Intelligent and Soft Computing in Infrastructure Systems Engineering: Recent Advances PDF
Preview Intelligent and Soft Computing in Infrastructure Systems Engineering: Recent Advances
KasthuriranganGopalakrishnan,HalilCeylan,andNiiO.Attoh-Okine(Eds.) IntelligentandSoftComputinginInfrastructureSystemsEngineering StudiesinComputationalIntelligence,Volume259 Editor-in-Chief Prof.JanuszKacprzyk SystemsResearchInstitute PolishAcademyofSciences ul.Newelska6 01-447Warsaw Poland E-mail:[email protected] Furthervolumesofthisseriescanbefoundonour Vol.248.CheePengLim,LakhmiC.Jain,and homepage:springer.com SatchidanandaDehuri(Eds.) InnovationsinSwarmIntelligence,2009 Vol.237.GeorgeA.PapadopoulosandCostinBadica(Eds.) ISBN978-3-642-04224-9 IntelligentDistributedComputingIII,2009 Vol.249.WesamAshourBarbakh,YingWu,andColinFyfe ISBN978-3-642-03213-4 Non-StandardParameterAdaptationforExploratoryData Vol.238.LiNiu,JieLu,andGuangquanZhang Analysis,2009 Cognition-DrivenDecisionSupportforBusinessIntelligence, ISBN978-3-642-04004-7 2009 Vol.250.RaymondChiongandSandeepDhakal(Eds.) ISBN978-3-642-03207-3 NaturalIntelligenceforScheduling,PlanningandPacking Vol.239.ZongWooGeem(Ed.) Problems,2009 HarmonySearchAlgorithmsforStructuralDesign ISBN978-3-642-04038-2 Optimization,2009 Vol.251.ZbigniewW.RasandWilliamRibarsky(Eds.) ISBN978-3-642-03449-7 AdvancesinInformationandIntelligentSystems,2009 Vol.240.DimitriPlemenosandGeorgiosMiaoulis(Eds.) ISBN978-3-642-04140-2 IntelligentComputerGraphics2009,2009 Vol.252.NgocThanhNguyenandEdwardSzczerbicki(Eds.) ISBN978-3-642-03451-0 IntelligentSystemsforKnowledgeManagement,2009 Vol.241.Ja´nosFodorandJanuszKacprzyk(Eds.) ISBN978-3-642-04169-3 AspectsofSoftComputing,IntelligentRoboticsandControl, Vol.253.RogerLeeandNaohiroIshii(Eds.) 2009 SoftwareEngineeringResearch,Managementand ISBN978-3-642-03632-3 Applications2009,2009 Vol.242.CarlosArtemioCoelloCoello, ISBN978-3-642-05440-2 SatchidanandaDehuri,andSusmitaGhosh(Eds.) Vol.254.KyandoghereKyamakya,WolfgangA.Halang, SwarmIntelligenceforMulti-objectiveProblemsinData HerwigUnger,JeanChamberlainChedjou, Mining,2009 NikolaiF.Rulkov,andZhongLi(Eds.) ISBN978-3-642-03624-8 RecentAdvancesinNonlinearDynamicsand Vol.243.ImreJ.Rudas,Ja´nosFodor,and Synchronization,2009 JanuszKacprzyk(Eds.) ISBN978-3-642-04226-3 TowardsIntelligentEngineeringandInformationTechnology, Vol.255.CatarinaSilvaandBernardeteRibeiro 2009 InductiveInferenceforLargeScaleTextClassification,2009 ISBN978-3-642-03736-8 ISBN978-3-642-04532-5 Vol.244.NgocThanhNguyen,Rados lawPiotrKatarzyniak, Vol.256.PatriciaMelin,JanuszKacprzyk,and andAdamJaniak(Eds.) WitoldPedrycz(Eds.) NewChallengesinComputationalCollectiveIntelligence, Bio-inspiredHybridIntelligentSystemsforImageAnalysis 2009 andPatternRecognition,2009 ISBN978-3-642-03957-7 ISBN978-3-642-04515-8 Vol.245.OlegOkunandGiorgioValentini(Eds.) Vol.257.OscarCastillo,WitoldPedrycz,and ApplicationsofSupervisedandUnsupervisedEnsemble JanuszKacprzyk(Eds.) Methods,2009 EvolutionaryDesignofIntelligentSystemsinModeling, ISBN978-3-642-03998-0 SimulationandControl,2009 Vol.246.ThanasisDaradoumis,SantiCaballe´, ISBN978-3-642-04513-4 JoanManuelMarque`s,andFatosXhafa(Eds.) Vol.258.LeonardoFranco,DavidA.Elizondo,and IntelligentCollaborativee-LearningSystemsand Jose´M.Jerez(Eds.) Applications,2009 ConstructiveNeuralNetworks,2009 ISBN978-3-642-04000-9 ISBN978-3-642-04511-0 Vol.247.MonicaBianchini,MarcoMaggini,FrancoScarselli, Vol.259.KasthuriranganGopalakrishnan,HalilCeylan,and andLakhmiC.Jain(Eds.) NiiO.Attoh-Okine(Eds.) InnovationsinNeuralInformationParadigmsand IntelligentandSoftComputinginInfrastructureSystems Applications,2009 Engineering,2009 ISBN978-3-642-04002-3 ISBN978-3-642-04585-1 Kasthurirangan Gopalakrishnan,Halil Ceylan, and Nii O.Attoh-Okine (Eds.) Intelligent and Soft Computing in Infrastructure Systems Engineering RecentAdvances 123 Prof.KasthuriranganGopalakrishnan Dr.NiiO.Attoh-Okine 354TownEngineeringBldg. 343BDuPontHall Dept.ofCivil,Constr.&Env.Engg. Dept.ofCivilandEnvironmentalEngineering IowaStateUniversity UniversityofDelaware Ames,IA50011-3232 Newark,DE19716 USA USA E-mail:[email protected] Prof.HalilCeylan 482BTownEngineeringBldg. Dept.ofCivil,Constr.