ANN-GA based optimization of a high ash coal-fired supercritical power plant

Ikjin Lee, Assistant Professor7109, N7-4, Mechanical Engineering DepartmentKorea Advanced Institute of Science and Technology (KAIST)291 Daehak-ro, Yuseong-gu, Daejeon 305-701, Republic of KoreaTel: +82-42-350-3041, Fax: +82-42-350-3210, Email: ikjin.lee@kaist.ac.kr_______________________________________________________________________EDUCATIONB.S. Mechanical Engineering Seoul National University, Korea 1993-2001 M.S. Mechanical Engineering Seoul National University, Korea 2001-2003 Ph.D. Mechanical Engineering University of Iowa 2003-2008RESEARCH AREAReliability-Based Design Optimization (RBDO)Reliability-Based Robust Design Optimization (RBRDO)System Reliability Analysis and Design OptimizationDesign under Uncertainties with Lack of InformationDesign under Uncertainties with Correlated Input VariablesSampling-Based RBDO with Parallel ComputingSurrogate Model Generation (Meta-modeling)PROFESSIONAL EXPERIENCESUniversity of Iowa Research Assistant 2003~2008 University of Iowa Teaching Assistant 2006~2007 University of Iowa Postdoctoral Research Scholar 2008.8~2011.7 University of Iowa Adjunct Professor 2010.7~2011.7 University of Connecticut Assistant Professor 2011.8~2013.7 KAIST Assistant Professor 2013.8~ TEACHING EXPERIENCESME3224 Analysis and Design of Mechanisms Fall 2011 ME3227 Design of Machine Elements Spring 2012 ME5895/ME3295 Probabilistic Engineering Design Fall 2012, Spring 2014 ME5511/ME3295 Principles of Optimum Design Spring 2013 MAE340 Engineering Design Fall 2013 MAE370 Understanding of Materials & Manufacturing Spring 2014 MAE475 Applied Mathematics Fall 2014 MAE 400 Capstone Design I Spring 2015 CD401 Multidisciplinary Capstone Design I Spring 2015 AWARDS1.ISSMO/Springer Prize for a young scientist, International Society of Structural andMultidisciplinary Optimization (ISSMO), 2009.2.Cited in Marquis Who’s Who in America, 64th Edition, 2010.PROJECT ACTIVITYFINSIHEDU.S. Army Tank-Automotive Command (TACOM)Caterpillar 994F Axle Pad ProjectU.S. Army Automotive Research Center (ARC) on FMTVWind Turbine Optimization with Clippers supported by Iowa Wind Project (IAWIND) Dynamic Analysis Software Development & Design Optimization by Stanley Black & DeckerIN PROGRESSLaunching Plug-in Digital Analysis Framework for Modular System DesignDevelopment of sensor-based virtual plant engineering technology for the support of plant O&MPROFESSIONAL SERVICESJournal Editor1.Associate Editor, Trans. Korean Soc. Mech. Eng. A, 2014~PresentPaper Reviewer (Reviewed more than 85 Journal Papers and 33 Conference Papers)1.Structural and Multidisciplinary Optimization (SMO)2.ASME Journal of Mechanical Design (JMD)3.Journal of Soils and Sediments (JSSS)4.Mechanism and Machine Theory (MECHMT)5.Journal of Optimization Theory and Applications (JOTA)6.International Journal of Vehicle Design (IJVD)7.Entropy8.Mechanics Based Design of Structures and Machines9.Probabilistic Engineering Mechanics (PREM)puters and Industrial Engineering (CAIE)11.Engineering Optimization (GENO)puter Methods in Applied Mechanics and Engineering (CMAME)13.Applied Mathematical Modeling (APM)14.Mechanical Systems and Signal Processing (MSSP)15.Ain Shams Engineering Journal (ASEJ)16.Journal of Mechanical Science and Technology (JMST)17.ASME (IDETC/CIE) Conference18.AIAA/MAO ConferencePaper Review Coordinator1.Paper review coordinator for the Design Automation Conference of ASMEInternational Design Engineering Technical Conferences (IDETC), 2011~2015.Program Committee1.Local organizing committee for 10th World Congress of Structural MultidisciplinaryOptimization (WCSMO), 2013.2.International scientific and organizing committee for 5th International Conference onComputational Methods (ICCM), 2014.3.Scientific committee for 8th China-Japan-Korea Joint Symposium on Optimization ofStructural and Mechanical Systems (CJK-OSM8), 2014.4.Local organizing committee for 12th World Congress on Computational Mechanics(WCCM), 2016Other Professional Activities1.Chaired sessions at the 38th Design Automation Conference of 2012 ASMEInternational Design Engineering Technical Conferences (IDETC), August 2012.2.Chaired sessions at the World Congress of Structural Multidisciplinary Optimization(WCSMO) 10, May 2013.3.Chaired sessions at the 39th Design Automation Conference of 2013 ASMEInternational Design Engineering Technical Conferences (IDETC), August 2013.4.Chaired sessions at the 40th Design Automation Conference of 2014 ASMEInternational Design Engineering Technical Conferences (IDETC), August 2014.MembershipAmerican Society of Mechanical Engineers (ASME)American Institute of Aeronautics and Astronautics (AIAA)International Society for Structural and Multidisciplinary Optimization (ISSMO)Korea Society of Mechanical Engineers (KSME)Korea Society of Computational Mechanics (KSCM)Korea Society of Design Optimization (KSDO)Korean Society of Precision Engineering (KSPE)Proposal Review Panel1.Romanian Executive Agency for Higher Education, Research, Development andInnovation Funding (UEFISCDI) Proposal Review Panel (2011,2013,2015)2.Kazakhstan National Center for Science and Technology Evaluation (NCSTE)Proposal Review Panel (2014)CONFERENCE SEMINAR PRESENTATIONS1.Presented a seminar “Alternative Methods for Reliability-Based Robust DesignOptimization Including Dimension Reduction Method,” at ARC conference, Ann Arbor, MI, May 24, 2006.2.Presented a seminar “Alternative Methods for Reliability-Based Robust DesignOptimization Including Dimension Reduction Method,” at 2006 ASME IDETC, Philadelphia, Pennsylvania, September 10-13, 2006.3.Presented a seminar “RBDO Using MPP-Based Dimension Reduction Method(DRM) for Multidimensional Highly Nonlinear Systems,” at ARC conference, Ann Arbor, MI, May 16, 2007.4.Presented a seminar “RBDO Using MPP-Based Dimension Reduction Method(DRM) for Multidimensional Highly Nonlinear Systems” at WCSMO7 conference, Seoul, Korea, May 22, 2007.5.Presented a seminar “A New Inverse Reliability Analysis Method Using MPP-BasedDimension Reduction Method (DRM),” at 2007 ASME IDETC, Las Vegas, Nevada, September 4-7, 2007.6.Presented a seminar “System Reliability-Based Design Optimization Using MPP-BasedDimension Reduction Method,” at ARC conference, Ann Arbor, MI, May 21, 2008.7.Presented a seminar “Sensitivity Analyses of FORM-Based and DRM-BasedPerformance Measure Approach for Reliability-Based Design Optimization,” at 2008 ASME IDETC, New York City, New York, August 3-6, 2008.8.Presented a seminar “Comparison Study between Probabilistic and PossibilisticApproach for Problems with Correlated Input and Lack of Input Statistical Information” at ARC conference, Ann Arbor, MI, May 13, 2009.9.Presented a seminar “Comparison Study between Probabilistic and PossibilisticApproach for Problems with Correlated Input and Lack of Input Statistical Information” at WCSMO8 conference, Lisbon, Portugal, June 1-5, 2009.10.Presented an award speech “RBDO Using MPP-Based Dimension