Tài liệu Congress on technical advancement 2017 infrastructure resilience and energy

Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/04/19. Copyright ASCE. For personal use only; all rights reserved. Congress on Technical Advancement 2017 Infrastructure Resilience and Energy Proceedings of the Congress on Technical Advancement 2017 Duluth, Minnesota September 10–13, 2017 Edited by Jon E. Zufelt, Ph.D., P.E., D.WRE Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/04/19. Copyright ASCE. For personal use only; all rights reserved. CONGRESS ON TECHNICAL ADVANCEMENT 2017 INFRASTRUCTURE RESILIENCE AND ENERGY PAPERS FROM SESSIONS OF THE FIRST CONGRESS ON TECHNICAL ADVANCEMENT September 10–13, 2017 Duluth, Minnesota SPONSORED BY Committee on Technical Advancement Aerospace Engineering Division Cold Regions Engineering Division Committee on Adaptation to a Changing Climate Energy Division Forensic Engineering Division Infrastructure Resilience Division Construction Institute Duluth Section of ASCE Utility Engineering and Surveying Institute of the American Society of Civil Engineers EDITED BY Jon E. Zufelt, Ph.D., P.E., D.WRE Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/04/19. Copyright ASCE. For personal use only; all rights reserved. Published by American Society of Civil Engineers 1801 Alexander Bell Drive Reston, Virginia, 20191-4382 www.asce.org/publications | ascelibrary.org Any statements expressed in these materials are those of the individual authors and do not necessarily represent the views of ASCE, which takes no responsibility for any statement made herein. No reference made in this publication to any specific method, product, process, or service constitutes or implies an endorsement, recommendation, or warranty thereof by ASCE. The materials are for general information only and do not represent a standard of ASCE, nor are they intended as a reference in purchase specifications, contracts, regulations, statutes, or any other legal document. ASCE makes no representation or warranty of any kind, whether express or implied, concerning the accuracy, completeness, suitability, or utility of any information, apparatus, product, or process discussed in this publication, and assumes no liability therefor. 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Congress on Technical Advancement 2017 iii Preface Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/04/19. Copyright ASCE. For personal use only; all rights reserved. The Congress on Technical Advancement was established to bring together several of the Divisions under the ASCE Board-level Committee on Technical Advancement (CTA) at a single venue. While some of the CTA Divisions hold regular small conferences, others do not have an established forum to present technical information to their constituents or the engineering community. One of the goals of the Congress is to provide greater opportunities for interaction and synergy among the activities of the Divisions and ASCE’s Institutes. This 1st Congress on Technical Advancement was held at the Duluth Entertainment and Convention Center in Duluth, Minnesota on September 10-13, 2017. This 1st Congress included the participation of and presentations by the Aerospace Engineering Division, Cold Regions Engineering Division, Committee on Adaptation to a Changing Climate, Energy Division, Forensic Engineering Division, Infrastructure Resilience Division, the Construction Institute (CI), and the Utilities Engineering and Surveying Institute (UESI), representing the combination of existing conference series as well as opportunities for new periodic technical symposia. The Congress was hosted by the Duluth Section of ASCE as they celebrated their 100th Anniversary with a special session and evening social event. The 2017 Congress on Technical Advancement included 3 days of presentations with daily plenary sessions followed by 6 parallel tracks of technical sessions providing a venue for over 160 presentations. The conference also included an Awards Luncheon highlighted by the presentation of the Harold R. Peyton Award for Cold Regions Engineering, the CANAM Civil Engineering Amity Award, the Charles Martin Duke Lifeline Earthquake Engineering Award and the Alfredo Ang Award on Risk Analysis and Management of Civil Infrastructure. Other recognitions during the Congress include the Eb Rice Lecture Award, the Best Journal of Cold Regions Engineering Paper Award, and the Best Cold Regions Conference Paper Award. An Opening Congress Reception, Duluth Section 100th Anniversary Session and Social Event, and Technical Tours provided additional opportunities for attendees to share ideas. This collection of 60 papers brings together the current state of knowledge on a variety of topic areas presented at the 2017 Congress on Technical Advancement and is separated into three EBooks. The first represents selected papers from the Proceedings of the 17th International Conference on Cold Regions