Login | Register

Integrated Decision Support System for Bridge Type Selection at Conceptual Design Stage

Title:

Integrated Decision Support System for Bridge Type Selection at Conceptual Design Stage

Otayek, Elie (2016) Integrated Decision Support System for Bridge Type Selection at Conceptual Design Stage. PhD thesis, Concordia University.

[img]
Text (application/pdf)
Otayek_PhD_S2018.pdf - Accepted Version
Restricted to Repository staff only until 31 December 2019.
Available under License Spectrum Terms of Access.
20MB

Abstract

Selecting a new bridge type at the conceptual design phase is subject to many weaknesses in the processes conducted. Given that the engineers’ decisions are based on their subjectivity, it is worthwhile to establish a Decision Support System (DSS) to effectively address different problems of consistency in decisions. In designing a new bridge, many factors, such as the cost and aesthetic appearance of the bridge, have to be considered due to their ability to affect the final decision. Generally, decision-makers will base their final design decisions on those factors as well as on human subjectivity.
The objective of this research is to propose a methodology to develop systematic procedures that can help decision-makers select the most appropriate bridge type with its diverse components and to forecast its Life-Cycle Cost (LCC) and other characteristics such as the level of public satisfaction and the environmental sustainability of the selected bridge type. The proposed methodology integrates a decision support system with a relevant data structure within an artificial intelligence (AI) environment and bridge information management tools in order to reduce the impact of human subjectivity on the decisions taken during the conceptual design stage of a bridge’s life. The Artificial Neural Network (ANN) with its back-propagation algorithm is adopted in order to identify the appropriate solution by setting up its engine guidelines. Elements of the ANN layers (Engine model) which include: input, hidden and output layers, have to be described based on a systematic and standardized process. The proposed methodology has the potential to be used at lower levels to determine other bridge components such as vertical structures, foundations, and connection types. The objectives of the proposed methodology are as follows: (a) Highlighting the influence of human subjectivity on the decision-making process; (b) Listing and ranking the potential alternatives in term of their performance criteria; (c) Ensuring equivalent and fair consideration for selected factors affecting the decision, and especially reducing the possibility of missing or overlooking the impact of some factors that could be ignored while proceeding with making the right decision by using conventional decision-making approaches; and (d) Developing a systematic methodology that can be considered as a guideline for further use within any decision-making environment, based on a relevant historical database and experts’ input.
For public benefit, governmental and private agencies may use this DSS in order to provide a suitable solution abiding by different opinions in a systematic way taking into consideration the factors that have most influence.
A case study has been conducted with appropriate questionnaires to collect the needed data from experts, the public and previous project sites. This case study has shown the influence of decision maker subjectivity and how it could be controlled by inducing expert opinions through questionnaires to collect valuable data that have influence on the final decision. Also, data related to existing similar projects in the same area have been collected and used in order to show their influence on the results. Data were manipulated in order to analyze them and to show their accuracy and influence on the results. For that, a sensitivity analysis has been conducted in order to determine how the final decision could be affected by a fluctuation in the decision maker opinions contained in the input data for the proposed method.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Building, Civil and Environmental Engineering
Item Type:Thesis (PhD)
Authors:Otayek, Elie
Institution:Concordia University
Degree Name:Ph. D.
Program:Building Engineering
Date:October 2016
Thesis Supervisor(s):Haghighat, Fariborz and Jrade, Ahmad and Zhu, Zhenhua
Keywords:artificial intelligence, Conceptual Design Phase, ANN, Bridges.
ID Code:983478
Deposited By: ELIE OTAYEK
Deposited On:05 Jun 2018 14:49
Last Modified:05 Jun 2018 14:49

References:

