• About SRES2015-Taiwan
  • Schedule & Program
    • Daily Program
  • Organizing Committees
    • Chairs
    • Steering Committee
    • International Scientific Committee
    • Local Organizing Committee (LOC)
  • Keynote Speakers
  • Proceedings
  • Gallery
    • 10.21 WED
    • 10.22 THU
    • 10.23 FRI
  • Registration
    • Abstract Submission
    • Full Papers Submission
    • Online Registration >
      • Registration Now
  • Organized Session
  • Accommodation
  • Organizer & Sponsors
  • Venue Map
  • Contact Us
图片

Keynote Speakers

Picture
  Alfredo H-S. Ang
 
NAE Member, Emeritus Professor
  University of California, USA


  Title of Speech:

   An Intuitive Basis of the Probability Density Evolution Method (PDEM) for Stochastic  
​  Dynamics

Abstract

The recently developed Probability Density Evolution Method (PDEM) is described in intuitive terms in order to permit a better understanding and wider application of the PDEM in practical engineering problems, particularly for assessing the risk and reliability of large and complex engineering systems. In implementation, the PDEM is similar, in a limited sense, to the basic Monte Carlo simulation (MCS) in that it also requires deterministic response solutions of a system. However, in principle and in theory it is very different from the MCS. The practical effectiveness of the PDEM is emphasized and illustrated.


图片
Jie Li
Distinguished Professor
Tongji University, China


Title of Speech:

A Quantitative Approch to Stochastic Dynamic Stability of structures

Abstract

In this lecture, a quantitative approach is proposed for stochastic dynamic stability analysis of structures. The classical concept of stochastic dynamic stability is firstly revisited and a new criterion of dynamic stability of structures is introduced. The generalized density evolution equation for probability-dissipated system is derived according to the principle of preservation of probability. On the basis, the probability of stability/instability can be obtained via solving this equation by introducing the physical mechanism of dynamic instability of structures. According to the obtained probability, it is readily applicable to quantitatively evaluate the dynamic stability of structures in the sense of stability in probability 1 or stability in a given probability. The effectiveness of the proposed approach is verified by typical examples for dynamic stability analysis. Figure 1 shows a two-bar truss structure for stochastic dynamic stability analysis. Numerical investigation indicates that the stability boundary hinges upon the probabilistic levels of stability; say Figures 2 and 3.

Fig. 1. The deformed truss.   Fig. 2. Stability boundary in the sense of stability in probability 1.   Fig. 3. Stability boundary in the sense of stability in probability.
Reference
Bažant ZP, Cedolin L. 2010. Stability of Structures: Elastic, Inelastic, Fracture and Damage Theories.World Scientific Publishing Company Incorporated.
Xie WC. 2006. Dynamic stability of structures. Cambridge University Press.
Xu J, Li J. 2015. An Energetic Criterion for Dynamic Instability of Structures under Arbitrary Excitations. International Journal of Structural Stability and Dynamics, 15(2): 1-32.
Li J, Chen JB. 2009. Stochastic Dynamics of Structures. John Wiley & Sons.
Li J, Chen JB. 2008. The principle of preservation of probability and the generalized density evolution equation. Structural Safety, 30(1): 65-77.
Li J, Xu J. 2014. A new probability dissipated system and its control equation. in The 17th working conference of the IFIP Working Group 7.5 on Reliability and Optimization of Structural Systems (IFIP2014)2014. Huangshan.


Picture
Bruce R. Ellingwood
NAE Member
Colorado State University, USA
Title of Speech:
Life-cycle performance goals for civil infrastructure: managing risk
in an era of climate change

Abstract

Civil infrastructure facilities play a central role in the economic, social and political health of modern society and must maintain their safety, integrity and functionality at manageable cost over their service lives. Such facilities are susceptible to aging, which is stochastic in nature and makes the reliability of affected facilities time-dependent [Mori and Ellingwood, 1993]. The presence of climate change complicates time-dependent reliability assessment significantly. Previous time-dependent assessments of reliability and life-cycle cost have treated the operational and environmental demands as stationary in nature [Lee and Ellingwood, 2013]. This assumption is not tenable when climate change effects are considered [Bjarnadottir, et al, 2011]. Moreover, it is commonly understood that buildings, bridges and similar infrastructure should perform their intended functions for service lives of approximately 50 to 75 years. For certain civil infrastructure projects (e.g,, large dams, critical flood-control structures, toxic waste repositories), the required service periods might be substantially longer. Such considerations extend time- dependent reliability assessments and the potential consequences of engineering decisions to future generations, far beyond customary budget cycles for public investment [Corotis, 2009]. Improved decision-theoretic approaches and life-cycle engineering models are required for steering the investments of public funds that are necessary to construct and maintain civil infrastructure for such extended service periods.