Reduction Method(DRM) for Multidimensional Highly Nonlinear Systems” at WCSMO8 conference, Lisbon, Portugal, June 1-5, 2009.11.Presented a seminar “Comparison Study between Probabilistic and PossibilisticApproach for Problems with Correlated Input and Lack of Input Statistical Information” at 2009 ASME IDETC, San Diego, California, August 31-September 2, 2009.12.Presented a seminar “Sampling-Based Stochastic Sensitivity Analysis Using Scorefunctions for RBDO problems with Correlated Random Variables” at ARC conference, Ann Arbor, MI, May 11, 2010.13.Presented a seminar “Sampling-Based Stochastic Sensitivity Analysis Using Scorefunctions for RBDO problems with Correlated Random Variables” at 2010 ASME IDETC, Montreal, Canada, August 16, 2010.14.Presented a seminar “Equivalent Standard Deviation to Convert High-ReliabilityModel to Low-Reliability Model for Efficiency of Sampling-Based RBDO” at ARC conference, Ann Arbor, MI, May 24, 2011.15.Presented a seminar “Equivalent Standard Deviation to Convert High-ReliabilityModel to Low-Reliability Model for Efficiency of Sampling-Based RBDO” at 2011 ASME IDETC, Washington, D.C., August 28-31, 2011.16.Presented a seminar “A Novel Second-Order Reliability Method (SORM) Using Non-Central or Generalized Chi-Squared Distributions” at 2012 ASME IDETC, Chicago, Illinois, August 13-15, 2012.17.Presented a seminar“Probabilistic Sensitivity Analysis for Novel Second-OrderReliability Method (SORM) Using Generalized Chi-squared Distribution” at WCSMO10 conference, Orlando, FL, May 19-24, 2013.18.Presented a seminar “Sampling-Based Design Optimization in the Presence ofInterval Variables” at APCOM&ISCM 2013, Singapore, December 12, 2013, Keynote Speech.19.Presented a seminar “Reliability-Based Vehicle Safety Assessment and DesignOptimization of Roadway Radius and Speed Limit in Windy Environments” at KSME conference, Jeongseon, Korea, December 19, 2013.20.Presented a seminar “Inverse Reliability Analysis for Approximated Second-OrderReliability Method Using Hessian Update” at 2014 ASME IDETC, Buffalo, New York, August 17-20, 2014.21.Presented a seminar “Enhanced Second-Order Reliability Method and StochasticSensitivity Analysis Using Importance Sampling” at WCSMO11 conference, Sydney, Australia, June 7-12, 2015.INVITED SEMINAR PRESENTATIONS1.Presented a seminar “Reliability-based Design Optimization: The Past, Present, andFuture,” at the University of Iowa, October 1, 2009.2.Provided a lecture on “Sampling-based RBDO using the Dynamic Kriging andStochastic Sensitivity Analysis” to John Deere, August, 2010.3.Presented a seminar “Sampling-Based RBDO Using the Dynamic Kriging (D-Kriging)Method and Stochastic Sensitivity Analysis” at ARC seminar, the University of Michigan, Ann Arbor, MI, October 29, 2010.4.Provided a seminar “Recent Improvements on Reliability-Based Design Optimization(RBDO) Methodology,” at the University of Connecticut, March 2, 2011.5.Provided a seminar “Recent Improvements on Reliability-Based Design Optimization(RBDO) Methodology,” at the Korea Advanced Institute of Science and Technology (KAIST), April 8, 2011.6.Provided a workshop on “Sampling-Based RBDO Using Dynamic Kriging Methodand Stochastic Sensitivity Analysis” to Army TARDEC members, Warren, MI, April 19, 2011.7.Presented a seminar “Reliability-Based Design Optimization,” at Hanyang University,August 20, 2013.8.Presented a seminar “Application of RBDO to Vehicle Design,” at Hyundai Motors,October 25, 2013.9.Presented a seminar “Application of RBDO to Vehicle Design,” at Doosan Infracore,November 22, 2013.10.Presented a seminar “Reliability Assessment and its Application to Shipbuilding andOcean Plant Design,” at Samsung Heavy Industry, June 20, 2014.11.Presented a seminar “Simulation-Based Design under Uncertainties: Theory &Application,” at Harbin Institute of Technology, January 19, 2015.12.Presented an invited lecture “Simulation-based Design Under Uncertainties: Theory& Application”, 2nd Annual Conference of Korea Society for Design Optimization, 2015.13.Presented a seminar “Simulation-based Design Under Uncertainties: Theory &Application”, at Korea Maritime University, 2015.14.Will present a seminar at Dalian University of Technology, July, 2015.15.Will present a seminar at NYU POLY, August, 2015.PUBLICATIONSBooks1.Lee, I.,Dimension Reduction Method for Design under Uncertainty: Applications ofDimension Reduction Method to Reliability-Based Design Optimization and Robust Design Optimization, LAP LAMBERT Academic Publishing, 2010.Ph. D. Thesis1.“Reliability-Based Design Optimization and Robust Design Optimization UsingUnivariate Dimension Reduction Method,” University of Iowa, 2008.Papers in Technical Journals (International)1.Lee, I., Choi, K.K., Du, L., and Gorsich, D., “Dimension Reduction Method forReliability-Based Robust Design Optimization,” Special Issue of Computers & Structures: Structural and Multidisciplinary Optimization, Vol. 86, pp. 1550–1562, 2008. (IF: 2.134)2.Lee, I., Choi, K.K., Du, L., and Gorsich, D., “Inverse Analysis Method Using MPP-Based Dimension Reduction for Reliability-Based Design Optimization of Nonlinear and Multi-Dimensional Systems,” Special Issue of Computer Methods in Applied Mechanics and Engineering: Computational Methods in Optimization Considering Uncertainties, Vol. 198, No. 1, pp. 14-27, 2008. (IF: 2.959)3.Noh, Y., Choi, K.K., and Lee, I., “Reduction of Ordering Effect in RBDO UsingDimension Reduction Method,” AIAA Journal, Vol. 47, No. 4, pp. 994-1004, 2009.(IF: 1.207)4.Lee, I., Choi, K.K., and Gorsich, D., “Sensitivity Analyses of FORM-Based andDRM-Based Performance Measure Approach (PMA) for Reliability-Based Design Optimization (RBDO),” International Journal for Numerical Methods in Engineering, Vol. 82, No.1, pp. 26-46, 2010. (IF: 2.055)5.Lee, I., Choi, K.K., and Gorsich, D., “System Reliability-Based Design OptimizationUsing the MPP-Based Dimension Reduction Method,” Journal of Structural and Multidisciplinary Optimization, Vol. 41, No. 6, pp. 823-839, 2010. (IF: 1.974)6.Noh, Y., Choi, K.K., and Lee, I., “Identification of Marginal and Joint CDFs UsingBayesian Method for RBDO,” Journal of Structural and Multidisciplinary Optimization, Vol. 40, No. 1, pp. 35-51, 2010.(IF: 1.974)7.Noh, Y., Choi, K.K., and Lee, I., “Comparison Study between MCMC-based andWeight-based Bayesian Methods for Identifications of Joint Distribution,” Journal of Structural and Multidisciplinary Optimization, Vol. 42, No. 6, pp. 823-833, 2010.(IF: 1.974)8.Lee, I., Choi, K.K., Noh, Y. Zhao, L., and Gorsich D., “Sampling-Based StochasticSensitivity Analysis Using Score Functions for RBDO Problems with CorrelatedRandom Variables,” Journal of Mechanical Design, Vol. 133, No. 2, 21003, 2011.(IF: 1.250)9.Noh, Y., Choi, K.K., and Lee, I., “Reliability-Based Design Optimization withConfidence Level under Input Model Uncertainty Due to Limited Test Data,” Journal of Structural and Multidisciplinary Optimization, Vol. 43, No. 4, pp. 443-458, 2011.