Bootstrap Aggregated Case-Based Reasoning Method for Conceptual Cost Estimation
Abstract
:1. Introduction
2. CBR Method for Conceptual Cost Estimation
3. CBR Method with Bagging
3.1. Bootstrap Method
3.2. Data Normalization and Resampling
3.3. Case Retrieval and Case Reuse
3.4. GA Attribute Weight Optimization Framework
3.5. Aggregation
4. Model Comparison
4.1. Data Sets
4.2. Test Results
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Nomenclature
N | Training Case Sample Size |
n | Training Case Sample Index Starting From 1 to N |
T | Test Case Sample Size |
t | Test Case Sample Index Starting From 1 to T |
I | Independent Variable Size |
i | Independent Variable Starting From 1 to I |
R | Bootstrapped Training Sample |
r | Bootstrapped Training Sample Index Starting From 1 to R |
V | Predicted Sample Size |
v | Predicted Values Starting From 1 to V |
m | Random Number Between 1 to I |
References
- Oberlender, G.D.; Trost, S.M. Predicting accuracy of early cost estimates based on estimate quality. J. Constr. Eng. Manag. 2021, 127, 173–182. [Google Scholar] [CrossRef]
- Hu, X.; Xia, B.; Skitmore, M.; Chen, Q. The application of case-based reasoning in construction management research: An overview. Autom. Constr. 2016, 72, 65–74. [Google Scholar] [CrossRef] [Green Version]
- Watson, I.; Marir, F. Case-based reasoning: A review. Knowl. Eng. Rev. 1994, 9, 327–354. [Google Scholar] [CrossRef]
- Kolodner, J.L. An introduction to case-based reasoning. Artif. Intell. Rev. 1992, 6, 3–34. [Google Scholar] [CrossRef]
- Jin, R.; Cho, K.; Hyun, C.; Son, M. MRA-based revised CBR model for cost prediction in the early stage of construction projects. Expert Syst. Appl. 2012, 39, 5214–5222. [Google Scholar] [CrossRef]
- Ji, S.H.; Park, M.; Lee, H.S. Data preprocessing–based parametric cost model for building projects: Case studies of Korean construction projects. J. Constr. Eng. Manag. 2010, 136, 844–853. [Google Scholar] [CrossRef]
- Jin, R.; Han, S.; Hyun, C.; Kim, J. Improving accuracy of early stage cost estimation by revising categorical variables in a case-based reasoning model. J. Constr. Eng. Manag. 2014, 140, 04014025. [Google Scholar] [CrossRef]
- Ji, S.H.; Ahn, J.; Lee, E.B.; Kim, Y. Learning method for knowledge retention in CBR cost models. Autom. Constr. 2018, 96, 65–74. [Google Scholar] [CrossRef]
- Polikar, R. Ensemble learning. In Ensemble Machine Learning; Springer: Boston, MA, USA, 2012; pp. 1–34. [Google Scholar]
- Sagi, O.; Rokach, L. Ensemble learning: A survey. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 2018, 8, e1249. [Google Scholar] [CrossRef]
- Campagner, A.; Ciucci, D.; Cabitza, F. Aggregation models in ensemble learning: A large-scale comparison. Inf. Fusion 2023, 90, 241–252. [Google Scholar] [CrossRef]
- Ferreira, A.J.; Figueiredo, M.A. Boosting algorithms: A review of methods, theory, and applications. Ensemble Mach. Learn. Methods Appl. 2012, 35–85. [Google Scholar] [CrossRef]
- Breiman, L. Bagging predictors. Mach. Learn. 1996, 24, 123–140. [Google Scholar] [CrossRef] [Green Version]
- Yau, N.J.; Yang, J.B. Case-based reasoning in construction management. Comput. Aided Civ. Infrastruct. Eng. 1998, 13, 143–150. [Google Scholar] [CrossRef]
- Kim, S.Y.; Choi, J.W.; Kim, G.H.; Kang, K.I. Comparing cost prediction methods for apartment housing projects: CBR versus ANN. J. Asian Archit. Build. Eng. 2005, 4, 113–120. [Google Scholar] [CrossRef]
- Ji, S.H.; Park, M.; Lee, H.S.; Ahn, J.; Kim, N.; Son, B. Military facility cost estimation system using case-based reasoning in Korea. J. Comput. Civ. Eng. 2011, 25, 218–231. [Google Scholar] [CrossRef] [Green Version]
- Choi, S.; Kim, D.Y.; Han, S.H.; Kwak, Y.H. Conceptual cost-prediction model for public road planning via rough set theory and case-based reasoning. J. Constr. Eng. Manag. 2014, 140, 04013026. [Google Scholar] [CrossRef] [Green Version]
- Ahn, J.; Ji, S.-H.; Ahn, S.-J.; Park, M.; Lee, H.-S.; Kwon, N.; Lee, E.B.; Kim, Y. Performance evaluation of normalization-based CBR models for improving construction cost estimation. Autom. Constr. 2020, 119, 103329. [Google Scholar] [CrossRef]
- Croux, C.; Joossens, K.; Lemmens, A. Trimmed bagging. Comput. Stat. Data Anal. 2007, 52, 362–368. [Google Scholar] [CrossRef]
- Efron, B.; Tibshirani, R.J. An Introduction to the Bootstrap; Chapman and Hall: New York, NY, USA, 1993. [Google Scholar]
- Sonmez, R. Parametric range estimating of building costs using regression models and bootstrap. J. Constr. Eng. Manag. 2008, 134, 1011–1016. [Google Scholar] [CrossRef]
