How smart sensoring improves tunnel resilience: from theoretical model to future application
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Published:2016
D.M. Zhang, H.W. Huang, Q.F. Hu, Y.J. Zhang, 2016. "How smart sensoring improves tunnel resilience: from theoretical model to future application", Transforming the Future of Infrastructure through Smarter Information: Proceedings of the International Conference on Smart Infrastructure and ConstructionConstruction, 27–29 June 2016, RJ Mair, K Soga, Y Jin, AK Parlikad, JM Schooling
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ABSTRACT
Preventive maintenance has gained more and more attentions since tunnel performance would inevitably degrade against time. It is generally accepted that smart sensoring could in some way assist in the decision for preventive maintenance. However, the timing and cost-benefit when using smart sensoring is quite vague. With regard to this circumstance, applying resilience analysis for tunnels could evaluate the effectiveness of smart sensoring rigorously. The resilience is explained conceptually as the ability of a tunnel to absorb the disruption and the ability to recover to the acceptable performance level. Using the framework of resilience model proposed by the authors recently, this paper illustrates explicitly the timing and cost-benefit in using the smart sensoring to improve the tunnel resilience. It has been derived that if the response time to disruption when applying smart sensoring were n times faster than the time using the traditional monitoring technique, the loss of tunnel resilience could be n2 times less than the loss in traditional way. The merit of using smart sensoring for tunnel resilience is thus numerically appreciated. Furtherly, preliminary study on resilience-based strategies for two types of repair works for tunnel is presented. One is the repair for disrupted tunnel subjected to unexpected extreme disruption and the other is repair for preventive maintenance under the condition of degradation of tunnel performance in long-term. The time duration and cost-benefit have been included in this design where the multi-objective optimization is applied.
