Long term health monitoring systems for bridges

2007 ◽  
Author(s):  
Ming L. Wang
Keyword(s):  
Health Monitoring ◽  
Monitoring Systems ◽  
Health Monitoring Systems

Life-cycle management cost analysis of transportation bridges equipped with seismic structural health monitoring systems

2021 ◽  
pp. 147592172199662
Author(s):  
Michela Torti ◽  
Ilaria Venanzi ◽  
Simon Laflamme ◽  
Filippo Ubertini
Keyword(s):  
Life Cycle ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Life Cycle Cost ◽  
Monitoring Systems ◽  
Health Monitoring System ◽  
Structural Health ◽  
Structural Health Monitoring System ◽  
Health Monitoring Systems

Life-cycle cost analysis is an approach that has gained popularity for assisting the design of civil infrastructures. The life-cycle cost analysis approach can be leveraged for structures equipped with structural health monitoring systems in order to quantify the benefits of the technology and de facto support its long-term implementation. However, for new structures, the long-term assessment of the expected value of the total investment cost, in terms of the current worth at the design time, is still the focus of ongoing research due to unknowns and uncertainties on the impact of the structural health monitoring system on long-term structural performance. This article proposes a new combined model of life-cycle cost formulation and simulation methodology for the long-term financial assessment of transportation bridges equipped with seismic structural health monitoring systems, in order to evaluate the total costs and benefits offered by such monitoring systems for post-seismic assessments. The formulation characterizes the time evolution of bridge management cost terms, highlighting the most sensitive parameters. The simulation methodology allows to quantitatively weigh each maintenance action on the total cost based on when the action is performed. The model is used to compare structures managed by the traditional approach of post-earthquake inspection versus those managed by a condition-based approach enabled by structural health monitoring systems. The originality of the model empowers the comparison by payback time, defined as the break-even point between costs and benefits of a structural health monitoring system, as well as by economic gain, defined as the difference between the total costs of an unmonitored versus a monitored structure through the end of service life. The proposed model is demonstrated through parametric analyses on a case study consisting of a continuous steel-concrete composite bridge, where the structural health monitoring system is used to monitor the elastic limit state condition of bending forces in piers during the earthquake.

scholarly journals Electrical Resistance Prediction for Functionalized Multi-Walled Carbon Nanotubes/Epoxy Resin Composite Gasket under Thermal Creep Conditions

Materials ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 2704 ◽  
Author(s):  
Wenlong Wang ◽  
Xia Yue ◽  
He Huang ◽  
Chao Wang ◽  
Diwei Mo ◽  
...  
Keyword(s):  
Carbon Nanotubes ◽  
Epoxy Resin ◽  
Health Monitoring ◽  
Resin Composite ◽  
Monitoring Systems ◽  
Epoxy Resin Composite ◽  
Multi Walled Carbon Nanotubes ◽  
Health Monitoring Systems ◽  
Walled Carbon Nanotubes

Carbon nanotube-based conductive polymer composites (CPC) showed great potentials for self-sensing and in situ structural health monitoring systems. Prediction of the long-term performance for such materials would be a meaningful topic for engineering design. In this work, the changing behavior of the long-term resistance of a multi-walled carbon nanotubes/epoxy resin composite gasket was studied under different temperature and loading conditions. Glass transition strongly influenced the resistance behavior of the composite during the thermal creep process. Similar to classical Kelvin–Voigt creep model, a model considering both the destruction and recovery processes of the conductive network inside the CPC was established. The long-term resistance variation can be predicted based on the model, and the results provided here may serve as a useful guide for further design of smart engineering structural health monitoring systems.

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