A number of key questions must be addressed to extend life-cycle reliability assessment and risk- informed decision-making to consider the imperatives of equitable intergenerational transfers of risk in an era of climate change. Among these questions: How should one deal with the non-stationarity in natural hazards that arises as a consequence of climate change and in structural behavior that arises from aging? How should these uncertainties be integrated in time-dependent structural reliability analysis to estimate future behavior and to demonstrate compliance with performance objectives for civil infrastructure expressed on reliability terms? How does one deal with life-cycle cost issues that arise when extending life-cycle engineering analysis beyond its traditional time frames of application to periods extending over several generations? This keynote lecture will focus on these key questions and offer some possible answers, touching upon common degradation mechanisms, practical time-dependent reliability analysis tools, and perspectives on risk that are germane to ensuring equity in civil infrastructure decision-making over multi-generational time frames.

References

Bjarnadottir, S., Li, Y. and Stewart, M.G. (2011). A Probabilistic-based Framework for Impact and
Adaptation Assessment of Climate Change on Hurricane Damage Risks and Costs. Struct. Safety, 33(3), 173-185.

Corotis, R.B. 2009. Societal issues in adopting life-cycle concepts within the political system. Struct. & Infrast. Engrg. 5(1): 59-65.

Lee, J.Y. & Ellingwood, B.R. Intergenerational risk-informed decision framework for civil infrastructure. Proc. Int. Conf. on Structural Safety and Reliability, New York, NY, 16-20 June 2013.

Mori, Y., & Ellingwood, B. R. (1993). Reliability-based service-life assessment of aging concrete structures. J. Struct. Engrg. ASCE 119(5), 1600-1621.


Picture
Dan M. Frangopol
Professor,Fazlur R. Khan Endowed Chair Professor
Lehigh University, USA

Title of Speech:
Life-Cycle Performance, Reliability, Risk, and Sustainability of
Bridges and Bridge Networks

Abstract

Infrastructure systems serve as the foundation of basic standards of living, a healthy economy, and an integrated and sustainable society. During their service life, they are subjected to progressive and/or sudden structural deterioration which can pose an imminent threat to society, economy, and surrounding environment (Adams 2006). In order to reduce the effects of such threats, it is necessary to develop effective life-cycle management strategies aimed at improving reliability, resilience and sustainability of infrastructure systems while minimizing life-cycle cost and risk. Accordingly, risk, resilience, and sustainability-informed methods are gaining increasing attention in design, assessment, maintenance, and management of infrastructure systems, especially for bridges and bridge networks (Ellingwood 2005; Cimellaro et al. 2010; Bocchini et al. 2014; Frangopol and Soliman 2015). Overall, bridge management aims to identify bridges that are mostly in need of maintenance and apply efficient strategies to best utilize limited financial resources and satisfy structural performance in terms of reliability, risk and sustainability (Dong et al. 2014). This keynote paper reviews critical aspects related to (a) investigation of bridge and bridge network life-cycle performance, reliability, risk, and sustainability, (b) evaluation of structural performance at system level in a probabilistic manner, and (c) integration of multi-criteria optimum bridge and bridge network management strategies in risk-informed decision making. Life-cycle management of bridges and bridge networks integrating reliability, risk, and sustainability under the effects of gradual structural deterioration and sudden hazards is discussed. Additionally, maintenance and retrofit multi-criteria optimization is presented through a probabilistic management framework (Jiménez et al. 2003; Frangopol 2011). This framework assists in optimal decision making and risk mitigation for bridges and bridge networks.

 

<REFERENCES>

Adams, W.M. 2006. The future of sustainability: re-thinking environment and development in the twenty-first century. Report of the IUCN Renowned Thinkers Meeting, 29–31.