Engineering. The second includes the papers on Infrastructure Resilience, Aerospace and Energy. The third EBook presents papers addressing Construction and Forensic Engineering. I would like to thank all of the volunteers and ASCE Staff who have made this 1st Congress on Technical Advancement and Proceedings possible. It could not have been done without all of the authors, reviewers, attendees, and Congress Committee members. Jon E. Zufelt, Ph.D., PE, D.WRE, F.ASCE Congress Chair and Proceedings Editor © ASCE Congress on Technical Advancement 2017 Acknowledgments Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/04/19. Copyright ASCE. For personal use only; all rights reserved. Congress Organizing Committee Jon Zufelt, Ph.D, P.E., CFM, D.WRE, F.ASCE James Anspach, P.G. (ret.), F.ASCE Ron Anthony, Aff.M.ASCE Hiba Baroud, Ph.D., Aff.M.ASCE Ana Boras, Ph.D, P.E., M.ASCE Martin Derby, A.M.ASCE Mike Drerup, P.E., M.ASCE Jim Harris, P.E., Ph.D, F.SEI, F.ASCE, NAE John Hinzmann, P.E., M.ASCE Jen Irish, Ph.D, P.E., D.CE, M.ASCE John Koppelman, A.M.ASCE Tom Krzewinski, P.E., D.GE, F.ASCE Bob Lisi, P.E., M.ASCE Juanyu "Jenny" Liu, Ph.D., P.E., M.ASCE Pat McCormick, P.E., S.E., F.ASCE, F.SEI Nick Patterson, P.E., M.ASCE David Prusak, P.E., M.ASCE Ziad Salameh, P.E., M.ASCE J. "Greg" Soules, P.E., S.E., P.Eng, SECB, F.SEI, F.ASCE Amy Thorson, P.E., F.ASCE Nasim Uddin, P.E., F.ASCE Joel Ulring, P.E., M.ASCE ASCE Staff Susan Davis, A.M.ASCE Jon Esslinger, PE, F.ASCE, CAE Mark Gable Katerina Lachinova Shingai Marandure Amanda Rushing, Aff.M.ASCE Jay Snyder, Aff.M.ASCE Catherine Tehan, Aff.M.ASCE Drew Caracciolo © ASCE iv Congress on Technical Advancement 2017 v Proceedings Reviewers Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/04/19. Copyright ASCE. For personal use only; all rights reserved. Il-Sang Ahn Lorenzo Allievi Ron Anthony Navid Attary Bilal Ayyub Eugene Balter Heather Brooks Henry Burton ZhiQiang Chen Adrian Chowdhury Edwin Clarke Billy Connor Craig Davis An Deng Alicia Diaz de Leon Curt Edwards Jon Esslinger Caroline Field Madeleine Flint Chris Ford Warren French Subhrendu Gangopadhyay Rob Goldberg Scott Hamel John Henning Jiong Hu Baoshan Huang © ASCE Josh Huang Joshua Kardon Mehrshad Ketabdar John Koppelman Thomas Krzewinski David Lanning Spencer Lee Jenny Liu Hongyan Ma Rajib Mallik Tony Massari Roberts McMullin Ralph Moon Anthony Mullin Mark Musial LeAnne Napolillo Kevin Orban Sivan Parameswaran Tim Parker Robert Perkins Brian Phillips Chris Poland Allison Pyrch Craig Ruyle Bill Ryan Stephan Saboundjian Ziad Salameh Andrea Schokker Yasaman Shahtaheri Jim Sheahan Xiang Shu John Smith Ryan Solnosky Greg Soules Bucky Tart Scott Tezak Ganesh Thiagarajan Eric Thornley John Thornley Jeff Travis Nasim Uddin Joel Ulring Shane Underwood Cindy Voigt Dan Walker Haizhong Wang Chenglin Wu Gang Xu Zhaohui Yang Kent Yu John Zarling Chris Zawislak Weiguang Zhang Jon Zufelt Congress on Technical Advancement 2017 vi Contents Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/04/19. Copyright ASCE. For personal use only; all rights reserved. Testing an Optimization/Simulation Model for the Real-Time Operations of Water Distribution Systems under Limited Power Availability ........................ 1 Puneet Khatavkar and Larry W. Mays Risk-Based Assessment of Texas Bridges to Natural and Induced Seismic Hazards ...................................................................................................................... 10 Farid Khosravikia, Vyacheslav Prakhov, Andy Potter, Patricia Clayton, and Eric Williamson A Retrospective Analysis of Hydraulic Bridge Collapse ....................................... 22 Cristopher Montalvo and Wesley Cook Black Sky Hazards: Resilience Planning ................................................................ 29 John F. Organek Water Supply Damage, Recovery, and Lifeline Interaction in an Earthquake Sequence ............................................................................................... 41 Keith Porter, Serge Terentieff, Roberts McMullin, and Xavier Irias Balanced Lifeline System Resilience: Collaborative Convening Platforms in the San Francisco Bay Area............................................................... 53 Michael Germeraad Performance of Interdependent Lifelines in the Pacific Northwest Resulting from an Earthquake on the Cascadia Subduction Zone: A Portland Example ..................................................................................................................... 62 Michael Saling and Michael Stuhr Mapping Slope-Failure Susceptibility for Infrastructure Management ............. 69 Omid Mohseni, Mike Strong, Aaron T. Grosser, Charles Hathaway, and Aaron M. Mielke City of Portland Water Bureau Business Continuity Plan (BCP)/Continuity of Operations (COOP) Case Study .......................................... 79 Teresa Elliott, Kim Anderson, Kent Yu, Jamaal Folsom, and Mary Ellen Collentine Post-Earthquake Restoration Modelling of a Railway Bridge Network ............. 