(IF: 1.974)10.Zhao, L., Choi, K.K., and Lee, I., “Metamodeling Method Using Dynamic Krigingfor Design Optimization,” AIAA Journal, Vol. 49, No. 9, pp. 2034-2046, 2011. (IF:1.207)11.Noh, Y., Choi, K.K., and Lee, I., “Reliability-based Design Optimization withConfidence Level for Non-Gaussian Distributions Using Bootstrap Method,” Journal of Mechanical Design, Vol. 133, No. 9, 91001, 2011. (IF: 1.250)12.Lee, I., Choi, K.K., and Zhao, L., “Sampling-Based RBDO Using the StochasticSensitivity Analysis and Dynamic Kriging Method,” Journal of Structural and Multidisciplinary Optimization, Vol. 44, No. 3, pp. 299-317, 2011. (IF: 1.974)13.Lee, I., Noh, Y., and Yoo, D., “A Novel Second-Order Reliability Method (SORM)Using Non-Central or Generalized Chi-Squared Distributions,” Special Issue of Journal of Mechanical Design on Design under Uncertainty, Vol. 134, No. 10, 100912, 2012. (IF: 1.250)14.Lee, I., Choi, K.K., Noh, Y., and Lamb, D., “Comparison Study betweenProbabilistic and Possibilistic Methods for Problems under a Lack of Correlated Input Statistical Information,” Journal of Structural and Multidisciplinary Optimization, Vol. 47, No. 2, pp. 175-189, 2013. (IF: 1.974)15.Song, H., Choi, K.K., Lee, I., Zhao, L., and Gorsich, D., “Adaptive Virtual SupportVector Machine for Reliability Analysis of High-Dimensional Problems,” Journal of Structural and Multidisciplinary Optimization,Vol. 47, No. 4, pp. 479-491, 2013.(IF: 1.974)16.Lee, I., Choi, K.K., and Shin, J., “Equivalent Target Probability of Failure to ConvertHigh-reliability Model to Low-reliability Model for Efficiency of Sampling-based RBDO,” Journal of Structural and Multidisciplinary Optimization, Vol. 48, No. 2, pp.235-248, 2013. (IF: 1.974)17.Zhao, L., Choi, K.K., Lee, I., and Gorsich, D., “Conservative Surrogate Model usingWeighted Kriging Variance for Sampling-based RBDO,”Journal of Mechanical Design, Vol. 135, No. 9, 091003, 2013. (IF: 1.250)18.Yoo, D., and Lee, I., “Sampling-based Approach for Design Optimization in thePresence of Interval Variables,” Journal of Structural and Multidisciplinary Optimization, Vol. 49, No. 2, pp. 253-266, 2014. (IF: 1.974)19.Shin, J., and Lee, I., “Reliability-Based Vehicle Safety Assessment and DesignOptimization of Roadway Radius and Speed Limit in Windy Environments,” Journal of Mechanical Design, Vol. 136. No. 8, 081006, 2014. (IF: 1.250)20.Yoo, D., Lee, I., and Cho, H., “Probabilistic Sensitivity Analysis for Novel Second-Order Reliability Method using Generalized Chi-Squared Distribution,” Journal of Structural and Multidisciplinary Optimization,Vol. 50, No. 5, pp. 787-797, 2014.(IF: 1.974)21.Lim, J., Lee, B., and Lee, I., “SORM-based Inverse Reliability Analysis UsingHessian Update for Accurate and Efficient Reliability-based Design Optimization,”International Journal for Numerical Methods in Engineering, Vol. 100, No. 10, pp.773-792, 2014. (IF: 2.055)22.Shin, J., and Lee, I., “Reliability Analysis and Reliability-Based Design Optimizationof Roadway Horizontal Curves Using a First-Order Reliability Method (FORM),”Engineering Optimization, Vol. 47, No. 5, pp. 622-641, 2015. (IF: 1.076)23.Lim, J., Lee, B., and Lee, I., “Sequential Optimization and Reliability Assessmentbased on Dimension Reduction Method for Accurate and Efficient Reliability-based Design Optimization,” Journal of Mechanical Science and Technology, Vol. 29, No.4, pp. 1349-1354, 2015. (IF: 0.838)24.Cho, H., Choi, K.K., and Lee, I., “Design Sensitivity Method for Sampling-BasedRBDO with Fixed COV,” submitted to Journal of Mechanical Design, 2015.Technical Notes1.Zhao, L., Choi, K.K., and Lee, I., “Reply by the Authors to the Comment by H. Liangand M. Zhu,” AIAA Journal, Vol. 51, No. 12, pp. 2989-2990, 2013. (IF: 1.207)International Conference Proceedings1.Choi, K.K., Lee, I., and Gorsich, D., “Dimension Reduction Method for Reliability-Based Robust Design Optimization,” III European Conference on Computational Mechanics, Lisbon, Portugal, June 5-8, 2006.2.Lee, I., Choi, K.K., and Du, L., “Alternative Methods for Reliability-Based RobustDesign Optimization Including Dimension Reduction Method,” 32nd ASME Design Automation Conference, Philadelphia, Pennsylvania, September 10-13, 2006.3.Lee, I., Choi, K.K., and Du, L., “Dimension Reduction Method (DRM) Based RBDOfor Highly Nonlinear Systems,” WCSMO7, COEX Seoul, Korea, May 21-25, 2007, Received the ISSMO-Springer Prize.4.Choi, K.K., Du, L., Lee, I., and Gorsich, D., “A New Robust Concept in PossibilityTheory for Possibility-Based Robust Design Optimization,” WCSMO7, COEX Seoul, Korea, May 21-25, 2007.5.Lee, I., Choi, K.K., Du, L., and Gorsich, D., “A New Inverse Reliability AnalysisMethod Using MPP-Based Dimension Reduction Method (DRM),” 33rd ASME Design Automation Conference, Las Vegas, Nevada, September 4-7, 2007.6.Du, L., Choi, K.K., and Lee, I., “Robust Design Concept in Possibility Theory AndOptimization For System With Both Random And Fuzzy Input Variables,” the 2007 ASME International Design Engineering Technical Conferences (IDETC), Las Vegas, Nevada, September 4-7, 2007.7.Lee, I., Choi, K.K., Du, L., and Gorsich, D., “Sensitivity Analyses of FORM-Basedand DRM-Based Performance Measure Approach for Reliability-Based Design Optimization,” 34th ASME Design Automation Conference, New York City, New York, August 3-6, 2008.8.Lee, I., Choi, K.K., Du, L., and Gorsich, D., “System Reliability-Based DesignOptimization Using MPP-Based Dimension Reduction Method,” 12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Victoria, British Columbia, September 10-12, 2008.9.Noh, Y., Choi, K.K., and Lee, I., “MPP-Based Dimension Reduction Method forRBDO Problems with Correlated Input Variables,” 12th AIAA/ISSMOMultidisciplinary Analysis and Optimization Conference, Victoria, British Columbia, September 10-12, 2008.mb, D., Gorsich, D., Choi, K.K., Noh, Y., and Lee, I., “The Use of Copulas andMPP-based Dimension Reduction Method (DRM) to Assess and Mitigate Engineering Risk in the Army Ground Vehicle,” 26th Army Science Conference, Orlando, Florida, December, 1-4, 2008.11.Lee, I., Choi, K.K., and Noh, Y., “Comparison Study between Probabilistic andPossibilistic Approach for Problems with Correlated Input and Lack of Input Statistical Information,” WCSMO8, Lisbon, Portugal, June 1-5, 2009.12.Noh, Y., Choi, K.K., Lee, I., and Gorsich, D., “Reliability-Based DesignOptimization with Confidence Level using Copula under Input Model Uncertainty,”WCSMO8, Lisbon, Portugal, June 1-5, 2009.13.Zhao, L., Choi, K.K., Lee, I., and Gorsich, D., “Sequential Sampling-Based KrigingMethod with Dynamic Basis Selection,” WCSMO8, Lisbon, Portugal, June 1-5, 2009.14.Lee, I., Choi, K.K., and Noh, Y., “Comparison Study between Probabilistic andPossibilistic Approach for Problems with Correlated Input and Lack of Input Statistical Information,” 35th ASME Design Automation Conference, San Diego, California, August 31-September 2, 2009.15.Noh, Y., Choi, K.K., Lee, I., Gorsich, D., and Lamb, D., “Reliability-Based DesignOptimization with Confidence Level using Copula