- Sonmez, R. Range estimation of construction costs using neural networks with bootstrap prediction intervals. Expert Syst. Appl. 2011, 38, 9913–9917. [Google Scholar] [CrossRef]
- Gardner, B.J.; Gransberg, D.D.; Rueda, J.A. Stochastic conceptual cost estimating of highway projects to communicate uncertainty using bootstrap sampling. ASCE-ASME J. Risk Uncertain. Eng. Syst. Part A Civ. Eng. 2017, 3, 05016002. [Google Scholar] [CrossRef]
- Idowu, O.S.; Lam, K.C. Conceptual Quantities Estimation Using Bootstrapped Support Vector Regression Models. J. Constr. Eng. Manag. 2020, 146, 04020018. [Google Scholar] [CrossRef]
- Tsai, T.I.; Li, D.C. Utilize bootstrap in small data set learning for pilot run modeling of manufacturing systems. Expert Syst. Appl. 2008, 35, 1293–1300. [Google Scholar] [CrossRef]
- Ji, C.; Hong, T.; Hyun, C. CBR revision model for improving cost prediction accuracy in multifamily housing projects. J. Manag. Eng. 2010, 26, 229–236. [Google Scholar] [CrossRef]
- Hyung, W.-G.; Kim, S.; Jo, J.-K. Improved similarity measure in case-based reasoning: A case study of construction cost estimation. Eng. Constr. Archit. Manag. 2020, 27, 561–578. [Google Scholar] [CrossRef]
- Altman, N.S. An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression. Am. Stat. 1992, 46, 3. [Google Scholar]
- Holland, J.H. Adaptation in Natural and Artificial Systems; University of Michigan Press: Ann Arbor, MI, USA, 1975. [Google Scholar]
- DeJong, K. Analysis of the Behavior of a Class of Genetic Adaptive. Ph.D. Thesis, University of Michigan, Ann Arbor, MI, USA, 1975. [Google Scholar]
- Bramlette, M.F. Initialization, mutation and selection methods in genetic algorithms for function optimization. In Proceedings of the Fourth International Conference on Genetic Algorithms, San Diego, CA, USA, 13–16 July 1991; pp. 100–107. [Google Scholar]
- Fitzgerald, J.; Azad, R.M.A.; Ryan., C. A bootstrapping approach to reduce over-fitting in genetic programming. In Proceedings of the 15th Annual Conference Companion on Genetic and Evolutionary Computation, Amsterdam, The Netherlands, 6–10 July 2013; pp. 1113–1120. [Google Scholar]
- Karshenas, S. Predesign cost estimating method for multistory buildings. J. Constr. Eng. Manag. 1984, 110, 79–86. [Google Scholar] [CrossRef]
- ElMousalami, H.H.; Elyamany, A.H.; Ibrahim, A.H. Predicting conceptual cost for field canal improvement projects. J. Constr. Eng. Manag. 2018, 144, 04018102. [Google Scholar] [CrossRef]
- Elmousalami, H.H.; Elyamany, A.H.; Ibrahim, A.H. Evaluation of cost drivers for field canals improvement projects. Water Resour. Manag. 2018, 32, 53–65. [Google Scholar] [CrossRef]
- Sonmez, R. Conceptual cost estimation of building projects with regression analysis and neural networks. Can. J. Civ. Eng. 2004, 31, 677–683. [Google Scholar] [CrossRef]
- Elmousalami, H.H. Artificial intelligence and parametric construction cost estimate modeling: State-of-the-art review. J. Constr. Eng. Manag. 2020, 146, 03119008. [Google Scholar] [CrossRef]
Data Set No | Reference | Case Instances |
---|---|---|
Data set 1 | [33] | 24 Office Buildings |
Data set 2 | [37] | 144 Field Canal Improvement Projects |
Data set 3 | [36] | 30 Retirement Community Projects |
CBR-GA | CBR-BSR (This Study) | |
---|---|---|
Fold 1 | 37.19 | 19.71 |
Fold 2 | 9.45 | 9.22 |
Fold 3 | 42.17 | 14.79 |
Fold 4 | 32.19 | 26.74 |
Fold 5 | 45.20 | 67.25 |
Overall MAPE % | 32.75 | 25.88 |
CBR-GA | CBR-BSR (This Study) | |
---|---|---|
Fold 1 | 21.26 | 12.84 |
Fold 2 | 14.46 | 12.35 |
Fold 3 | 14.71 | 11.89 |
Fold 4 | 9.85 | 7.05 |
Fold 5 | 16.21 | 12.46 |
Overall MAPE % | 15.29 | 11.32 |
CBR-GA | CBR-BSR (This Study) | |
---|---|---|
Fold 1 | 13.52 | 12.63 |
Fold 2 | 15.66 | 13.32 |
Fold 3 | 14.78 | 12.67 |
Fold 4 | 17.93 | 19.63 |
Fold 5 | 19.93 | 19.69 |
Overall MAPE % | 16.38 | 15.59 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Uysal, F.; Sonmez, R. Bootstrap Aggregated Case-Based Reasoning Method for Conceptual Cost Estimation. Buildings 2023, 13, 651. https://doi.org/10.3390/buildings13030651
Uysal F, Sonmez R. Bootstrap Aggregated Case-Based Reasoning Method for Conceptual Cost Estimation. Buildings. 2023; 13(3):651. https://doi.org/10.3390/buildings13030651
Chicago/Turabian StyleUysal, Furkan, and Rifat Sonmez. 2023. "Bootstrap Aggregated Case-Based Reasoning Method for Conceptual Cost Estimation" Buildings 13, no. 3: 651. https://doi.org/10.3390/buildings13030651
APA StyleUysal, F., & Sonmez, R. (2023). Bootstrap Aggregated Case-Based Reasoning Method for Conceptual Cost Estimation. Buildings, 13(3), 651. https://doi.org/10.3390/buildings13030651