&Env.Engg. IowaStateUniversity Ames,IA50011-3232 USA ISBN 978-3-642-04585-1 e-ISBN 978-3-642-04586-8 DOI 10.1007/978-3-642-04586-8 Studiesin Computational Intelligence ISSN1860-949X Library of Congress Control Number:2009937151 (cid:2)c 2009 Springer-VerlagBerlin Heidelberg Thisworkissubjecttocopyright.Allrightsarereserved,whetherthewholeorpart of the material is concerned, specifically therights of translation, reprinting,reuse ofillustrations, recitation,broadcasting, reproductiononmicrofilm orinanyother way, and storage in data banks. Duplication of this publication or parts thereof is permittedonlyundertheprovisionsoftheGermanCopyrightLawofSeptember9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution undertheGerman Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Typeset&CoverDesign:ScientificPublishing ServicesPvt. Ltd., Chennai, India. Printed in acid-free paper 9 8 7 6 5 4 3 2 1 springer.com Preface The term “soft computing” applies to variants of and combinations under the four broad categories of evolutionary computing, neural networks, fuzzy logic, and Bayesian statistics. Although each one has its separate strengths, the complemen- tary nature of these techniques when used in combination (hybrid) makes them a powerful alternative for solving complex problems where conventional mathe- matical methods fail. The use of intelligent and soft computing techniques in the field of geome- chanical and pavement engineering has steadily increased over the past decade owing to their ability to admit approximate reasoning, imprecision, uncertainty and partial truth. Since real-life infrastructure engineering decisions are made in ambiguous environments that require human expertise, the application of soft computing techniques has been an attractive option in pavement and geomechani- cal modeling. The objective of this carefully edited book is to highlight key recent advances made in the application of soft computing techniques in pavement and geome- chanical systems. Soft computing techniques discussed in this book include, but are not limited to: neural networks, evolutionary computing, swarm intelligence, probabilistic modeling, kernel machines, knowledge discovery and data mining, neuro-fuzzy systems and hybrid approaches. Highlighted application areas include infrastructure materials modeling, pavement analysis and design, rapid interpreta- tion of nondestructive testing results, porous asphalt concrete distress modeling, model parameter identification, pavement engineering inversion problems, sub- grade soils characterization, and backcalculation of pavement layer thickness and moduli. This book belongs to the “Studies in Computational Intelligence (SCI)” series published by Springer Verlag. Each chapter contained in this book has been peer- reviewed by at least two anonymous referees to assure the highest quality. The valuable contributions of the following individuals in assisting with the review process are greatly appreciated: Sunghwan Kim (Iowa State University), Roger W. Meier (The University of Memphis), Fwa Tien Fang (National University of Sin- gapore), P. Chris Marshall (Golder Associates Inc.), Abhisek Mudgal (Iowa State University), and Amit Pande (Iowa State University). VI Preface Researchers and practitioners engaged in developing and applying soft comput- ing and intelligent systems principles to solving real-world infrastructure engi- neering problems will find this book very useful. This book will also serve as an excellent state-of-the-art reference material for graduate and postgraduate students in transportation infrastructure engineering. August 13, 2009 Kasthurirangan (Rangan) Gopalakrishnan Ames, Iowa About This Book The use of intelligent and soft computing techniques in the field of geomechanical and pavement engineering has steadily increased over the past decade owing to their ability to admit approximate reasoning, imprecision, uncertainty and partial truth. Since real-life infrastructure engineering decisions are made in ambiguous environments that require human expertise, the application of soft computing techniques has been an attractive option in pavement and geomechanical model- ing. The objective of this carefully edited book is to highlight key recent advances made in the application of soft computing techniques in pavement and geome- chanical systems. Soft computing techniques discussed in this book include, but are not limited to: neural networks, evolutionary computing, swarm intelligence, probabilistic modeling, kernel machines, knowledge discovery and data mining, neuro-fuzzy systems and hybrid approaches. Highlighted application areas include infrastructure materials modeling, pavement analysis and design, rapid interpreta- tion of nondestructive testing results, porous asphalt concrete distress modeling, model parameter identification, pavement engineering inversion problems, sub- grade soils characterization, and backcalculation of pavement layer thickness and moduli. Researchers and practitioners engaged in developing and applying soft computing and intelligent systems principles to solving real-world infrastructure engineering problems will find this book very useful. This book will also serve as an excellent state-of-the-art reference material for graduate and postgraduate stu- dents in transportation infrastructure engineering. Written for Researchers and practitioners engaged in developing and applying soft computing and intelligent systems principles to solving real-world geomechanical and pave- ment engineering problems. Keywords Pavement engineering; artificial intelligence; artificial neural networks; evolution- ary computing; genetic algorithms; particle swarm optimization; shuffled complex evolution; support vector machines; data mining; rough set; neuro-fuzzy; decision trees; genetic polynomial; relief ranking filter; extended Kalman filter. Contents Rapid Interpretation of Nondestructive Testing Results Using Neural Networks Imad N. Abdallah, Soheil Nazarian .................................. 