Abu Dabous S. , August 2008 “A Decision Support Methodology for Rehabilitation management of Concrete Bridges”, Presented in Partial Fulfillment of the requirements for the degree of Doctor of Philosophy, Building Civil and Environmental Engineering, Concordia University, Montreal. (Thesis)
AISC, 2008, “Steel Bridge Design Handbook – Chapter 7, Selecting the Right Bridge Type”; National Steel Bridge Alliance, http://www.aisc.org/contentNSBA.aspx?id=20244.
AISC, 2008, “Steel Bridge Design Handbook – Chapter 8, Stringer Bridges”; National Steel Bridge Alliance, http://www.aisc.org/contentNSBA.aspx?id=20244.
Al Ghorbanpoor and Dudek J., 2007, “Bridge Integrated Analysis and Decision Support: Case Histories”; Wisconsin Department of Transportation, Wisconsin Highway Research Program, Project No. 0092-04-15
Al Ghorbanpoor and Puerto S. , 2011, “Bridge Integrated Analysis and Decision Support Phase II”; Wisconsin Department of Transportation, Wisconsin Highway Research Program, Project No. 0092-04-15
Al-Hajj A. and Aoud G., 1999, “The development of an integrated life cycle costing. model using object oriented and vr technologies. An integrated life cycle costing model”; Institute for Research in Construction, Ottawa ON, K1A 0R6,Canada, pp. 2901-2908
Arain F. M. and Pheng L. S., 2006, “A Knowledge-Based System as a Decision Making Tool for Effective Management of Variations and Design Improvement: Leveraging on Information Technology Applications”; ITcon, Special Issue Architectural Informatics, http://www.itcon.org/2006/27, Vol. 11, pp 373-392.
Bridgeman, 2012, “About bridges”; nireland.
Chang, P.L., and Chen, Y.C. (1994), “A fuzzy multi-criteria decision making method for technology transfer strategy selection in biotechnology,” Fuzzy Sets and Systems, Vol.63, No.2, pp.131-139.
Chen L. and Weng M., 2003, “A Fuzzy for Exploiting Quality Function Deployment”; Mathematical and Computer Modelling, Vol. 38, Issue 5, pp 559-570.
Chen S. S., Li J. and Tangirala, V., 2006, “Accelerating the design and delivery of bridges with 3D bridge information modeling: pilot study of 3D-centricmodeling processes for integrated designand construction of highway bridges”; Transportation Research Board of the national academies, Final report for highway IDEA project 108.
Chen W. and Duan L., 2000, “Bridge Engineering HandBook”; Library of CongressCataloging-in-publication Data, by CRC press LLC, ISBN: 0-8493-7434-0.
Cho A., 2009 “Ten Minutes with the Godfather of Bridge Information Modeling”; http://enr.construction.com/people/interviews/2009/0812-ChenShirole.asp, 2012
Choi C. K. and Choi I. H., 1993, “An Expert System for Selecting Types of Bridges”; Computers & Structures Vol. 48, No.2, pp. 183-192
De Brito J. and Branco F.A., 1998, “Concrete Bridge Management: From Design to Maintenance”; Practice periodical on structural design and construction, ASCE, ISSN 1084-0680/98/0002-0068-0075, paper N 15740, Vol. 3, No. 2,pp 68-75.
Decker K., 2000, “Conceptual Design of Concrete Structures”; Master’s Thesis, Department of Structural Engineering, Chalmers University of Technology, Publication no. 00:4, Geoteborg, Sweden.
Delony E., 1996, “Context for Word Heritage Bridges”; A joint publication with TICCIH, http://www.icomos.org/studies/bridges.htm.
Deshpande J. M. and Mukherjce A., 1993 “Application of Artificial Neural Network in Structural Design Expert Systems”; Pergamon, Elsevier Science Ltd, Computers & Structures, Vol. 54, No. 3, p.p. 367-375