under Input Model Uncertainty,”35th ASME Design Automation Conference, San Diego, California, August 31-September 2, 2009.16.Zhao, L., Choi, K.K., Lee, I., and Du, L., “Response Surface Method usingSequential Sampling for Reliability-Based Design Optimization,” 35th ASME Design Automation Conference, San Diego, California, August 31-September 2, 2009.17.Noh, Y., Choi, K.K., Lee, I., Gorsich, D., and Lamb, D., “Reliability-Based DesignOptimization with Confidence Level for Non-Gaussian Distributions Using Bootstrap Method,” 6th China-Japan-Korea Joint Symposium on Optimization of Structural and Mechanical Systems, Kyoto, Japan, June 22-25, 2010.18.Lee, I., Choi, K.K., Noh, Y., Zhao, L., and Gorsich, D., “Sampling-Based StochasticSensitivity Analysis Using Score Functions for RBDO Problems with Correlated Random Variables,” 36th ASME Design Automation Conference, Montreal, Canada, August 16-18, 2010.19.Noh, Y., Choi, K.K., Lee, I., and Gorsich, D., “Reliability-Based DesignOptimization with Confidence Level for Non-Gaussian Distributions Using Bootstrap Method,” 36th ASME Design Automation Conference, Montreal, Canada, August 16-18, 2010.20.Lee, I., Choi, K.K., and Zhao, L., “Sampling-Based RBDO Using the DynamicKriging (D-Kriging) Method and Stochastic Sensitivity Analysis,” 13th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Fort Worth, Texas, September 13-15, 2010.21.Zhao, L., Choi, K.K., Lee, I., and Gorsich, D., “A Metamodeling Method UsingDynamic Kriging and Sequential Sampling,”13th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Fort Worth, Texas, September 13-15, 2010. 22.Zhao, L., Choi, K.K., Lee, I., and Gorsich, D., “Conservative Surrogate Model usingWeighted Krigin Variance for Sampling-Based RBDO,” WCSMO9, Shizuoka, Japan,June 13-17, 2011.23.Choi, K.K., Lee, I., Zhao, L., Noh, Y., Lamb, D., and Gorsich, D., “Sampling-BasedRBDO Using Stochastic Sensitivity and Dynamic Kriging for Broader Army Applications,” NDIA Ground Vehicle Systems Engineering And Technology Symposium, Dearborn, Michigan, August 9-11, 2011.24.Lee, I., Choi, K.K., and Gorsich, D., “Equivalent Standard Deviation to ConvertHigh-reliability Model to Low-reliability Model for Efficiency of Sampling-based RBDO,” 37th ASME Design Automation Conference, Washington, D.C., August 28-31, 2011.25.Song, H., Choi, K.K., Lee, I., Zhao, L., and Lamb, D., “Adaptive Virtual SupportVector Machine for the Reliability Analysis of High-Dimensional Problems,”37th ASME Design Automation Conference, Washington, D.C., August 28-31, 2011.26.Lee, I., Noh, Y., and Yoo, D., “A Novel Second-Order Reliability Method (SORM)Using Non-Central or Generalized Chi-Squared Distributions,” 38th ASME Design Automation Conference, Chicago, Illinois, August 13-15, 2012.27.Cho, H., Choi, K.K., Lee, I., and Gorsich, D., “Confidence Level Estimation andDesign Sensitivity Analysis for Confidence-Based RBDO,” 38th ASME Design Automation Conference,Chicago, Illinois, August 13-15, 2012.28.Song, H., Choi, K.K., Lee, I., Zhao, L., and Gorsich, D., “Sampling-based RBDOUsing Stochastic Sensitivity-based Analysis and Virtual Support,” 38th ASME Design Automation Conference, Chicago, Illinois, August 13-15, 2012.29.Yoo, D., Lee, I., and Cho, H., “Probabilistic Sensitivity Analysis for Novel Second-Order Reliability Method using Generalized Chi-Squared Distribution,” WCSMO10, Orlando, Florida, May 19-24, 2013.30.Shin, J., and Lee, I., “Reliability-Based Design Optimization of Highway HorizontalCurves Based on first-Order Reliability Method,” WCSMO10, Orlando, Florida, May 19-24, 2013.31.Yoo, D., and Lee, I., “Sampling-Based Approach for Design Optimization in thePresence of Interval Variables,” WCSMO10, Orlando, Florida, May 19-24, 2013.32.Shin, J., and Lee, I., “First-Order Reliability Analysis of Vehicle Safety in HighwayHorizontal Curves,” 39th ASME Design Automation Conference, Portland, Oregon, August 4-7, 2013.33.Yoo, D., and Lee, I., “Sampling-based Approach for Design Optimization in thePresence of Interval Variables,” 39th ASME Design Automation Conference, Portland, Oregon, August 4-7, 2013.34.Yoon, G., Hur, J., Lee, I., and Youn, S., “Efficiency Improvement Approach ofSurrogate-model Based BLISS,” 8th China-Japan-Korea Joint Symposium on Optimization of Structural and Mechanical Systems (CJK-OSM8), Gyeongju, Korea, May 25-29, 2014.35.Lim, J., Lee, I., and Lee, B., “Sequential Optimization and Reliability AssessmentBased on Dimension Reduction Method for Accurate and Efficient Reliability-based Design Optimization,” 8th China-Japan-Korea Joint Symposium on Optimization of Structural and Mechanical Systems (CJK-OSM8),Gyeongju, Korea, May 25-29, 2014.36.Lim, J., Lee, I., and Lee, B., “Inverse Reliability Analysis for Approximated Second-Order Reliability Method Using Hessian Update” 40th ASME Design AutomationConference, Buffalo, New York,August 17-20, 2014.37.Piao, M.J., Park, C.H., Huh, H., and Lee, I., “Validation of Dynamic HardeningModels with Taylor Impact Tests at High Strain Rates,” 12th Asia-Pacific Conference on Engineering Plasticity and Its Application (AEPA), Taiwan, September 1-5, 2014.38.Lim, J., Lee, B., and Lee, I., “Enhanced second-order reliability method andstochastic sensitivity analysis using importance sampling” 11th World Congress of Structural and Multidisciplinary Optimization (WCSMO), Sydney, Australia, June 7-12, 2015.39.Kang, S.,Lee, I., and Lim, J., “Accuracy Improvement of MPP-Based DimensionReduction Method Using the Eigenvectors of the Hessian Matrix” 11th World Congress of Structural and Multidisciplinary Optimization, Sydney (WCSMO), Australia, June 7-12, 2015.Domestic Conference Proceedings1.Song, J., and Lee, I., “Accurate and Efficient Dimension Reduction Method UsingEigenvectors of the Hessian Matrix,” Proceedings of the KSME Fall Annual Meeting, Gwangju, Korea, November 11-14, 2014.2.Kang, K., and Lee, I., “A study on Efficiency Improvement of Kriging using cross-validationand process variance,” Proceedings of the KSME Fall Annual Meeting, Gwangju, Korea, November 11-14, 2014.3.Kang, S., and Lee, I., “Efficient reliability-based design optimization using approximation ofmost probable point,” Proceedings of the KSME Fall Annual Meeting, Gwangju, Korea, November 11-14, 20144.Kang, S., and Lee, I., “Accuracy Improvement of MPP-Based Dimension Reduction MethodUsing the Eigenvectors of Hessian Matrix” Proceedings of the KSME Spring Annual Meeting, Jeju, Korea, April 15-18, 2015.5.Song, J., Lee, I., and Lee, B., “Accurate and Efficient Dimension Reduction Method UsingEigenvectors of the Hessian Matrix” Proceedings of the KSME Spring Annual Meeting, Jeju, Korea, April 15-18, 2015.6.Kang, K., and Lee, I., “Basis Screening Kriging : Efficient and accurate surrogate modeling”Proceedings of the KSME Spring Annual Meeting, Jeju, Korea, April 15-18, 2015.7.Lee, I., and Kang, K., “Design Optimization of Subframe of Chassis Using SurrogateModeling” Proceedings of the KSME Spring Annual Meeting, Jeju, Korea, April 15-18, 2015.11。

