Construction Cost Estimation Using a Case-Based Reasoning Hybrid Genetic Algorithm Based on Local Search Method

Sangsun Jung, Jae-Ho Pyeon, Hyun-Soo Lee, Moonseo Park, Inseok Yoon and Juhee Rho
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Sangsun Jung: Department of Architectural Engineering, Seoul National University, Seoul 08826, Korea
Jae-Ho Pyeon: Civil & Environmental Engineering, San Jose State University, Washington Sq, San Jose, CA 95192, USA
Hyun-Soo Lee: Department of Architectural Engineering, Seoul National University, Seoul 08826, Korea
Moonseo Park: Department of Architectural Engineering, Seoul National University, Seoul 08826, Korea
Inseok Yoon: Department of Architectural Engineering, Seoul National University, Seoul 08826, Korea
Juhee Rho: Department of Architectural Engineering, Seoul National University, Seoul 08826, Korea

Sustainability, 2020, vol. 12, issue 19, 1-17

Abstract: Estimates of project costs in the early stages of a construction project have a significant impact on the operator’s decision-making in essential matters, such as the site’s decision or the construction period. However, it is not easy to carry out the initial stage with confidence, because information such as design books and specifications is not available. In previous studies, case-based reasoning (CBR) is used to estimate initial construction costs, and genetic algorithms are used to calculate the weight of the retrieve phase in CBR’s process. However, it is difficult to draw a better solution than the current one, because existing genetic algorithms use random numbers. To overcome these limitations, we reflect correlation numbers in the genetic algorithms by using the method of local search. Then, we determine the weights using a hybrid genetic algorithm that combines local search and genetic algorithms. A case-based reasoning model was developed using a hybrid genetic algorithm. Then, the model was verified with construction cost data that were not used for the development of the model. As a result, it was found that the hybrid genetic algorithm and case-based reasoning applied with the local search performed better than the existing solution. The detail mean error value was found to be 3.52%, 6.15%, and 0.33% higher for each case than the previous one.

Keywords: cost estimation; case-based reasoning; hybrid genetic algorithm; local search; correlation analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
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