91 Sushreyo Misra and Jamie E. Padgett © ASCE Congress on Technical Advancement 2017 Permafrost-Supported Linear Infrastructure Risk Analysis Software: Design and Goals..................................................................................................... 104 Heather Brooks, Guy Doré, and Ariane Locat Jr. Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/04/19. Copyright ASCE. For personal use only; all rights reserved. The Core Competencies of Resilience ................................................................... 116 Richard A. Fosse Experimental Characterization of Hazard-Resilient Ductile Iron Pipe Soil/Structure Interaction under Axial Displacement ......................................... 124 Brad P. Wham, Chalermpat Pariya-Ekkasut, Christina Argyrou, Addie Lederman, Thomas D. O’Rourke, and Harry E. Stewart A Longitudinal Study of Tohoku Telecommunication Network Three Years after the Great East Japan Earthquake and Tsunami ............................. 135 Alex Tang, Alexis Kwasinski, and Kent Yu Blast Analysis of Aging Transportation Structures with Little Stand-Off Distance .................................................................................................................... 143 Yongwook Kim, Salvatore Florio, and Qian Wang Modular Preference Function Development Strategy for the Design of Multi-Hazard Resilient and Sustainable Buildings ............................................. 152 Yasaman Shahtaheri, Jesus M. de la Garza, and Madeleine M. Flint United States Energy Issues ................................................................................... 165 Sam Bandimere © ASCE vii Congress on Technical Advancement 2017 Testing an Optimization/Simulation Model for the Real-Time Operations of Water Distribution Systems under Limited Power Availability Puneet Khatavkar1 and Larry W. Mays, F.ASCE2 Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/04/19. Copyright ASCE. For personal use only; all rights reserved. 1 Graduate Student, School of Sustainable Engineering and the Built Environment, Arizona State Univ., Tempe, AZ 85257-5306. E-mail: [email protected] 2 Professor, School of Sustainable Engineering and the Built Environment, Arizona State Univ., Tempe, AZ 85257-5306. E-mail: [email protected] Abstract This paper presents a new methodology for the real-time operation of water distribution systems under critical conditions of limited electrical energy due to emergencies such as extreme drought conditions, electric grid failure, and other severe conditions related to natural conditions. The basic objective of optimizing the operations of a water distribution system under limited availability of energy is to attempt to satisfy the required levels of demand at various locations while meeting pressure requirements of the system and maintaining an optimal pump schedule. The approach adopted here is to interface an optimization procedure (genetic algorithm) with a simulator (EPANET) in the framework of an optimal control problem to determine the real-time optimal operation of a water distribution system under limited electrical input. The genetic algorithm (GA) searches over pump operation in combination with maximizing the demands that can be satisfied with limited energy input. The interfacing of the simulator and the genetic algorithm has been accomplished within the framework of MATLAB. INTRODUCTION The interdependencies between the electric power system and the water distribution utility systems have long been recognized. These two systems, collectively, are known informally as the water-energy nexus. The interaction between these two critical infrastructures are being studied using a coupled, time-domain simulation. The ultimate desired need would be for the actual systems to exchange real time data. Software Defined Networking (SDN) could be used to represent the communication overlay implemented via a middleware architecture (see Figure 1). This overlay enables a reliable and efficient data exchange between the two otherwise isolated supervisory control and data acquisition (SCADA) systems. This is the ultimate measure of how the two systems behave when subjected to various disturbances in either system as well as under conditions of long-term water shortages. The control actions undertaken in both networks represent an improvement over the current implementations due to the increased situational awareness resulting from the exchanged information. Real-time Operation Framework during Limited Electrical Energy Input The basic objective of optimizing the operations of a water distribution system under limited availability of energy is an attempt to satisfy the required levels of demand at various locations while also meeting pressure requirements of the system. The objective statement formulated here considers a particular required or desired demand. The following describes the overall concept of the real-time operation. © ASCE 1 Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/04/19. Copyright ASCE. For personal use only; all rights reserved. Congress on Technical Advancement 2017 1. At real-time t receive electrical energy input from electrical system. 