文章编号：1006-3080(2021)02-0177-06DOI: 10.14135/ki.1006-3080.20191226002乙烯装置裂解气压缩机性能预测模型研究马芳芳1， 熊 达1， 孙铁栋2， 欧阳福生1（1. 华东理工大学石油加工研究所，上海 200237；2. 石化盈科信息技术有限责任公司，北京 100007）摘要：以乙烯装置裂解气压缩机的设计信息为基础，通过修正设计数据建立了压缩机性能模型；基于学习速率自适应误差变化思想并结合遗传算法（GA ）的全局寻优特性，提出了一种改进BP 算法LR-GA-BP 进行压缩机性能预测；用模型对某乙烯装置四级压缩系统进行了模拟计算，压缩机第四段出口气体主要组分的预测值与实测值的相对误差均小于2%，压缩机各段出口温度和压力的预测值与实测值的相对误差均小于1%，说明裂解气压缩机性能预测模型是可靠的。

根据热力学原理，将四级压缩过程等价为绝热压缩，分析了压缩机第四段出口温度较高的影响因素，提出了降温措施，并进行模拟验证。

结果表明，适当增加压缩机段间返回流量可以降低压缩机第四段出口温度。

本文结果对于减缓压缩系统结焦、优化压缩机操作具有重要参考价值。

关键词：裂解气压缩机；性能预测模型；模拟计算；LR-GA-BP 算法中图分类号：TQ 052文献标志码：A作为乙烯装置的关键设备，裂解气压缩机多采用多级离心式压缩机。

