Construction litigation prediction system using ant colony optimization

Construction Management and Economics

Volumn 27, Issue 3, 2009, Pages 241-251

  Pulket, Thaveeporn ^a   Arditi, David ^a  

^a Illinois Institute of Technology   (United States)

Author keywords

Ant colony algorithm; Artificial intelligence; Case based reasoning; Decision trees; Litigation; Neural networks

Indexed keywords

ANT COLONY ALGORITHM; ANT-COLONY OPTIMIZATION; ATTRIBUTE SELECTION; CLASSIFICATION RULES; CONSTRUCTION CONTRACT; CONSTRUCTION LITIGATION; COURT CASE; ILLINOIS; LITIGATION; NOISY DATA; PREDICTION MODEL; PREDICTION SYSTEMS; RULE SET; RULE-BASED CLASSIFICATION SYSTEM;

ALGORITHMS; CONSTRUCTION INDUSTRY; DECISION TREES; MATHEMATICAL MODELS; MINERS; NEURAL NETWORKS; OPTIMIZATION;

CASE BASED REASONING;

ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; CONSTRUCTION INDUSTRY; LEGAL SYSTEM; OPTIMIZATION; PREDICTION;

EID: 67650341000     PISSN: 01446193     EISSN: 1466433X     Source Type: Journal    
DOI: 10.1080/01446190802714781     Document Type: Article

References (56)