1 Probabilistic Inversion: A New Approach to Inversion Problems in Pavement and Geomechanical Engineering Rambod Hadidi, Nenad Gucunski.................................... 21 Neural Networks Application in Pavement Infrastructure Materials Sunghwan Kim, Kasthurirangan Gopalakrishnan, Halil Ceylan .......... 47 Backcalculation of Flexible Pavements Using Soft Computing A. Hilmi Lav, A. Burak Goktepe, M. Aysen Lav ...................... 67 Knowledge Discovery and Data Mining Using Artificial Intelligence to Unravel Porous Asphalt Concrete in the Netherlands Maryam Miradi, Andre A.A. Molenaar, Martin F.C. van de Ven........ 107 Backcalculation of Pavement Layer Thickness and Moduli Using Adaptive Neuro-fuzzy Inference System Mehmet Saltan, Serdal Terzi........................................ 177 Case Studies of Asphalt Pavement Analysis/Design with Application of the Genetic Algorithm Bor-Wen Tsai, John T. Harvey, Carl L. Monismith ................... 205 Extended Kalman Filter and Its Application in Pavement Engineering Rongzong Wu, Jae Woong Choi, John T. Harvey ..................... 239 X Contents Hybrid Stochastic Global Optimization Scheme for Rapid Pavement Backcalculation Kasthurirangan Gopalakrishnan..................................... 255 Regression and Artificial Neural Network Modeling of Resilient Modulus of Subgrade Soils for Pavement Design Applications Pranshoo Solanki, Musharraf Zaman, Ali Ebrahimi .................... 269 Application of Soft Computing Techniques to Expansive Soil Characterization Pijush Samui, Sarat Kumar Das, T.G. Sitharam ...................... 305 Author Index................................................... 325 Rapid Interpretation of Nondestructive Testing Results Using Neural Networks Imad N. Abdallah1 and Soheil Nazarian2 1 Center for Transportation Infrastructure Systems, University of Texas at El Paso, El Paso, Texas [email protected] 2 Center for Transportation Infrastructure Systems, University of Texas at El Paso, El Paso, Texas [email protected] Abstract. Artificial neural network tools for structural pavement evaluation have been de- veloped to facilitate the determination of the integrity of existing flexible pavements. With the onset of the movement toward more mechanistic pavement design, such as Mechanistic Empirical Pavement Design Guide, nondestructive testing techniques play a major role to determine properties of pavement structures. Conventional methods such as backcalculat- ing the layer properties are complex and either require a significant computational effort and/or frequent operator intervention. Studies are presented that show the power of artifi- cial neural networks to estimate pavement layer properties and allow for capabilities in developing pavement performance curves and for estimating and monitoring remaining life. 1 Introduction Many highways agencies are attempting to incorporate this new design process in the state-of-practice. Currently, nondestructive testing (NDT) devices such as the Falling Weight Deflectometer (FWD) and the Seismic Pavement Analyzer (SPA) are available for collecting field data. Each of these technologies provides support to the design process. These tools have significantly contributed to pavement maintenance and rehabilitation strategies. In this chapter, a discussion of the conventional use of NDT data is described, followed by an overview of the use of the artificial neural networks to supplant the conventional methods. Finally, two studies are presented to demonstrate the power of incorporating ANN in pavement evaluation. 2 Conventional Analysis of NDE Programs Using FWD and SPA 2.1 Estimating Modulus of Pavement Layers Using Falling Weight Deflectometer The Falling Weight Deflectometer is the most popular NDT device. As shown in Figure 1a, the FWD applies an impulse load to the pavement and seven or more K. Gopalakrishnan et al. (Eds.): Intel. & Soft Comp. in Infra. Sys. Eng., SCI 259, pp. 1–19. springerlink.com © Springer-Verlag Berlin Heidelberg 2009
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