2. Receive latest data (status of pumps, tank levels, status of valves, and flows in and out of the system from the SCADA system. 3. Update the EPANET water distribution system (WDS) input using data from the SCADA system. 4. WDS optimization model is run to determine the actual demand pattern and pump operation that can be met with the limited electrical energy input. During the optimization/simulation process the EPANET model is used repeatedly within the genetic algorithm optimization to determine the status of the network over the next 24 hours. The optimization model is searching over the decision variables which are the pump operation and the demand pattern that can be satisfied during the limited electrical energy input. The simulator is determining the values of the state variables (nodal pressure heads, pipe flows, and tank levels) for each set of control variables determined in the optimizer. 5. Implement the optimal pump schedule over the next one hour only which is accomplished through the SCADA system. 6. Repeat steps 1 through 5 continuously during the emergency event each time incrementing the time t = t + ∆t in which case ∆t = 1 hour. MODELLING APPROACH The overall model interface between the genetic algorithm (WDS optimization model) and the EPANET (Rossman, 2000) simulator is accomplished using MATLAB as shown in Figure 2. The interface between MATLAB and EPANET is performed using the open-source EPANETMATLAB toolkit (Eliades, 2016). The interface facilitates use of all the functionalities in the original EPANET code written in C++ language within the MALTAB environment by passing the various commands to and from the MATLAB mathematical language to the EPANET simulator. This toolkit was used in conjunction with genetic algorithm (GA) in MATLAB to accomplish an overall optimization/ simulation methodology for a WDS. The methodology works through an interface facilitating data exchange between three systems viz. optimization model (reduced problem), WDS Supervisory Control and Data Acquisition (SCADA) system and a WDS hydraulic simulator (EPANET) as shown in Figure 2. Figure 3 is a schematic of the overall simulation/optimization procedure being implemented for every unit time period for the entire simulation period. Starting at the first time period, the optimization/simulation model in MATLAB receives inputs from the power optimization/simulation model including observed and predicted power availability schedule for each time period and data from WDS SCADA system including pressure heads, discharges and tank levels as well as pump status at various nodes and links in the system. The optimization/simulation model for WDS is then run with the updated information to obtain the pump controls and demand satisfaction multipliers for the next time period, several times until the GA stopping conditions are met. The solution of the GA is then sent to the power optimization/simulation model and the WDS SCADA system as inputs for the next time period. This process is continued till the last time period for simulation is reached. © ASCE 2 Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/04/19. Copyright ASCE. For personal use only; all rights reserved. Congress on Technical Advancement 2017 Figure 1 Real-time operation model for power and water systems operation 3 Figure 2 Optimization framework for WDS operation Figure 3 Schematic of the overall simulation/optimization procedure © ASCE Congress on Technical Advancement 2017 4 Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/04/19. Copyright ASCE. For personal use only; all rights reserved. EXAMPLE APPLICATION The example system is a hypothetical water distribution system (WDS) shown in Figure 4, including two cities and a hypothetical power distribution system (PDS). The PDS is based on the IEEE 14 bus system (Kodsi and Canizares, 2003), which consists of five power plants. The cooling water for these power plants is supplied from both a freshwater source and from the reclaimed wastewater waste water treatment plant (WWTP). City 1 consists of four pressure zones with a total base demand of 30,000 gpm and City 2 consists of five pressure zones with a total base demand of 25,000 gpm. A total of 17 freshwater pumps and 11 reclaimed water pumps serve the WDS. Figure 5 is a schematic of the directions of flows taking place within the system. Figure 4: Example WDS system PW1 PW2 PW3 PW4 Power Plant PW PW5 Pumping Station P Check valve P Water line P Sewer line P P P Source P P P P P P WWTP City 1 City 2 Figure 5 Schematic of water supply system © ASCE P 5 City 1 has four pressure zones designated as nodes 1.1 – 1.4, and city 2 consists of five pressure zones designated as