1. AbouRizk, S. M. and Dozzi, S. P. (1993) Application of computer simulation in resolving construction disputes. ASCE Journal of Construction Engineering and Management, 119:2, pp. 355-373.
2. Adeli, H. and Wu, M. Y. (1998) Regularization neural network for construction cost estimation. ASCE Journal of Construction Engineering and Management, 124:1, pp. 18-24.
3. Amenu, G. G., Markus, M., Kumar, P. and Demissie, M. (2007) Hydrologic applications of MRAN algorithm. ASCE Journal of Hydrologic Engineering, 12:1, pp. 124-129.
4. An, S., Park, U., Kang, K., Cho, M. and Cho, H. (2007) Application of support vector machines in assessing conceptual cost estimates. ASCE Journal of Computing in Civil Engineering, 21:4, pp. 259-264.
5. Arditi, D. and Pulket, T. (2005) Predicting the outcome of construction litigation using boosted decision trees. ASCE Journal of Computing in Civil Engineering, 19:4, pp. 387-393.
6. Arditi, D. and Tokdemir, O. B. (1999) A comparison of case-based reasoning and artificial neural networks. ASCE Journal of Computing in Civil Engineering, 13:3, pp. 162-169.
7. Arditi, D., Tunca, A. A. and Yuyan, R. (1995) Analysis of construction claims that lead to litigation. Proceedings of the International Symposium on Modern Project Management, the International Project Management Association and Russian Project Management Association, St Petersburg, Russia
8. Arditi, D., Oksay, F. E. and Tokdemir, O. B. (1998) Predicting the outcome of construction litigation using neural networks. Computer-Aided Civil and Infrastructure Engineering, 13:2, pp. 75-81.
9. Bairaktaris, D., Delis, V., Emmanouilidis, C., Frondistou-Yannas, C., Gratsias, K., Kallidromitis, V. and Rerras, N. (2007) Decision-support system for the rehabilitation of deteriorating sewers. Journal of Performance of Constructed Facilities, 21:3, pp. 240-248.
10. Bonabeau, E., Dorigo, M. and Theraulaz, G. (1999) Swarm Intelligence: From Natural to Artificial System, Oxford University Press, New York
11. Cheung, S. O., Tam, C. M. and Harris, F. C. (2000) Project dispute resolution satisfaction classification through neural network. ASCE Journal of Management in Engineering, 16:1, pp. 70-79.
12. Deka, P. and Chandramouli, V. (2005) Fuzzy neural network model for hydrologic flow routing. ASCE Journal of Hydrologic Engineering, 10:4, pp. 302-314.
13. Diaz, F., Fdez-Riverola, F. and Corchado, J. M. (2006) Gene-CBR: A case-based reasoning tool for cancer diagnosis using microarray datasets. Computational Intelligence, 22:3, pp. 254-268.
14. Diekmann, J. E. and Girard, M. J. (1995) Are contract disputes predictable?. ASCE Journal of Construction Engineering and Management, 121:4, pp. 355-363.
15. Dogan, S. Z., Arditi, D. and Gunaydin, H. M. (2006) Determining attribute weights in a CBR model for early cost prediction of structural systems. ASCE Journal of Construction Engineering and Management, 132:10, pp. 1092-1098.
16. Dorigo, M., Colorni, A. and Maniezzo, V. (1996) The ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics: Part B, 26:1, pp. 1-13.
17. Georgy, M. E., Chang, L. and Zhang, L. (2005) Prediction of engineering performance: A neurofuzzy approach. ASCE Journal of Construction Engineering and Management, 131:5, pp. 548-557.
18. Goh, A. T. C., Kulhawy, F. H. and Chua, C. G. (2005) Bayesian neural network analysis of undrained side resistance of drilled shafts. ASCE Journal of Geotechnical and Geoenvironmental Engineering, 131:1, pp. 84-93.
19. Goh, B. H. (2000) Evaluating the performance of combining neural networks and genetic algorithms to forecast construction demand: The case of the Singapore residential sector. Construction Management and Economics, 18:1, pp. 209-217.
20. Gupta, R., Kewalramani, M. A. and Goel, A. (2006) Prediction of concrete strength using neural-expert system. ASCE Journal of Materials in Civil Engineering, 18:3, pp. 462-466.
21. Han, J. and Kamber, M. (2001) Data Mining Concepts and Techniques, Morgan Kaufmann, San Francisco, CA
22. Horng, S. and Lin, S. (2005) A hybrid classification tree for products of complicated machines in flexible manufacturing systems. IEEE International Conference on Systems, Man and Cybernetics, 4, pp. 3775-3780.
23. Kassab, M., Hipel, K. and Hegazy, T. (2006) Conflict resolution in construction disputes using the graph model. ASCE Journal of Construction Engineering and Management, 132:10, pp. 1043-1052.
24. Khan, M. S. and Coulibaly, P. (2006) Application of support vector machine in lake water level prediction. ASCE Journal of Hydrologic Engineering, 11:3, pp. 199-205.
25. Kilian, J. J. and Gibson, G. E. (2005) Construction litigation for the U.S. naval facilities engineering command, 1982-2002. ASCE Journal of Construction Engineering and Management, 131:9, pp. 945-952.
26. Kim, G. H., Seo, D. S. and Kang, K. I. (2005) Hybrid models of neural networks and genetic algorithms for predicting preliminary cost estimates. ASCE Journal of Computing in Civil Engineering, 19:2, pp. 208-211.
