1887
Volume 2017, Issue 1
  • E-ISSN: 2310-516X

Abstract

The aim of this study was to strengthen the engineering curriculum by integrating innovative electrical and computer engineering (ECE)-specialized artificial intelligence (AI) methodologies and algorithms into traditional civil engineering (CE) problem-solving methods. An interactive and comprehensive knowledge-based expert system (KBES) was developed to document, compare, and analyze cutting-edge AI applications in the field of CE. With a large amount of successful/unsuccessful AI applications being tried and tested in the CE field, this unique intelligent database can be used as the platform and educational media for the development and implementation of curricula for the problem-based learning approach to bridge the gap in the current curricula between conventional mathematics, physics, engineering methods and state-of-the-art AI techniques. This study is the first of its kind to (1) develop an intelligent KBES platform to increase the intellectual rigor, breadth, and depth of undergraduate engineering study and lay a foundation for students pursuing master's degree or PhD in engineering; (2) establish a new interdisciplinary AI curriculum as a capstone course, enrich existing curricula by integrating case studies of AI applications into different levels of undergraduate CE courses, and include knowledge automation software in an ECE course; (3) foster interdisciplinary academic setting that will introduce latest state-of-the-art AI applications to undergraduate students and facilitate their early involvement in research. A brief description of the comprehensive literature search is presented, followed by the proposal of methodologies for the development of the KBES and curriculum. Furthermore, a case study is described to demonstrate the effectiveness and advantages of introducing AI tools into the syllabus of a sophomore CE core course. Students' experiences were assessed and evaluated. The results of the analysis showed that the interdisciplinary curriculum could significantly increase students' awareness on the need for knowledge acquisition, which, in turn, will enhance the learning outcome. The integration of innovative theories and practical applications also improves the problem-solving and critical thinking skills of engineering students, broadens their horizons to new technology, and fosters interdisciplinary settings to better prepare them for diverse and multidisciplinary workforce requirements.

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2017-11-24
2019-11-14
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References

  1. Cheng M. Y., Tsai H. C., Ko C. H.Chang W. T. 2008;. Evolutionary fuzzy neural inference system for decision making in geotechnical engineering. Journal of Computing in Civil Engineering, 22:4, 272280.
    [Google Scholar]
  2. Das B.Sobhan K. 2013;. Principles of geotechnical engineering 8th ed.. Stamford, CT: Cengage Learning.
    [Google Scholar]
  3. Eiben A. E.Smith J. E. 2010;. Introduction to evolutionary computing . 2. Berlin: Springer.
    [Google Scholar]
  4. Feigenbaum E. A. 1984;. Knowledge engineering. Annals of the New York Academy of Sciences, 426:1, 91107.
    [Google Scholar]
  5. Govindaraju R. S. 2000;. Artificial neural networks in hydrology. II: Hydrologic applications. Journal of Hydrologic Engineering, 5:2, 124137.
    [Google Scholar]
  6. Huber M. T.Hutchings P. 2004;. Integrative Learning: mapping the terrain. Washington, DC: Association of American Colleges and Universities and the Carnegie Foundation for the Advancement of Teaching. Published by the Association of American Colleges and Universities 1818 R Street, NW, Washington, DC 20009, ISBN 0-11696-98-9.
  7. Karlaftis M. G., Easa S. M., Jha M. K.Vlahogianni E. I. 2012;. Design and construction of transportation infrastructure. Transportation Research Circular E-C168: Artificial Intelligence Applications to Critical Transportation Issues, November. ISSN 097-8515.
    [Google Scholar]
  8. Lopatto D. 2008;. Exploring the benefits of undergraduate research experiences: The SURE survey. In R. Taraban & R. L. Blanton Eds., Creating effective undergraduate research programs in science . 112132, New York: Teachers College Press.
    [Google Scholar]
  9. Lu P., Chen S.Zheng Y. 2012;. Artificial intelligence in civil engineering., Mathematical Problems in Engineering, 2012.
    [Google Scholar]
  10. Mahdi Amiripour S., Mohaymany A.Ceder A. 2014;. Optimal modification of urban bus network routes using a genetic algorithm. Journal of Transportation Engineering, 141:3, 401408.
    [Google Scholar]
  11. Marzouk M.Amin A. 2013;. Predicting construction materials prices using fuzzy logic and neural networks. Journal of Construction Engineering and Management, 139:9, 11901198.
    [Google Scholar]
  12. NSSE. 2012;. 2012 Annual Survey Results, National Survey of Student Engagement. Promoting student learning and institutional improvement: Lessons from NSSE at 13. Bloomington, IN, Indiana University Center for Postsecondary Research.
    [Google Scholar]
  13. Russell S.Norvig P. 2009;. Artificial intelligence: A modern approach 3rd ed.. Englewood Cliffs, NJ: Prentice Hall.
    [Google Scholar]
  14. Schmidt J. W.Costantino T. 2012;. Foundations of knowledge base management: contributions from logic, databases, and artificial intelligence applications. Springer Science and Business Media, Incorporated, Berlin, Heidelberg.
    [Google Scholar]
  15. Schreiber G. 2000;. Knowledge engineering and management: The commonKADS methodology. The MIT Press, Cambridge, Massachusetts.
  16. Senouci A.Al-Derham H. R. 2008;. Genetic algorithm-based multi-objective model for scheduling of linear construction projects. Advances in Engineering Software, 39:12, 10231028.
    [Google Scholar]
  17. Taghavifar H., Modarres Motlagh A., Mardani A., Hassanpour A., Haji Hosseinloo A., Taghavifar L.Wei C. 2016;. Appraisal of Takagi–Sugeno type neuro-fuzzy network system with a modified differential evolution method to predict nonlinear wheel dynamics caused by road irregularities. Transport, 31:2, 211220.
    [Google Scholar]
  18. Tayfur G., Erdem T. K.Kırca Ö. 2013;. Strength prediction of high strength concrete by fuzzy logic and artificial neural networks. Journal of Materials in Civil Engineering, 26:11, 401407.
    [Google Scholar]
  19. Turban E. 1990;. Decision support and expert systems: Management support system 2nd ed.. MacMillan Publishing Company, Prentice Hall PTR, Upper Saddle River, NJ, USA.
    [Google Scholar]
  20. Yegnanarayana B. 2009;. Artificial neural networks. PHI Learning Pvt. Ltd. New Delhi, Indian.
    [Google Scholar]
  21. Zadeh L. A. 1965;. Fuzzy sets. Information and Control, 8:3, 338353.
    [Google Scholar]
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