在压缩机运转过程中，其性能信息的完备性至关重要。

通常压缩机在设计工况下的性能信息由制造商提供，但根据实际生产需要，压缩机常在偏离设计工况的条件下运行。

由于实际工况下的工艺参数不可避免会发生变化，所以需要经常对压缩机进行频繁的手动调节，人工干预程度较高，且存在一定的安全风险。

为保证压缩机在变工况条件下安全、平稳运行，研究其变工况性能十分必要。

为获取压缩机在各类工况下的性能信息，Sieros 等[1]提出了压缩机和涡轮机性能图的解析表示法，该方法的有效性已在发动机试验中得到验证，但为达到高的精度要求，该方法通常需要引入大量的过程参数，导致模型较为复杂；褚菲等[2]利用热力学定理和压缩机能量损失机理建立了机理模型，并通过BP （Back Propagation ）神经网络修正机理模型，虽然该模型的精度较高，但由于离心机操作条件复杂，导致机理模型中的一些重要参数如冲击损失系数等难以准确获取。

改进小生境差分进化算法在配网无功优化中的应用黄俊辉1，李琥1，衣涛2，元梨花3，韩俊1 （1.国网江苏省电力公司经济技术研究院，江苏南京210000；2.上海交通大学电气工程系，上海200240；3.上海博英信息科技有限公司，上海200240）【摘要】摘要：配网无功优化是一类非线性的整数规划问题，通过调整变压器的变比，改变发电机的端电压和连接补偿电容来改变电力网络中的无功，减小系统网损。

差分进化算法是一种收敛速度快，收敛精度高的智能进化算法，针对无功优化模型对差分进化算法做出改进，引入小生境思想。

通过实例验证了小生境粒子群算法（NPSO）和改进小生境差分进化算法（FERDE）对无功补偿装置布点优化规划的有效性。

结果表明，增强算法的局部搜索能力和扩宽搜索范围，在收敛速度和精度上都有不同程度的提高。

【期刊名称】电网与清洁能源【年(卷),期】2015(031)006【总页数】5【关键词】配网无功优化；差分进化算法；小生境；粒子群优化配网无功优化问题是一个多变量、多约束的混合非线性规划问题，其控制变量既有连续变量，又有离散变量，整个优化过程十分复杂，计算规模大。

从传统的算法，如线性规划法、非线性规划法等，到人工智能算法，如粒子群优化算法、遗传算法等，都在不同程度上对无功优化做出贡献。

随着智能启发式优化算法的发展，差分进化算法逐步被应用到电力系统中，该算法具有易理解、并行处理、鲁棒性好等特点，能以较大概率找到问题的全局最优解，且计算效率比传统的进化规划等算法高。

其最大的优势在于简单易实现、收敛速度快、搜索精度高，不但适合科学研究，而且适合工程应用。

因此，差分进化算法（Differential evolution algorithm，DE）一经提出，立刻引起了演化计算领域研究者的广泛关注，并涌现出大量的研究成果，已经在函数优化、神经网络设计、分类、模式识别、信号处理、机器人技术等应用领域取得了成功应用[1-2]。

基于神经网络的微生物生长预测模型侯奇;刘静;管骁【摘要】鉴于现有大多数预测模型都是经验型模型,含有过多没有生物解释的参数,提出一个基于神经网络的非经验型的微生物生长预测模型,并以李斯特菌为研究实例,利用其试验环境的温度、pH 值和Aw值建立BP神经网络二级生长模型,在不同环境条件下拟合微生物的生长速率和倍增时间,结合微生物初始浓度对一级模型的时间与微生物生长情况进行预测,最后利用李斯特菌生长数据对模型进行仿真测试.试验结果证明,该模型可以对微生物生长的各个时期进行有效预测,相对于经验模型,该模型更加适用于微生物生长动力学预测,有效地解决了经验型模型的参数问题.%Most of the existing predictive models are empirical models which contain too many parameters without biological explanations. In this study,a non-empirical growth prediction model based on neu-ral network was proposed.A BP neural network secondary growth model was established by using Listeria monocytogenes as an exam-ple,using the temperature,pH value and Aw value of the experi-mental environment.The growth rate and double time of microbes were fitted in different environments.Subsequently,combining with the initial concentration of microorganisms,the primary model of mi-croorganism growth with time was predicted.Finally,the growth data of Listeria monocytogenes were tested,and the experimental re-sults showed that the model could predict the growth period of pared with the empirical model,this non-empirical pre-diction one was more suitable for predicting themicrobial growth dy-namics,and also the parameters of the empirical model could be solved effectively.【期刊名称】《食品与机械》【年(卷),期】2018(034)002【总页数】4页(P120-123)【关键词】微生物;生长预测模型;神经网络【作者】侯奇;刘静;管骁【作者单位】上海海事大学信息工程学院,上海 201306;上海海事大学信息工程学院,上海 201306;上海理工大学医疗器械与食品学院,上海 200093【正文语种】中文预测微生物学本质上是基于微生物群体对环境因素反应的可重现，利用过去观察到的试验数据通过数学模型预测食物环境中的微生物行为，并用试验结果证明模型所得到的误差不大于微生物试验所带来的误差[1]。

基于JITL的多模态工业数据预测发布时间：2021-10-14T07:32:32.120Z 来源：《科学与技术》2021年17期作者：陈雨杉[导读] 在工业过程中，由于产品变化、工况切换或控制器调整等原因，过程数据往往呈现多模态特征。

数据驱动方法通常基于单模态假设，这可能无法描述过程特征。

陈雨杉杭州电子科技大学浙江杭州 310018摘要：在工业过程中，由于产品变化、工况切换或控制器调整等原因，过程数据往往呈现多模态特征。

数据驱动方法通常基于单模态假设，这可能无法描述过程特征。

传统的实时学习（JITL）方法能够不断更新模型来描述多模态数据，但耗时长，不能满足实时性要求。

本文提出了一种改进的JITL方法来快速发现相似样本。

首先将新样本划分为主类别，然后查找相似样本，提高搜索效率。

通过一个工业软测量实例与偏最小二乘法（PLS）相结合，证明了该方法的有效性，与基本JITL相比，该方法的均方根误差（RMSE）降低了0.09，运行速度提高了8.8倍。