nodes 2.1 – 2.5. Each pressure zones has a separate demand pattern (Figure 6). The WDS has six demand patterns. One pattern applies to all the residential zones within the cities and separate demand patterns are provided for each of the 5 five power plants in the system. Pipes in the WDS are categorized into three types. The first type includes main lines (ML1 – ML7), which connect the freshwater pumps to the various power plants and cities. The second type is intermediate lines (IL 1.1 – IL 1.4 and IL 2.1 – IL 2.5), which are interconnecting nodes within the cities. The reclaimed water pipelines (RW1 – RW5) are the pipes connecting the waste water treatment plants to the five power plants in the system respectively. The pumps supporting the WDS are categorized as fresh water pumps (WP1 – W7 series) and reclaimed water pumps (RWP1 – RWP5 series). Five types of pumps are used in the system with each with different pump curves. Demand patterns for test system 1.8 1.6 Demand Multiplier Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/04/19. Copyright ASCE. For personal use only; all rights reserved. Congress on Technical Advancement 2017 1.4 1.2 1 0.8 0.6 0.4 0.2 0 1 6 11 16 21 Time (hour) Demand Pattern 1 Demand Pattern 2 Demand Pattern 3 Demand Pattern 4 Demand Pattern 5 Demand Pattern 6 Figure 6. Demand patterns for example system The simulation – optimization procedure illustrated in Figure 3 was applied to the operation of the example system under conditions of limited power availability at a few pumps in the system during a 24-hour real-time simulation run of the system. Table 1 gives the details of the pumps affected due to the limited power availability at these pumps. The power input was received as an input from the power simulation – optimization model. © ASCE Congress on Technical Advancement 2017 6 Table 1. Details of pumps affected by limited power availability Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/04/19. Copyright ASCE. For personal use only; all rights reserved. Pump # WP 4.1 WP 4.2 WP 4.3 RWP 1.1 RWP 1.2 RWP 2.1 RWP 2.2 RWP 2.3 Time of Power Outage (Hrs.) 3:00 to 8:00 3:00 to 8:00 3:00 to 8:00 3:00 to 6:00 3:00 to 6:00 3:00 to 6:00 3:00 to 6:00 3:00 to 8:00 Power Available (kw-hr) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Consumer Served City 1 City 1 City 1 Power Plant (PW1) Power Plant (PW1) Power Plant (PW2) Power Plant (PW2) Power Plant (PW2) RESULTS OF EXAMPLE APPLICATION The results of application of the optimization – simulation model to the example system for a scenario of limited power availability at certain pumps for a certain time during real – time operations simulation for a 24-hour period are presented in Figures 7 and 8. Figures 7 (a) – (f) show the time series of the power available and the power consumed by pumps WP4.1, WP4.2, WP4.3, RWP1.1, RWP1.2, RWP2.1, RWP2.2 and RWP2.3 respectively. Figures 8 (a) – (f) show the required and supplied demand multipliers for residential zones of city 1 and city 2 and the five power plants respectively. It could be observed from Figures 7 (a) – (f) that the trends of power consumption closely follow the trends of power availability and the power consumed consumption never exceeds the availability. During the periods of power outage (listed in Table 1) the pumps are switched off, as indicated from the power consumption in Figure 7. The methodology aims at minimizing the demand deficit (difference between the required and supplied demands) for various consumer points in the system. The resulting demand pattern multipliers shown in Figure 8 (a) – (b) depict minimal deficits between the required and supplied demands. An average demand satisfaction of 91 percent was observed in this application. The maximum demand deficits are experienced during the period of power outages, while a few could be observed during non-power outage hours of the day. Changes in the pumping schedule due to the power outage, leads to adverse effects on the water storage in the system. Scarcity of stored water in the system causes low pressures at the nodes, leading to lower pressure bound violations. Demand deficits could be observed (in Figures 8 (b), (c) and (d)) even during the non-outage period, since the optimization model attempts to reduce the pressure bound violations by reducing the demands in that particular zone. © ASCE Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/04/19. Copyright ASCE. For personal use only; all rights reserved. Congress on Technical Advancement 2017 (a) Pump WP4.1 (c) Pump WP 4.3 (e) Pump RWP 1.2 © ASCE 7 (b) Pump WP4.2 (d) Pump RWP 1.1 (f) Pump RWP 2.1 Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/04/19. Copyright ASCE. For personal use only; all rights reserved. Congress on Technical Advancement 2017 (g) Pump RWP 2.2 8 (h) Pump RWP 2.3 Figure 7. Power availability and consumption patterns for pumps affected by power outage (a) Pattern 1 - Residential (c) Pattern 3 – PW 3 © ASCE (b) Pattern 