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    David Arditi, PhD - Professor, CAE

    David Arditi, PhD

    Professor, CAE
    Director, Construction Engineering and Management

    Construction Engineering and Management

    Office: AM 229
    3201 South Dearborn Street
    Chicago, IL 60616-3793

    Phone: 312.567.5751
    Fax: 312.567.3519
    Email: arditi@iit.edu
    Web: Personal Website
         

    Expertise

    • All aspects of construction management, engineering and support.

    Education

    • B.S., Civil Engineering; Middle East Technical University, Ankara, Turkey, 1967
    • M.S., Civil Engineering (Construction Engineering); Middle East Technical University, Ankara, Turkey, 1968
    • Ph.D., Civil Engineering (Construction Management); Loughborough University, UK, 1973

    Research

    Descriptions of Current Research

    1. A method is developed to measure progress in different activities automatically by using laser scanning technology.  The system is able to automatically generate interim schedules based on percent complete information obtained automatically from the new tool.
    2. This study looks at the bidding process from the point of view of the construction owner.  It detects unbalanced bids as well as collusive bids by using rigorous statistical analysis.
    3. A visual scheduling and progress management system is developed by using Geographical Information Systems (GIS) that allows the manager to view progress simultaneously on the CPM network (or its bar chart representation) and the 3D drawings of the project, and that allows the manager to make informed managerial decisions.
    4. Advanced linear scheduling systems are developed (ALISS and RUSS).
    5. Different objective functions are used to optimize resource leveling in line-of-balance diagrams.
    6. The factors that affect the selection of the most appropriate delay analysis method are investigated.
    7. A contemporaneous delay analysis method is developed to allow parties to resolve delay claims in a way that is agreeable to all parties involved.
    8. Different prediction models including statistical analysis, artificial neural networks, case-based reasoning, boosted decision trees, and a rule-based classification system using ant colony optimization are used to predict the outcome of construction claims.
    9. A powerful hybrid decision model is developed (Universal Prediction Model) to predict the decisions of the courts in construction litigation.
    10. The electronic retrieval of information (text analytics) from court records is explored.  This information provides data for the prediction models mentioned in Item (8).
    11. The possibilities of using Public Private Partnerships (PPPs) are explored to build and operate large and expensive infrastructure projects in the US.
    12. The optimum combination of materials that provides maximum sustainability at minimum cost is obtained by using genetic optimization.
    13. The adoption of LEED criteria is investigated in developing countries relative to local codes, standards, regulations, and technical knowhow.
    14. An optimal material selection process is developed to support design decisions for LEED certification.
    15. A formal post-mortem analysis is promoted that leads to a “lessons learned database” of construction management issues for use in future projects.
    16. A Google-docs-based “best practices” system is developed to acquire, store and disseminate information necessary for sustainability-related decisions.
    17. A time-series-based model is developed to measure organizational flexibility that allows construction companies to expand and retract in reaction to periodic peaks and slumps in construction activity.
    18. The level of learning on the part of construction workers is investigated in safety training sessions.
    19. Worker safety issues are evaluated in nighttime highway construction and maintenance.
    20. The impact of organizational learning is investigated in the context of the design firm.
    21. The impact of the European Union enlargement on the European construction industry is investigated.
    22. The link between company culture relative to scheduling activities vs. construction delays is investigated in the US and India.
    23. The factors that motivate construction project managers are investigated; construction project managers’ human values are assessed; the motivation/values linkage is researched.
    24. The performance of minority based small and medium enterprises in construction is measured and compared with the performance of non-minority companies.
    25. The performance of international joint ventures is assessed by using structural equation modeling.
    26. The managerial competencies of female and male construction managers are measured and compared.
    27. The planning of the design, construction and brand-related activities is researched for hospitality projects.
    28. The risk of construction company default is researched and a model is developed to predict serious decline than can lead to failure.
    29. The critical issues in subcontracting are identified and feasible solutions are recommended.
    30. The marketing practices of construction companies are critically reviewed.
    31. The rate of innovation in construction management is analyzed and its effects on the construction activity are assessed.
    32. A model is developed that allows construction executives to make informed decisions concerning the short-term and long-term expansion policy of a company to international markets.
    33. The effectiveness of an internal pre-tender project peer review is investigated by means of an industry wide study.
    34. A comparative study is conducted that investigates the expectations of the different parties relative to construction management services, and that tries to reconcile the differences.
    35. A simulation model is used to prove that total project cost is minimized if supply chain buffers are not minimized under special circumstances such as fluctuating cash flows, inflation, and various discount schemes.
    36. Different prediction tools such as artificial neural networks, case based reasoning, and boosted decision trees are used to predict the cost estimate of low-rise reinforced concrete structures at the early stages of design.
    37. Different innovation diffusion models are used to explain the speed with which CAD technologies spread in the construction industry.
    38. The impact of BIM on construction management practices is researched and an up-to-date syllabus is developed to teach how to manage projects designed by BIM.

