analysis was considered. The structural safety evaluation found the structural design results to be valid.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1995.04a
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• pp.160-169
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• 1995

This study presents an application of object-oriented methodology for structural dcsign process. A prototype system of integrated a structural design system is developed by introducing a structural analysis object model(SAOM) and structural design object model(SDOM). The SAOM module. which is modeled as a part of structural member, performs structural analysis using FEM approach and the SDOM module checks structural members based on Korea steel design standard. Above mentionedmodelsareabstraclencapsulatibleandreusable.

• International Journal of High-Rise Buildings
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• v.7 no.3
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• pp.233-242
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• 2018

Since the response reduction effect on over 200-meter-tall resulting from the seismic isolation system is smaller in general than low-rise and mid-rise buildings, mid-story isolated buildings are considered to reduce the response in the upper part above the isolation story, however, in many cases, the acceleration response just below the isolation story is likely to be the largest. This paper presents the structural design schemes, the design of the main structural frames, and the constructions of a 230-meter-tall super high-rise building with mid-story isolation mechanism integrated in Roppongi, Tokyo. Moreover, this paper shows how the architectural and structural design for integrating a mid-story isolation system in a super high-rise building has been conducted and what solutions have been derived in this project. The realization of this building indicates new possibilities for mid-story isolation design for super high-rise buildings.

• Wind and Structures
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• v.3 no.4
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• pp.221-241
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• 2000

Structural dynamics usually applies modal transformation rules aimed at de-coupling and/or minimizing the equations of motion. Proper orthogonal decomposition provides mathematical and conceptual tools to define suitable transformed spaces where a multi-variate and/or multi-dimensional random process is represented as a linear combination of one-variate and one-dimensional uncorrelated processes. Double modal transformation is the joint application of modal analysis and proper orthogonal decomposition applied to the loading process. By adopting this method the structural response is expressed as a double series expansion in which structural and loading mode contributions are superimposed. The simultaneous use of the structural modal truncation, the loading modal truncation and the cross-modal orthogonality property leads to efficient solutions that take into account only a few structural and loading modes. In addition the physical mechanisms of the dynamic response are clarified and interpreted.

• Computational Structural Engineering
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• v.25 no.1
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• pp.28-33
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• 2012

• Computational Structural Engineering
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• v.20 no.1 s.75
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• pp.37-42
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• 2007

• Computational Structural Engineering
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• v.20 no.2 s.76
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• pp.22-27
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• 2007