2 – PW 1 (d) Pattern 4 – PW4 Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/04/19. Copyright ASCE. For personal use only; all rights reserved. Congress on Technical Advancement 2017 (e) Pattern 5 – PW 5 9 (f) Pattern 6 – PW 6 Figure 8. Required and satisfied demand pattern multipliers for different consumers in the example system CONCLUSIONS The example application of the model provides evidence of applicability of the model for realtime operations of a realistic WDS under limited power availability. The demands are observed to be satisfied in the period of sufficient power availability while the demand satisfaction is curtailed in case of limited power availability time periods. It is thus concluded here that the model presented in this paper is a novel approach towards optimization of WDS operations under normal as well as limited power conditions. The methodology presented in this paper can be used in conjunction with a similar optimization/simulation model for power distribution system to obtain real time operations of a combined power – water system, thus providing a robust solution for cascading failures arising out of contingencies in any of the system. REFERENCES Elíades, D. (2009). EPANET MATLAB Toolkit. KIOS, University of Cyprus. Nocosia, Republic of Cyprus. https://www.mathworks.com/MATLIBcentral/filesexchange/25100-kios-researchepanet-MATLAB-tookit/ Kodsi, S.K.M and Canizares, C.A. (2003). Modeling and simulation of IEEE 14 bus systems with FACTS controllers. Technical Report (vol. 3), University of Waterloo, Waterloo, Canada. Rossman, L.A. (2000). EPANET-2 User’s Manual. EPA/600/R-00/057, U.S. Environmental Protection Agency, Cincinnati, USA. © ASCE Congress on Technical Advancement 2017 Risk-Based Assessment of Texas Bridges to Natural and Induced Seismic Hazards Farid Khosravikia, M.ASCE1; Vyacheslav Prakhov2; Andy Potter3; Patricia Clayton4; and Eric Williamson5 Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/04/19. Copyright ASCE. For personal use only; all rights reserved. 1 Ph.D. Student, Dept. of Civil, Architectural, and Environmental Engineering, Univ. of Texas at Austin, Austin, TX. E-mail: [email protected] 2 Graduate Student, Dept. of Civil, Architectural, and Environmental Engineering, Univ. of Texas at Austin, Austin, TX. E-mail: [email protected] 3 Graduate Student, Dept. of Civil, Architectural, and Environmental Engineering, Univ. of Texas at Austin, Austin, TX. E-mail: [email protected] 4 Assistant professor, Dept. of Civil, Architectural, and Environmental Engineering, Univ. of Texas at Austin, Austin, TX. E-mail: [email protected] 5 Professor, Dept. of Civil, Architectural, and Environmental Engineering, Univ. of Texas at Austin, Austin, TX. E-mail: [email protected] ABSTRACT The primary objective of this paper is to study the effects of natural and induced seismic hazards on Texas bridges. Predicted structural damage forms the primary performance metric for this study. The motivation for this research stems from the significant increase in the number of earthquakes greater than magnitude 3.0 in Texas over the past five years. Texas is historically known as a non-seismic region; therefore, this significant increase in seismicity raises concerns over the safety of infrastructure designed with little to no consideration of seismic demands. The bridge population is characterized by different classes, and for each class, computationally efficient nonlinear models are implemented for simulating damage intensity in non-seismically detailed bridge components. The damage level is evaluated based on deformations of bridge components, namely bridge bearings in this paper, and fragility functions representing the probability of exceeding each damage state for various bridge classes are generated. The results show that although it is not likely to have full damage of bearing components after the earthquake, it is more likely to have slight and moderate. This information will be used to inform post-earthquake inspection plans and identify the most vulnerable bridge types in terms of bearing fragility. Keywords: Risk-based assessment; Induced seismic hazards; Bridge assessment; Structural Damage © ASCE 10 Congress on Technical Advancement 2017 INTRODUCTION Beginning in 2008, the rate of earthquakes in Texas greater than magnitude 3 increased from approximately two per year in previous decades to about twelve per year (Frohlich et al. 2016; Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/04/19. Copyright ASCE. For personal use only; all rights reserved. Hornbach et al. 2016). Most of these earthquakes occurred primarily in areas nearby wastewater injection wells, and it is believed that the majority of these earthquakes are human-caused, associated with petroleum production or wastewater disposal (Frohlich et al. 2016; Hornbach et al. 2016). More importantly, these earthquakes occurred in areas that historically have had negligible seismicity, and therefore, infrastructures