关键词：软测量、即时学习、多模式、偏最小二乘、数据驱动一、背景介绍在实际的工业过程中，追求产品质量改进是一项长期且具有工业价值的任务。

然而，由于设备的成本或环境的复杂性，许多关键的过程变量很难获得。

随着人工智能和数据存储技术的发展，软测量越来越受到人们的重视。

数据驱动的软测量方法有许多吸引人的特性：（1）它们为昂贵的硬件传感器提供了一种低成本的替代方案(2）它们允许实时估计数据，克服了缓慢的硬件传感器带来的时间延迟，从而提高了控制算法的性能(3）它们在质量控制中起着不可或缺的作用。

在过去的几十年中，基于数据的软测量建模方法已经得到了广泛的研究，如支持向量机（SVM）[1]，人工神经网络（ANN）[2]，偏最小二乘（PLS）[3]。

支持向量机被定义为一个凸二次优化问题，它具有计算量小、优化选择方便等优点。

然而，当输入大规模样本时，模型的构建很难实现。

神经网络通过建立数据之间的关系和调整各种网络参数来建立模型。

GA与ANN的结合作者：吴伟来源：《吉林省教育学院学报·上旬刊》 2011年第9期吴伟（1．苏州市职业大学，江苏苏州 215104 2．苏州大学计算机科学与技术学院，江苏苏州 215104）摘要：本文主要讲述了GA算法的特点和ANN的优点并说明了GA与ANN结合的必要性，同时对今后的研究前景作了具体的展望。

关键词：遗传算法GA；人工神经网络ANN；结合中图分类号：G642文献标识码：A文章编号：1671—1580（2011）09—0111—02一、遗传算法(Genetic Algorithms，GA)遗传算法是一类借鉴生物界自然选择和自然遗传机理的随机优化算法，是模拟达尔文遗传选择和自然淘汰生物进化过程的计算模型。

主要特点是群体搜索策略和群体中个体的信息交换，搜索不依赖于梯度信息。

尤其适用于处理传统搜索方法难以解决的复杂和非线性问题。

可广泛用于组合优化，机器学习，自适应控制，规划设计和人工生命等领域。

随着问题种类的不同以及问题规模的扩大，要寻求一种能以有限的代价来解决搜索和优化的通用方法，GA正是为我们提供的一个有效的途径，它不同于传统的搜索和优化方法。

主要区别在于：一是自组织、自适应和自学习性(智能性)。

应用GA求解问题时，在编码方案、适应度函数及遗传算子确定后，算法将利用进化过程中获得的信息自行组织搜索。

通常，适应度大的个体具有更适应环境的基因结构，再通过基因重组和基因突变等遗传操作，就可能产生更适应环境的后代。

进化算法的这种自组织、自适应特征，使它同时具有能根据环境变化来自动发现环境的特性和规律的能力。

自然选择消除了算法设计过程中的一个最大障碍，即需事先描述问题的全部特点。

因此，利用GA的方法，可以解决复杂的非结构化问题。

二是GA的本质并行性。

GA按并行方式搜索一个种群数目的点，而不是单点。

它的并行性表现在两个方面，一方面遗传算法是内在并行的(inherent parallelism)，即GA本身非常适合大规模并行；另一方面是GA的内涵并行性(implicit parallelism)。