Dogan S. Z., Arditi D. and Gunaydin H. M., 2006, “Determining Attribute Weights in a CBR Model for Early Cost Prediction of Structural System”; ASCE, Journal of Construction Engineering and Management, Vol. 132, No. 10, pp. 1092-1098
Don P., 2009, “Bridge Information Modelling To Cover A Complete Set Of Processes”; CE&CR, Asia Bently Systems, pp 84-86
Elmasri R. and Navathe S. B., 2016, “FUNDAMENTALS OF Database Systems”; Pearson, Seventh Edition.
El-Sawah H. and Moselhi O., 2014, “Comparative Study In The Use Of Neural Networks For Order Of Magnitude Cost Estimating In Construction”; ITCON, Journal of Information Technology in Construction - ISSN 1874-4753, 2014/27.
Encyclopedia Article, 2008 “Highway Bridge”; McGraw-Hill’s access Science, Encyclopedia of Science& Technology Online,
http://books.mcgrawhill.com/EST10/site/spotlight/automobiles/articles/HighwayBridge.pdf.
Engstrom B., 2002 “SS-intuitive phase_10.pdf”; Structural systems course, Chalmers University of Technology.
Fakhrahmad S. M. and Jahromi Z. M., 2009, “A New Rule-weight Learning Method based on Gradient Descent”; WCE 2009, Proceeding of the World Congress on Engineering, ISBN: 978-988-17012-5-1, Vol. I
Fausett L., 1994“Fundamentals of Neural Networks – Architectures, Algorithms, and Applications”; Prentice-Hall, Inc. Upper Saddle River, NJ, USA, ISBN:0-13-334186-0
FHWA, 1991, “Bridge Inspectors Training Manual 90”; U.S. Department of Transportation, Bureau of Public Roads, Washington, D.C., USA
Frangopol D. M., Kong Jung S. and Gharaibeh E. S., 2001, “Reliability-Based Life-Cycle Management of Highway Bridges”;Journal of Computing in Civil Engineering, ASCE, ISSN: 0887-3801/01/0001-0027-0034, Vol. 15, No. 1, pp27-33
Furuta H., Hirokane M.and Ishida K., 2001, “Decision Support System for Bridge Aesthetic Design Using Immune System”; Current and Future trends in bridge design, Construction and Maintenance 2, Thomas Telford, London, pp 328-337
Garrell C., 2012“National Steel Bridge Alliance, Bridge Information Modeling and Integrated Practice”; NSBA, http://ftp.dot.state.tx.us/pub/txdot-info/brg/texas_steel/brim_integrated_practice_garrell.pdf
Gerhenson C., 2003“Artificial Neural Networks for Bigenners”; Cornell University Library
Gottemoeller F. & al., 2009, “Bridge Aesthetic Sourcebook – Practical Ideas for Short and Medium Span Bridges”; Subcommittee on Bridge Aesthetic AFF10 TRB, www.bridgeaesthetics.org
Hauser J. R. and Clausing D., 1988, “The House of Quality”; Harvard Business Review, May-June 1988, Reprint 88307
Heikkila R.,2011“National Guidelines for Bridge Information Modeling and Automation”; I.A.A.R.C., GERONTECHNOLOGY, International journal on the fundamental aspects of technology to serve the ageing society, Vol 11, No 2
Hendrickson C., 2008, “Project Management for Construction, Fundamental concept for Owners, Engineers, Architects and Builders”; Version 2.2, http://pmbook.ce.cmu.edu/
Herman G. A., Trotta B. W. and Peterson James C., 2008, “Patent application: BRIDGE INFORMATION MODELING”; HNTB HOLDINGS LTD., Class Name: Structural Design – USPC Class: 703 1
Hoeckman W. and Nelis O., 2012, “Environmental Implications of Steel Bridge Construction”; Victor Buyck Steel Construction.
Hristakeva M. and Shrestha D., 2003+, “Solving the 0-1 Knapsack Problem with Genetic Algorithmms”; Simpson College, Computer Science Department
Hunt L. R., 2005, “Development of a Rating System for Sustainable Bridges”; Thesis Dissertation, Massachusetts Institute of Technology
Illinois Department of Transportation, 2008, “Bureau of Local Roads and Streets Manual”; IDOT Organization, Division of Highways
