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

Sangsun Jung,Moonseo Park,Juhee Rho,Jae-Ho Pyeon,Inseok Yoon,Hyun-Soo Lee

doi:10.3390/su12197920

Sangsun Jung, Moonseo Park + Show 4 more

Open Access

https://doi.org/10.3390/su12197920

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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.

Highlights

Cost estimation at the early project stage plays an important role in a contractor’s decision-making, especially in budgeting for the project and construction period calculation [1]
The rest of this research follows the procedure below: (a) The implications are derived through the analysis of the preceding study. (b) The theoretical backgrounds and practical applications of case-based reasoning, genetic algorithms, and local search are studied considered for developing a hybrid genetic algorithm. (c) Correlative analysis with the data from three cases of apartment housing, military barracks, and office buildings are conducted, and the corresponding correlation coefficient is calculated for each property. (d) A model for estimating case-based reasoning construction costs is developed using a hybrid genetic algorithm with local search application. (e) The validity of this study is verified by comparing the estimated accuracy of the hybrid GA–CBR model and the model with different weighting methods
This research used correlations of each attribute in the already mentioned concept of local search to determine the weight of GA–CBR

Summary

Introduction

Cost estimation at the early project stage plays an important role in a contractor’s decision-making, especially in budgeting for the project and construction period calculation [1]. If the construction cost is accurately predicted, it is possible to save resources, because it does not waste unnecessary resources on the construction project. To address this current limitation, studies have made significant efforts to improve the accuracy of the initial construction cost by developing cost estimating models. It is used to estimate initial construction costs It consists of four steps: to retrieve, to reuse, to revise, and to retain. The first step to retrieve is a reasoning process to explore similar cases in the project case data [4]

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Results

Conclusion