建筑工程毕业设计外文翻译英文原文The effects of surface preparation on the fracture behavior ofECC/concrete repair systemToshiro Kamada a,*, Victor C. Li ba Department of Civil Engineering, Gifu University, Yanagido, Gifu 501-1193, Japanb Advanced Civil Engineering Materials Research Laboratory, Department of Civil and Environmental Engineering,University of Michigan, Ann Arbor, Michigan, MI 48109-2125, USAReceived 7 July 1999; accepted 15 May 2000AbstractThis paper presents the influence of surface preparation on thekink-crack trapping mechanism of engineered cementitious composite (ECC)/concrete repair system. In general,surfacepreparation of the substrate concrete is considered essential to achieve a durable repair. In thisexperiment, the ``smooth sur face’’ system showed more desirable behavior in the crack pattern and the crack widths than the ``rough surface’’ system. This demonstrates that the smooth surface system is preferable to the rough surface system, from the view point of obtaining durable repair structure. The special phenomenon of kink-crack trapping which prevents the typical failuremodes of delamination or spalling in repaired systems is best revealed when the substrate concrete is prepared to have a smooth surface prior to repair. This is in contrast to the standard approach when the substrate concrete is deliberately roughened to create better bonding to the new concrete. Ó 2000 Elsevier Science Ltd. All rights reserved.Keywords: ECC repair system; Kink-crack trapping mechanism; Surface preparation; Durable repair1. IntroductionEngineered cementitious composites (ECCs) [1,2] are high performance fiber-reinforced cement based composite materials designed with micromechanical principles. Micromechanicalparameters associated with fiber, matrix and interface are combined to satisfy a pair of criteria, the first crack stress criterion and steady state cracking criterion [3] to achieve the strain hardening behavior. Micromechanics allows optimization of the composite for high performance while minimizing the amount of reinforcing fibers (generally less than 2-3%). ECC has a tensile strain capacity of up to 6% and exhibits pseudo-strain hardening behavior accompanied by multiple cracking. It also has high ultimate tensile strength (5-10 MPa), modulus of rupture (8-25 MPa), fracture toughness (25-30 kJ/m2) and compressive strength (up to 80 MPa) and strain (0.6%). A typical tensile stress-strain curve is shown in Fig. 1. ECC has its uniqueness not only insuperior mechanical properties in tension or in relatively small amount ofchopped fiber usage but also in micromechanical methodology in material design.The use of ECC for concrete repair was proposed by Li et al. [4], and Lim and Li [5]. In theseexperiments, specimens representative of an actual repair system - bonded overlay of a concrete pavement above a joint, were used. It was shown that the common failure phenomenona ofspalling or delamination in repaired concrete systems were eliminated. Instead, microcracksemanated from the tips of defects on the ECC/concrete interface, kinked into and subsequently were arrested in the ECC material (see Fig. 2, [5]). The tendency for the interface crack to kink into the ECC material depends on the competing driving force for crack extension at differentorientations, and on the competing crack extension resistance along the interface and into the ECC material. A low initial toughness of ECC combined with a high Mode II loading configuration tends to favor kinking. However, if the toughness of ECC remains low after crack kinking, this crack will propagate unstably to the surface, forming a surface spall. This is the typically observed phenomenon associated with brittle concrete and even fiber-reinforced concrete (FRC). In the case of ECC, the kinked crack is trapped or arrested in the ECC material, dueto the rapidly rising toughness of the ECC material. Conceptually, the ECC behaves like a material with strong R-curve behavior, with lowinitial toughness similar to that of cement (0.01 kJ/m2) and high plateau toughness (25-30 kJ/m2). After kinked crack arrest,additional load can drive further crackextension into the interface, followed by subsequent kinking and arrest.Details of the energetics of kink-crack trapping mechanism can befound in [5]. It was pointed out that this kink-crack trapping mechanism could serve as a means for enhancing repaired concrete system durability.In standard concrete repair, surface preparation of the substrate concrete is considered critical in achieving a durable repair [6]. Inthe study of Lim and Li [5], the ECC is cast onto a diamond saw cut surface of the concrete. Hence, the concrete surface is smooth and is expected as a result to produce a low toughness interface. Higherinterface roughness has been associated with higher interface toughnessin bi-material systems [7].In this paper, this particular aspect of the influence of surface preparation on the kink-crack trapping phenomenon is investigated. Specifically, the base concrete surfaces were prepared by threedifferent methods. The first surface was obtained as cut surface byusing a diamond saw (smooth surface), similar to that used in theprevious study [5]. The second one was obtained by applying a lubricanton the smooth surface of the concrete to decrease the bond between thebase concrete and the repair material. This surface was applied only in one test case to examine the effect of weak bond of interface on the fracture behavior of the repaired specimen. The third surface was prepared with a portable scarifier to produce a roughened surface (rough surface) from a diamond saw-cut surface.Regarding the repair materials, the water/cement ratio of ECC was varied to control its toughness and strength. Thus, two different mixtures of ECC were used for the comparison of fracture behavior in both smooth and rough surface case. Concrete and steel fiber-reinforced concrete (SFRC) were also used as control repair materials instead of ECC.2. Experimental procedure2.1. Specimens and test methodsThe specimens in this experiment were designed to induce a defect in the form of aninterfacial crack between the repair material and the base concrete, as well as a joint in thesubstrate. Fig. 3 shows the dimensions of the designed specimen and the loading configuration, and these were the same as those of the previous experiment [5]. This loading condition can provide a stable interface crack propagation condition, when the crack propagates along the interface [8].In this experiment, concrete, SFRC and ECC (with two different W/C ratios) were used as therepair materials. Table 1 illustrates the combinations of the repair material and the surface condition of test specimens. The numbers of specimens are also shown in Table 1. Only in the concrete overlay specimens, a special case where lubricant was smeared on the concrete smooth surface was used.The mix proportions of materials are shown in Table 2. Ordinary mixture proportions wereadopted in concrete and SFRC as controls for comparisons with ECC overlay specimens. The steel fiber for SFRC was ``I.S fiber’’, straight with indented surfaceand rectangular cross-section (0.5* 0.5 mm2), 30 mm in length. An investigation using a steel fiber with hooked ends had already been performed in the previous study [5]. Polyethylene fiber (Trade name Spectra 900) with 19 mm length and 0.038 mm diameter was used for ECC. The elastic modulus, the tensile strength and the fiber density of Spectra 900 were 120 GPa, 2700 MPa and 0.98 g/cm3, respectively. Two different ECCs were used with different water/cement ratios. The mechanical properties of the base concrete and the repair materials are shown in Table 3. The tensile strain capacity of the ECC materials are not measured, but are estimated to be in excess of 3% based on test results of similar materials [2].An MTS machine was used for loading. Load and load point displacement were recorded. The loading rate in this experiment was0.005 mm/s. After the final failure of specimens, interface crack (extension) lengths were measured at both (left and right) sides of a specimen as the distance from a initial notch tip to a propagated crack tip along the interface between the base concrete and the repair material.2.2. Specimen preparationMost of the specimen preparation procedures followed those of the previous work [5]. The base concrete was prepared by cutting a concrete block (see Fig. 4(a)) into four pieces (see Fig. 4(b)) using a diamond saw. Two out of the four pieces were usedfor one smooth surface repairspecimen. In order to make a rough surface, a cut surface was roughened uniformly with ascarifier for 30 s. To prepare a repair specimen in the form of an overlay system, a repair material was cast against either the smooth surface or the rough surface of the base concrete blocks (see Fig. 5). Special attention was paid both to maintain cleanliness and to provide adequate moisture on the base concrete surface just before the casting. In two of the concrete overlay specimens, lubricant was sprayed on the smooth surface just before concrete casting. The initial notch and joint were made by applying a smooth tape on the base concrete before casting the repair materials(see Fig. 4(c)).The specimens were cured for 4 weeks in water. Eventually, the base concrete was cured for a total of 8 weeks, and repair materials were cured for 4 weeks in water. The specimens were dried for 24 h before testing.3. Results and discussion3.1. Comparison of the ECC overlay system with the control systemsFig. 6 shows the representative load-deflection curves in each test case. The overall peak load and deflection at peak load are recorded in Table 4. In the ECC overlay system, the deflections at peak load, which reflect the system ductility, are considerably larger than those of both theconcrete overlay (about one order of magnitude higher) and the SFRC overlay system (over five times). These results show good agreement with the previous results [5]. Moreover, it is clear fromFig. 6 that the energy absorption capacity in the ECC overlay system is much enhanced when it is compared with the other systems. This significant improvement in ductility and in energyabsorption capacity of the ECC overlay system is expected to enhance the durability of repaired structures by resisting brittle failure. The ECC overlay system failed without spalling ordelamination of the interface, whereas, both the concrete and SFRC overlay systems failed by spalling in these experiments (Fig. 7).3.2. Influence of surface preparationBoth in the concrete overlay system and the SFRC overlay system, the peak load and thedeflection at peak load do not show significant differences between smooth surface specimen and rough surface specimen (Table 4). Thetypical failure mode for both overlay systems (for smooth surface) is shown in Fig. 7. In the concrete overlay specimen with lubricant on the interface, delamination between repair concrete and substrate occurred first, followed by a kinked crack which propagates unstably to the surface of the repair concrete. On the other hand, in the concrete overlay system without lubricant, the initial interface crack kinked out from the interface into the repair concrete with a sudden load drop, without any interface delamination. The fractured halves of the specimens separated completely in both smooth surface specimens and rough surfacespecimens. In the SFRC overlay system, the initial interface crack also kinked out into the SFRC and the load decreased gradually in both surface conditions of specimen. In all these repairsystems, a single kink-crack always leads to final failure, and the influence of surface preparation is not reflected in the experimental data. Instead, only the fracture behavior of the repair material (concrete versus SFRC) are revealed in the test data. These specimen failures are characterized bya single kinked crack with immediate softening following elastic response.。