International Conference on Sustainable Design and Construction, Kansas City, Missouri, March 23-25, 2011, pp. 457-466
Itoh Y., Sunuwar L., Hirano T., Hammad A. and Nishido T., 2000, “Brigde Type Selection System Incorporating Environmental Impacts”; Journal of Global Environment Engineering, Vol. 6, p.p. 81-101.
Jin Y., 1998, “Conceptual Design of bridge Proposal for Nanjing Yangtze River Crossing of Jing-Hu High Speed Railway”; 13th symposium of bridge and structure engineering division of China Association of Civil Engineering
Jorund J., 2013 “BIM in Bridge Design”, Master Thesis, Norwegian University of Science and Technology, Structural Engineering Department.
Keoleian G. A., Alyssa, K., Richard, C., Gloria, E. H., Michael, L., and Victor C. L., 2005, “Life-Cycle Cost Model for Evaluating the Sustainability of Bridge Decks”; Proceedings of the International Workshop on Life-Cycle Cost Analysis and Design of Civil Infrastructure Systems. Cocoa Beach, Florida (May 8-11, 2005): 143-150
Kivimaki T. and Heikkila R., 2009, “Integrating 5D Product Modelling to On-Site 3D Surveying of Bridges”;26th International Symposium on Automation and Robotics in Construction (ISARC 2009), pp445-450
Kivimaki T., and Heikkila R., 2010, “Bridge Information Modeling (BrIM) and Model Utilization at Worksites in Finland”; 27th International Synposium on Automation and Robotics in Construction (ISARC 2010), pp505-513
Krose B. and Smagt P. V., 1996, “An Introduction to Neural Networks”; 8th Edition, University of Amsterdam.
Lee T.L., Jeng D.S., Zhang G.H. and Hong J.H.,2007“Neural Network Modeling for Estimation of Scour Depth Around Bridge Piers”; Science Direct, Journal of Hydrodynamics, Ser.B, 2007, Vol. 19, Issue 3, pp 378-386
Liu L., Shahi A., Haas C. T., Goodrum P., and Caldas C. H., 2017, “Validation Methodologies and Their Impact in Construction Productivity Research”; ASCE, Journal of Construction Engineering and Management, Volume 140 Issue 10, pp 1-10
- See more at: http://ascelibrary.org/doi/abs/10.1061/(ASCE)CO.1943-7862.0000882#sthash.c0rkKQAP.dpuf
Madanat S. and Lin D., 2000, “Bridge Inspection Decision Making Based on Sequential Hypothesis Testing Methods”; Transportation Research Record, Vol. 1697, Paper 00.0269, pp 14-18
Mahmoud M. K., 2015, “Sustainable Bridge Structures”; CRC Press, Taylor & Francis Group.
Maier F. and Brinckerhoff P., 2012, “Bridge Information Modeling: Opportunities, Limitations, and Spanning the Chasm with Current Tools”; Autodesk University, AU, C11529 Syllabus.
Malekly H., Mousavi S. M., Hashemi H., 2010, “A fuzzy integrated methodology for evaluating conceptual bridge design”; Expert System with Application, Elsevier, ISSN: 0957-4174, Vol. 37, Issue 7, pp 4910-4920
Maryland Department of Transportation, 2005 “Aesthetic Bridges, Users Guide”; State Highway Administration SHA, Office of Bridge Development, Aug. 1993 Revised Jan. 2005.
Marzouk M.d, Nouth A. and El-Said M., 2013“Developping green bridge rating system using Simos’s procedure”; HBRC journal (2013), http://dx.doi.org/10.1016/j.hrbcj.2013.10.001.
Marzouk M. M. and Hisham M., 2011“Bridge Information Modeling in Sustainable Bridge Management”; ICSDC 2011, ASCI 2012, Proceedings of the Mayer Judy, Barker James M. and Ishmael Ken, 2001, “Public Participation and Bridge Type Selection”; Transportation Research Board,ISSN: 0361-1981, ISBN 0309072328, Issue No. 1770/2001, pp173-180
MATLAB, 2015 “Machine Learning Challenges: Choosing the Best Model and Avoiding Overfitting”, MathCad.