    Current Projects

    Awards/Honors

    Patents

    Books

    Selected Publications

    • Chotibhongs, R. and Arditi, D., Detection of Collusive Behavior, Journal of Construction Engineering and Management, ASCE, Vol. 138, No. 11, November 2012, pp: 866-876.
    • Yasamis-Speroni, F., Lee, D.-E. and Arditi, D.  Evaluating the Quality Performance of Pavement Contractors, Journal of Construction Engineering and Management, ASCE, Vol. 138, No. 10, October 2012, pp: 1114-1124.
    • Lee, D.-E., Lim, T.-K. and Arditi, D., Stochastic Project Financing Analysis System for Construction, Journal of Construction Engineering and Management, ASCE, Vol. 138, No. 2, March 2012, pp: 376.389.
    • Chotibhongs, R and Arditi, D., Analysis of Collusive Bidding Behavior, Construction Management and Economics, Vol. 30, No. 3, March 2012, pp: 221-231.
    • Lee, D.-E., Bae, T.-H. and Arditi, D., Advanced Stochastic Schedule Simulation System, Civil Engineering and Environmental Systems, Vol. 29, No. 1, March 2012, pp: 23-40.
    • Li, H., Arditi, D. and Wang, Z., Transaction-Related Issues and Construction Project Performance, Construction Management and Economics, Vol. 30, No. 2, February 2012, pp: 151-164.
    • Lee, D.-E., Lim, T.-K. and Arditi, D., An Expert System for Auditing Quality Management Systems in Construction, Computer-Aided Civil and Infrastructure Engineering, Vol. 26, No. 8, November 2011, pp: 612-631.
    • Ozorhon, B., Arditi, D., Dikmen, I. and Birgonul, M.T., Towards a Multidimensional Performance Measure for International Joint Ventures in Construction, Journal of Construction Engineering and Management, ASCE, Vol. 137, No. 6, June 2011, pp: 403-411.
    • Demirkesen, S. and Arditi, D., Safety Training in Construction, Turkish Engineering News, Special Issue: Worker Safety and Health, Vol. 56, No. 469, May 2011, pp: 49-55 (in Turkish).
    • Lee, D.-E., Yi, C.-Y., Lim, T.-K. and Arditi, D., Integrated Simulation System for Construction Operation and Project Scheduling, Journal of Computing in Civil  Engineering, ASCE, Vol. 24, No. 6, November/December 2010, pp: 557-569.
    • Ozorhon, B., Arditi, D., Dikmen, I. and Birgonul, M.T., The Performance of International Joint Ventures in Construction, Journal of Management in Engineering, ASCE, Vol. 26, No. 4, October 2010, pp: 209-222.
    • Kim, A. and Arditi, D., Performance of Minority Firms Providing Construction Management Services in the U.S. Transportation Sector, Construction Management and Economics, Vol. 28, No. 8, August 2010, pp: 839-851.
    • Arditi, D. and Polat, G., Graduate Education in Construction Management, Journal of Professional Issues in Engineering Education and Practice, ASCE, Vol. 136, No. 7, July 2010, pp: 175-179.
    • Kim, A. and Arditi, D., Performance of MBE/DBE/WBE Construction Firms in Transportation Projects, Journal of Construction Engineering and Management, ASCE, Vol. 136, No. 7, July 2010, pp: 768-777.
    • Isik, Z., Arditi, D., Dikmen, I. and Birgonul M. T., The Role of Exogenous Factors in the Strategic Performance of Construction Companies, Engineering, Construction and Architectural Management, Vol. 17, No. 2, 2010, pp: 119-134.
    • Kale, S. and Arditi, D., Innovation Diffusion Modeling in the Construction Industry, Journal of Construction Engineering and Management, ASCE, Vol. 136, No. 3, March 2010, pp: 329-340.
    • Kaplanoglu, B. and Arditi, D., Guidelines for Pre-Project Peer Reviews in Construction Contracting, International Journal of Project Organization and Management, Vol. 2, No. 2, 2010, pp: 154-173.
    • Arditi, D., Polat, G. and Akin, S., Lessons Learned System in Construction Management, International Journal of Project Organization and Management, Vol. 2, No. 1, 2010, pp: 61-83.
    • Arditi, D. and Pulket, T., Predicting the Outcome of Construction Litigation Using an Integrated Artificial Intelligence Model, Journal of Computing in Civil  Engineering, ASCE, Vol. 24, No. 1, January/February 2010, pp: 73-80.
    • Isik, Z., Arditi, D., Dikmen, I. and Birgonul, T.M., Impact of Resources and Strategies on Construction Company Performance, Journal of Management in Engineering, ASCE, Vol. 26, No. 1, January 2010, pp: 9-18.

    Professional Society Memberships

    Civil and Architectural Engineering
    3201 South Dearborn Street
    Room 228 Alumni Memorial Hall
    Chicago, IL 60616-3793
    Phone: 312.567.3540
    312.567.3519