27. Kisi, O. and Ozturk, O. (2007) Adaptive neurofuzzy computing technique for evapotranspiration estimation. ASCE Journal of Irrigation and Drainage Engineering, 133:4, pp. 368-379.
28. Kovahi, H. (1995) A study of cross validation and bootstrap for accuracy estimation and model selection.
29. Kurup, P. U. and Griffin, E. P. (2006) Prediction of soil composition from CPT data using general regression neural network. ASCE Journal of Computing in Civil Engineering, 20:4, pp. 281-289.
30. Lawrence, S., Giles, C. L., Tsoi, A. C. and Back, A. D. (1997) Face recognition: A convolutional neural network approach.
31. Li, H., Shen, L. Y. and Love, P. E. D. (1999) ANN-based mark-up estimation system with self-explanatory capacities. ASCE Journal of Construction Engineering and Management, 125:3, pp. 185-189.
32. Mello, K. L. and Brown, S. D. (1999) Novel hybrid classification method employing Bayesian networks. Journal of Chemometrics, 13:6, pp. 579-590.
33. Meyer, F. and Parpinelli, R. S. (2005) SourceForge.net: GUI Ant-Miner
34. Mingers, J. (1989) An empirical comparison of pruning methods for decision tree induction. Machine Learning, 4, pp. 227-243.
35. Morcous, G. and Lounis, Z. (2005) Prediction of onset of corrosion in concrete bridge decks using neural networks and case-based reasoning. Computer-Aided Civil and Infrastructure Engineering, 20:2, pp. 108-117.
36. Ozorhon, B., Dikmen, I. and Birgonul, M. T. (2006) Case-based reasoning model for international market selection. ASCE Journal of Construction Engineering and Management, 132:9, pp. 940-948.
37. Parasuraman, K. and Elshorbagy, A. (2007) Cluster-based hydrologic prediction using genetic algorithm-trained neural networks. ASCE Journal of Hydrologic Engineering, 12:1, pp. 52-62.
38. Parpinelli, R. S., Lopes, H. S. and Freitas, A. (2002) An ant colony algorithm for classification rule discovery. Data Mining: A Heuristic Approach, pp. 191-208. Idea Group Publishing, Hershey, PA
39. Pearl, J. (1984) Heuristics, Intelligent Search Strategies for Computer Problem Solving, Addison-Wesley, Boston, MA
40. Pena-Mora, F., Sosa, C. E. and McCone, D. S. (2003) Introduction to Construction Dispute Resolution, Prentice Hall, Boston, MA
41. Quinlan, J. R. (1986) Induction of decision trees. Machine Learning, 1:1, pp. 81-106.
42. Quinlan, J. R. (1989) Unknown attribute values in induction. Proceedings of the Sixth International Machine Learning Workshop, pp. 164-168. December, Morgan Kaufmann, San Mateo, CA
43. Quinlan, J. R. (1992) C4.5 Programs for Machine Learning, Morgan Kaufmann, San Mateo, CA
44. Ren, Z., Anumba, C. J. and Ugwu, O. O. (2003) Multiagent system for construction claims negotiation. ASCE Journal of Computing in Civil Engineering, 17:3, pp. 180-188.
45. Russell, S. J. and Norvig, P. (2003) Artificial Intelligence: A Modern Approach, Prentice Hall, Boston, MA
46. Sadek, A., Morse, S., Ivan, J. and El-Dessouki, W. (2003) Case-based reasoning for assessing intelligent transportation systems benefits. Computer-Aided Civil and Infrastructure Engineering, 18:3, pp. 173-183.
47. Saez, D., Cortes, C. E. and Nunez, A. (2007) Hybrid adaptive predictive control for the multivehicle dynamic pickup and delivery problem based on genetic algorithms and fuzzy clustering. Computers & Operations Research, Special Issue: Real-time Supply Chain Management
48. Tah, J. H. M., Carr, V. and Howes, R. (1998) An application of case-based reasoning to the planning of highway bridge construction. Engineering Construction and Architectural Management, 5:4, pp. 327-338.
49. Tam, C. M., Tong, T. K. L. and Tse, S. L. (2002) Artificial neural networks model for predicting excavator productivity. Engineering Construction and Architectural Management, 9:5, pp. 446-452.
50. Tayfur, G. and Singh, V. P. (2006) ANN and fuzzy logic models for simulating event-based rainfall-runoff. ASCE Journal of Hydraulic Engineering, 132:12, pp. 1321-1330.
51. Van Lint, J. W. C. (2006) Reliable real-time framework for short-term freeway travel time prediction. ASCE Journal of Transportation Engineering, 132:12, pp. 921-932.
52. Wang, J. and Ghosn, M. (2006) Hybrid data mining/genetic shredding algorithm for reliability assessment of structural systems. ASCE Journal of Structural Engineering, 132:9, pp. 1451-1460.
53. Wang, W. M., Cheung, C. F., Lee, W. B. and Kwok, S. K. (2007) Knowledge-based treatment planning for adolescent early intervention of mental healthcare: A hybrid case-based reasoning approach. Expert Systems, 24:4, pp. 232-251.
54. Witten, I. H. and Frank, E. (2005) Data Mining, Practical Machine Learning Tools and Techniques, Morgan Kaufmann, San Francisco, CA
55. Xu, L. D. (1994) Developing a case-based knowledge system for AIDS prevention. Expert Systems, 11:4, pp. 237-244.
56. Yiu, T. W., Cheung, S. O. and Mok, F. M. (2006) Logistic likelihood analysis of mediation outcomes. ASCE Journal of Construction Engineering and Management, 132:10, pp. 1026-1036.