in these areas were designed with little to no consideration of seismic demands. Therefore, this recent increase in human-induced seismicity raises concerns over the safety of Texas infrastructure. This study addresses the vulnerability of Texas bridges to the effects of natural and induced seismic hazards. Predicted structural damage forms the primary performance metric for this study. Previous research has been done to assess seismic hazards and bridge vulnerability in regions across the Central and Eastern United States (CEUS). However, no such study had been done specifically addressing seismic vulnerability of the bridge inventory in Texas, which use different details than other CEUS states with no seismic design considerations. This is done with utilizing a probabilistic framework which considers major sources of uncertainty such as uncertainty in ground motions and local soil conditions, as well as uncertainty in design and detailing practices over past decades when the bridge population was constructed. To assess the seismic vulnerability of the bridge infrastructure in Texas, it is necessary to address two key parameters: 1) Seismic hazard. Seismic hazard maps are used to geographically show the level of shaking that is expected due to natural and non-natural earthquakes. Development of a seismic hazard map of Texas requires that one first understand the locations and expected magnitudes of potential earthquakes that can occur across the state. To do so, ground motion prediction models specific to the geologic and soil conditions across the state are required to predict the intensity of ground shaking. Work is currently being done in collaboration with researchers at The University of Texas at Austin (UT) in Geotechnical Engineering (Cox Rathje), the UT Institute of Geophysics (Frohlich, Walter), and the UT Bureau of Economic Geology (Paine). These collaborators develop ground motion prediction equations based on recordings throughout the © ASCE 11 Congress on Technical Advancement 2017 state, and they estimate the shear wave velocity over the top 30 m of soil, VS30, across the state of Texas. 2) Seismic vulnerability, or fragility, of Texas bridges. Fragility curves can be used to predict Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/04/19. Copyright ASCE. For personal use only; all rights reserved. the likelihood of bridge damage for a given level of seismic intensity, measured by metrics such as Peak Ground Acceleration (PGA) or spectral acceleration at a structure’s fundamental period (Sa(Tn)). Developing seismic fragility curves for Texas bridges requires an understanding of the inventory, structural detailing, and seismic behavior of typical bridges in the state. This part of the seismic assessment is the focus of the current paper. Quantification of bridge vulnerability and seismic hazards across the state is used to develop a rapidly-deployable post-earthquake response plan for state bridge officials. Establishing a post-event response plan can help ensure public safety and facilitate economic and timely bridge inspections following a seismic event. To create probabilistic models of expected bridge damage, the sources of uncertainty in bridge demands and capacities are taken into account. One approach to creating empirical fragility curves is to observe damage in constructed bridges following real earthquakes. While this approach inherently incorporates variability in material strength, construction quality, seismic demands, detailing, etc., it requires that a significant number of bridges be damaged during one or more seismic events to make meaningful probabilistic models. This approach is not feasible for predicting probabilistic seismic damage in Texas bridges since little to no bridge damage data exist from past seismic events in Texas. Alternatively, an analytical fragility curve can be developed in which numerical models representative of Texas bridges and ground motion records representative of Texas seismic hazards are used. The numerical models used to develop analytical fragility curves are capable of simulating bridge damage that may occur under seismic loading. To name but a few, the typical damage in bridge are bearing deformation and unseating, column damage, abutment and bridge deck pounding. Such models should incorporate potential variation, or uncertainty, in bridge geometry and component strength. The bridge population is characterized by different classes, and a set of bridges representative of each class are established using statistical sampling techniques. Using Monte Carlo Simulation methodologies, each bridge model parameter such as concrete strength, bearing stiffness, unit-weight, span length, column height, is selected from its respective probabilistic distribution as determined from the Texas bridge inventory. Like previous studies such as Zelaschi et al. (2015b), bridge samples are selected from the population to account for © ASCE 12