Bocchini, P., Frangopol, D.M., Ummenhofer, T., & Zinke, T. 2014. Resilience and sustainability of the civil

infrastructure: Towards a unified approach. Journal of Infrastructure Systems 20(2), 04014004, 1-16. Cimellaro, G. P., Reinhorn, A. M., & Bruneau, M. 2010. Framework for analytical quantification of disaster

resilience. Engineering Structures 32(11), 3639-3649.
Dong, Y., Frangopol, D.M., & Saydam, D. 2014. Pre-earthquake probabilistic retrofit optimization of bridge

networks based on sustainability. Journal of Bridge Engineering 19(6), 04014018, 1-10.
Ellingwood, B. R. 2005. Risk-informed condition assessment of civil infrastructure: State of practice and

research issues. Structure and Infrastructure Engineering 1(1), 7–18.
Frangopol, D.M. 2011. Life-cycle performance, management, and optimization of structural systems under

uncertainty: Accomplishments and challenges. Structure and Infrastructure Engineering 7(6), 389-413. Frangopol, D.M., & Soliman, M. 2015. Life-cycle of structural systems: Recent achievements and future

directions. Structure and Infrastructure Engineering, doi:10.1080/15732479.2014.999794 (in press). Jiménez, A., Ríos-Insua, S., & Mateos, A. 2003. A decision support system for multi-attribute utility evaluation

based on imprecise assignments. Decision Support Systems 36(1), 65–79.


Picture
Bilal M. Ayyub
Professor
University of Maryland, USA


Title of Speech:
Practical Resilience Metrics for Critical Infrastructure

Abstract

Natural disasters in 2011 alone resulted in $366 billion (2011 US$) in direct damages and 29,782 fatalities worldwide. Storms and floods accounted for up to 70% of the 302 natural disasters worldwide, with earthquakes producing the greatest number of fatalities. Managing these risks rationally requires an appropriate definition of resilience and associated metrics. This paper provides a resilience definition that meets a set of requirements with clear relationships to reliability and risk as key relevant metrics. The resilience definition proposed is of the intension type, which is of the highest order. Resilience metrics are reviewed, and simplified ones are proposed to meet logically consistent requirements drawn from measure theory. Such metrics provide a sound basis for the development of effective decision-making tools for multihazard environments. The paper also examines recovery, with its classifications based on level, spatial, and temporal considerations. Three case studies are developed and used to gain insights to help define recovery profiles. Two recovery profiles, linear and step functions, are introduced. Computational examples and parametric analysis illustrate the reasonableness of the metrics proposed.


This keynote is based on the paper
Ayyub, B. M., “Practical Resilience Metrics for Planning, Design, and Decision Making,” DOI: 10.1061/AJRUA6.0000826. © 2015 American Society of Civil Engineers.


Picture
Ding-Fu Su 蘇丁福
Deputy Commissioner副局長
Department of Rapid Transit Systems, Taipei City Government, Taiwan

臺北市政府捷運工程局    
Title of Speech:
Quality management, Risk Management & Engineering insurance during the construction of the  Taipei Rapid Transit Systems
​                                
台北捷運系統之品質管理, 風險管理及工程保險


Picture
Wanchi Huang 黃琬淇
Environmental Engineer 環境工程一部工程師
Sinotech Engineering Consultants, Ltd., Taiwan
中興工程顧問股份有限公司

Title of Speech:
Carbon management for road infrastructure – carbon footprint inventory of the Suhua Highway Improvement Project
                                   公路工程碳管理制度之發展 - 以蘇花改工程碳管理計畫為例

Abstract

Carbon reduction and energy saving strategies are concerned in both global and national policy in accordance with the increasing impact of climate change. The Directorate General of Highways, MOTC, which is in charge of highway engineering in Taiwan, proactively took the responsibility to promote low carbon infrastructure. The carbon management cycle for road construction was proposed and first practiced on the Suhua Highway Improvement Project. During the planning and design stage, hot spot of carbon emission within the project were discussed and corresponding carbon reduction alternatives were evaluated.


In order to testify the information that was found through the carbon emission estimation result, real data were planned to be collected and analyzed to help further carbon management tasks. The carbon footprint inventory for major road construction was followingly practiced in the Suhua Highway Improvement Project. According to the daily records and annual inventory pre-verification reports, the amount of carbon emission was continually inspected and calculated. Various characteristics of carbon emission were identified by the amount generated from different types of structure, construction techniques and environmental condition. More useful information can be expected through the transparent process to accumulate data.


An overall report is expected to give a full and clear picture about how to make effective carbon management for major road infrastructure. All the results will be the firm basis for future road construction projects to understand and handle their carbon footprint more efficiently and effectively. The carbon management methodology and experiences from the Suhua Highway Improvement Project may attract other construction administrations to emphasize on carbon emission and reduction issue. Low carbon construction will become another force to shift our community and country become a low carbon society progressively.

 

Powered by Create your own unique website with customizable templates.