Miles J.C. and Moore C.J., 1991, “An Expert System for the Conceptual Design of Bridges”; Computer & Structures, Vol. 40, No1, pp 101-105
Minnesota Department of Transportation, 2011, “Bridge Inspection Field Manual”; MnDOT Bridge Office, Version 1.9 – November, 2011
Moore C. J. and Lehane M.S, 1999, “Development of a Case Representation Strategy for a Bridge Design Case Base”; Engineering Structures, Vol. 21, Issue 3, pp 219-231
Moore C. J., Miles J. C. and Evans S. N., 1996, “Innovation Computational Support in Bridge Aesthetics”; Transportation Research Record 1549
Moore C. J., Miles J. C. and Rees W. G., 1996, “Decision Support for Conceptual Bridge Design”; Published by Elsevier Science Limited, Artificial Intelligence in Engineering, Vol. 11, pp 259-272
Moselhi O. and Alshibani A., 2013, “Schedule Compression Using Fuzzy Set Theory and Contractors Judgment”; ITCON, Journal of Information Technology in Construction - ISSN 1874-4753, 2013/4.
Moselhi O. and Lorterapong P., 1995, “Fuzzy Vs Probabilistic Scheduling”; IMBiGS, Automation and Robotics in Construction XII.
Mousoulides N. G., 2008, “Modeling as a Bridge Between Real World Problems and School Mathematics”; Department of Education, University of Nicosia
Munakata T., 2008, “Fundamentals of the New Artificial Intelligence – Neural, Evoltionary, Fuzzy and More”; second edition, TextsIn Computer Science, Springer, ISBN: 978-1-84628-838-8.
Nedev G. and Khan U., 2011, “Guidelines for conceptual design of short-span bridges”; Master’s Thesis Seminar, Charlmers University of Technology.
Niemeyer S., 2003 “Conceptual Design in building industry”; Master’s Thesis, Department of Structural Engineering, Chalmers University of Technology, Publication no. 03:6, Geoteborg, Sweden.
NMDOT, 2005, “Bridge Procedures & Design Guide”; New Maxico Department of TRANSPORTATION.
ODOT, 2011, “Bridge Terms Definitions”; Ohio Department of Transportation, http://www.dot.state.oh.us/Divisions/Communications/BridgingtheGap/Pages/BridgeTermDefinitions.aspx
Ogilvie T. and Shibley R., 2005, “Peace Bridge Recommendation Jury – Bridge Type Background Paper”; Buffalo and Fort Erie Public Bridge Authority, U.S. Department of Transportation Federal Highway Administration & New York State Department of Transportation.
Ohioa Department of Transportation, 2010, “Manual of Bridge Inspection”; State of Ohio, department of Transportaion, ORC 5501.47
Opoku-Darkwa P., 2011, “Environmental and Social Impact Assessment Summary”; OITC.2, Kazungula Bridge, Botswana-Zambia; Project P-Z1-DBO-031.
Paslawski J., 2008, “Flexibility Approach in Construction Process Engineering”; Technological and economic development of Economy Baltic Journal on Sustainability, 14(4): 518–530; ISSN 1392-8619 print/ISSN 1822-3613 online;
Ricketts J. T., Lften M. K. and Merritt F. S., 1999, “Standard Handbook for Civil Engineers”; fifth edition, McGraw-Hill STANDARD HANDBOOKS
Rojas R., 1996 “Neural Networks – The back-propagation algorithm, chapter 7”; Springer-Verlag, Berlin, pp. 151-184.
Ryall M. J., 2001 “Bridge Management”; Plant A Tree, first published, ISBN 0 7506 5077 X.
Sadek A. W., Spring G., and Smith, B. L., 2003, “Toward More Effective Transportation Applications of Computational Intelligence Paradigms”. In Transportation Research Record: Journal of the Transportation Research Board, No. 1836, Transportation Research Board of the National Academies, Washington, D.C. pp. 57–63.
