Structural Performance Analysis of SAE Supra Chassis

Structural performance analysis of stochastic Petri nets

Proceedings of 1995 IEEE International Computer Performance and Dependability Symposium ◽

10.1109/ipds.1995.395816 ◽

2002 ◽

Cited By ~ 1

Author(s):

M. Silva ◽

J. Campos

Keyword(s):

Performance Analysis ◽

Petri Nets ◽

Stochastic Petri Nets ◽

Structural Performance

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STRUCTURAL PERFORMANCE ANALYSIS OF A THREAD IN AN ACADEMIC ROCKET MOTOR

10.26678/abcm.cobem2021.cob2021-2048 ◽

2021 ◽

Author(s):

Airton Felipe Soares Fernandes ◽

Alexandre Mateus Mendonça Bezerra ◽

Leonardo Camara Queiroz ◽

Evans Ferreira ◽

João Carlos Arantes Costa Júnior

Keyword(s):

Performance Analysis ◽

Rocket Motor ◽

Structural Performance

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Performance of asmetric structures reviewed with based plastic design performance (case study of application on building in Pekan Baru)

E3S Web of Conferences ◽

10.1051/e3sconf/202015605021 ◽

2020 ◽

Vol 156 ◽

pp. 05021

Author(s):

Jati Sunaryati ◽

Nidiasari ◽

Alfadian

Keyword(s):

Structural Analysis ◽

Performance Analysis ◽

Energy Method ◽

Structural Behavior ◽

Structural Performance ◽

Plastic Design ◽

Prone Area ◽

Zonation Maps ◽

Thrust Load

Performance-Based Plastic Design (PBPD) is a structural analysis that can be used to review structural performance. This method is increasingly popular to be used in the earthquake-prone area. This method is based on energy method that can be applied to steel or concrete structures. Meanwhile, Indonesia has already SNI 1726:2102 to be used as a guide in designing the thrust load to review the level of structural performance. Both of these things need to be used as a reference in areas that were initially considered safe from the earthquake but based on the development of earthquake micro zonation maps, it is very possible to become potential areas that also become earthquake regions. For this reason, the case of the structure that was built in the Pekanbaru area was taken. From the analyses of structural behavior, the structure that applied PBPD has greater displacement than the structures that apply the thrust load of SNI 1726: 2012. The percentage of displacement that occurred was 8-37 %. Based on performance analysis, the structures according to PBPD shows a better level of performance to the application of SNI 1726: 2012 thrust load.

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Predictive Analytics of In-Service Bridge Structural Performance from SHM Data Mining Perspective: A Case Study

Shock and Vibration ◽

10.1155/2019/6847053 ◽

2019 ◽

Vol 2019 ◽

pp. 1-11 ◽

Cited By ~ 2

Author(s):

Qiwen Jin ◽

Zheng Liu ◽

Junchi Bin ◽

Weixin Ren

Keyword(s):

Data Mining ◽

Performance Analysis ◽

Early Warning ◽

Predictive Analytics ◽

Structural Response ◽

Steel Tube ◽

Structural Performance ◽

Online Analysis ◽

Analysis Methods ◽

Load Tests

In-service bridge structural performance analysis and prediction are usually complicated and challenging because of many unknown and uncertain factors. Contrary to the traditional structural appearance inspections and load tests, structural health monitoring (SHM) can provide a perspective for online analysis, prediction, and early warning. So far, SHM has been widely used in many bridge structures, and a lot of bridge SHM data have also been collected. However, the existing studies usually focus on some independent and unsystematic analysis methods, which are hard to use widely in engineering applications to reveal the overall structural performance. This study focuses on the structural performance analysis and prediction of the highway in-service bridge. The dynamic problems in bridge SHM are pointed out firstly, followed by a detailed analysis about the characteristics of bridge SHM data. With the consideration of different characteristics, three targeted analysis methods are proposed. An urban concrete-filled steel tube (CFST) truss girder bridge (opened to traffic in 1995) is also presented, which once experienced some prominent vibration problems. The bridge SHM system is designed and stalled after several appearance inspections, load tests, and some reinforcement measures. The data mining methods proposed (distribution function, association analysis, and time-series analysis) are employed for the analysis and prediction of structural response and deterioration extent. This study can provide some references for maintenance and management and can also build a foundation for further online analysis and early warning.

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