Sankar K. P., 1992, “Multilayer Perceptron, Fuzzy Sets, and Classification”; IEEE Transactions on Neural Network, Vol. 3, No. 5, pp 683-697
Sharon B., 1997, “Environmental Impact Assessment”; Ecodate, july 1997, pp 3-8.
Shim C. S., Yun, N. R., Song H.H., 2011, “Application of 3D Bridge Information Modeling to Design and Construction of Bridges”; The Twelfth East Asia-Pacific Conference on Structural Engineering and Construction, Procedia Engineering, 14 (2011) 95–99.
Shirole A. M. et al., 2009 “BrIM for project delivery and life-cycle: state of the art”; Taylor & Francis, Bridge Structures, Vol. 5, No 4, pp. 173-187
Smith R., Bush R. J. and Schmoldt D. L., 1994, “A Hierarchical Model and Analysis of Factors Affecting the Adoption of Timber as a Bridge Material”; Wood and Fiber Science, Vol. 23, Issue 3, pp 225-238
Spencer S., 2012 “Bridge Information Modeling (BrIM), Introducing Bentley’s Initiative to Improve Bridge Project Delivery”; BENTLY,ftp://ftp2.bentley.com/dist/collateral/CMS/EMEA/misc/BrIM.pdf
Srinivas V., Ramanjaneyulu, K., 2007, “An integrated approach for optimum design of bridge decks using genetic algorithms and artificial neural networks”; Elsevier, Advances in Engineering Software, Vol. 38, Issue 7, pp 475-487
Stone D. N. and Schkade D. A., 1989, “Numeric and Linguistic Information Representation in Multiattribute Choice”, Bebr Faculty Working Paper No. 89-1608.
Stone R., 2012 “Tipniques: Bridge Information Modeling (BrIM) Baby Steps”; http://www.augi.com/library/tipniques-bridge-information-modeling-brim-baby-steps
Sutton R. S. and Barto A. G., 2005, “Reinforcement learning: An introduction”; MIT Press, Cambridge, Massachusetts
Takagi H., 1997, “Introduction to Fuzzy Systems, Neural Networks, and Genetic Algorithms”; Kluer Academic Publishers, Intelligent systems, Ch1, pp.1-33
Tang M., 2007, “Evolution of Bridge Technology”; IABSE Symposium, Weimar 2007
Thoft-Christensen P., 2009, “Life-Cycle Cost-Benefit (LCCB) analysis of bridges from a user and social point of view”; Taylor & Francis, Structure and Infrastructure Engineering, Vol. 5, Issue. 1, pp 49-57
Thompson P. D. and Shepard, R. W., 2000, “AASHTO Commonly-Recognized Bridge Elements”; National Workshop on commonly recognized measures for maintenance.
TRB, 2007 “Artificial Intelligence in Transportation – Information for Application”; Transportation Research Board, Artificial Intelligence and Advanced Computing Applications Committee, Transportation Research Circular E-C113.
US Department of Transportation, 2002, “Life-Cycle Cost Analysis Primer”; U.S. Department of transportation Federal Highway Administration Office of Asset Management
Woodward R.J., 2001, “Bridge Management Systems: Extended Review of Existing Systems and outline framework for a European System”; Brime PL97-2220
Yao Y., Zhou X. & Dong P., 2011, “Management Decision-Making Based on Fuzzy Neural Network”; E-Business and E-Government (ICEE), 2010 International Conference, ISBN 978-0-7695-3997-3, pp 2454-2456
Zhang M. and Smart W., 2005, “Learning Weights in Genetic Programs Using Gradient Descent for Object Recognition”; @Sprnger-Verlag Berlin Heidelberg, F. Rothlauf et al.(Eds.): EvoWorkshops 2005, LNCS 3449, pp. 417-427
All items in Spectrum are protected by copyright, with all rights reserved. The use of items is governed by Spectrum's terms of access.

Repository Staff Only: item control page

Downloads per month over past year

Research related to the current document (at the CORE website)
- Research related to the current document (at the CORE website)
Back to top Back to top