Combining case-based reasoning with genetic algorithm optimization for preliminary cost estimation in construction industry

2014 ◽  
Vol 41 (1) ◽  
pp. 65-73 ◽  
Author(s):  
Sangyong Kim ◽  
Jae Heon Shim
Keyword(s):  
Genetic Algorithms ◽  
Cost Estimation ◽  
Hybrid Approach ◽  
Design Stage ◽  
Case Based Reasoning ◽  
Planning Stage ◽  
High Rise ◽  
The Cost ◽  
Cost Factors ◽  
Case Based

In this paper, we propose a hybrid case-based reasoning (CBR) system for predicting the construction cost of high-rise buildings at the preliminary design stage. First, the extracted cost factors (CFs) of a high-rise building were shown to significantly improve the cost estimation system’s performance. For developing a CBR system, a hybrid approach that combines CBR with genetic algorithms (GAs) for cost estimation was adopted. Genetic algorithms were used for optimized weight generation and applied to real project cases. Additionally, this paper proposes the identification of an alternative similarity score measurement formula. The proposed formula evaluates the contrast between the alternative case matching approach and the classical formula in a scenario involving the use of cost factors describing a case. The results indicate that the proposed GA-based CBR system can consistently reduce errors and potentially be useful to owners and contractors in the early financial planning stage. Accordingly, it is expected that the developed CBR system would provide decision-makers with accurate cost information to assess and compare multiple alternatives for obtaining the optimal solution and controlling the cost.

Improved similarity measure in case-based reasoning: a case study of construction cost estimation

2019 ◽  
Vol 27 (2) ◽  
pp. 561-578 ◽  
Author(s):  
Won-Gil Hyung ◽  
Sangyong Kim ◽  
Jung-Kyu Jo
Keyword(s):  
Cost Estimation ◽  
Hybrid Approach ◽  
Optimal Solution ◽  
Case Based Reasoning ◽  
Optimization Approach ◽  
Cost Information ◽  
Planning Stage ◽  
Content Type ◽  
Case Based

Purpose Applied a hybrid approach using genetic algorithms (GAs) for a case-based retrieval process in order to increase the overall improved cost accuracy for a case-based library. The paper aims to discuss this issue. Design/methodology/approach A weight optimization approach using case-based reasoning (CBR) with proposed GAs for developing the CBR model. GAs are used to investigate optimized weight generation with an application to real project cases. Findings The proposed CBR model can reduce errors consistently, and be potentially useful in the early financial planning stage. The authors suggest the developed CBR model can provide decision-makers with accurate cost information for assessing and comparing multiple alternatives in order to obtain the optimal solution while controlling cost. Originality/value The system can operate with more accuracy or less cost, and CBR can be used to better understand the effects of factor interaction and variation during the developed system’s process.

scholarly journals Index Cost Estimation Using Case Based Reasoning Model Based on Macro BIM

2018 ◽  
Author(s):  
Krzysztof Zima ◽  
Agnieszka Leśniak
Keyword(s):  
Correlation Coefficient ◽  
Cost Estimation ◽  
Pearson Correlation ◽  
Construction Project ◽  
Information Modeling ◽  
Unit Price ◽  
Case Based Reasoning ◽  
Estimation Process ◽  
The Cost ◽  
Case Based

Information regarding the cost of a construction project is available to the investor and project participants in order to determine the subsequent success of a project, given that the information they collect has an impact on the decisions they make. Cost calculations, especially in the initial phase of a project, often generate large errors. This paper presents the new approach based on a combination of the Case Based Reasoning method (CBR) with the originally selected criteria for the description of a construction project (as a result of Pearson correlation coefficient and Spearman's rank correlation coefficient) and Building Information Modeling (BIM) technology. The CBR method fulfils expectations for a simple and fast system supporting the cost estimation process. It does not require any specialist knowledge, so it will be comprehensible to cost estimation practitioners. The BIM-based model gives the opportunity for the calculation of quantity take-offs and enables the use of the information contained in the BIM model in the cost estimation process. In order to prepare the model an appropriate relational database had to be developed. With extensive research, a database of 173 construction projects, including the construction of a sports field, was obtained. There were 14 variables defined originally by authors; however, only 10 (as a result of the correlation analysis) were used for the calculation. Data related to the project were collected in the BIM model. Results estimating the project’s unit price, using the CBR method, were presented and discussed. The Mean Absolute Estimate Error was used to evaluate the model.

A STUDY ON THE DEVELOPMENT OF A COST MODEL BASED ON THE OWNER'S DECISION MAKING AT THE EARLY STAGES OF A CONSTRUCTION PROJECT

2010 ◽  
Vol 14 (2) ◽  
pp. 121-137 ◽  
Author(s):  
Choong-Wan Koo ◽  
TaeHoon Hong ◽  
Chang-Taek Hyun ◽  
Sang H. Park ◽  
Joon-oh Seo
Keyword(s):  
Decision Making ◽  
Cost Estimation ◽  
Cost Model ◽  
Construction Project ◽  
Case Based Reasoning ◽  
Planning Stage ◽  
Attribute Weights ◽  
Early Stages ◽  
Minimum Criteria ◽  
The Cost

Decision making at the early stages of a construction project has a significant impact on the project, and various scenarios created based on the owner's requirements should be considered for the decision making. At the early stages of a construction project, the information regarding the project is usually limited and uncertain. As such, it is difficult to plan and manage the project (especially cost planning). Thus, a cost model that could be varied according to the owner's requirements was developed. The cost model that was developed in this study is based on the case‐based reasoning (CBR) methodology. The model suggests cost estimation with the most similar historical case as a basis for the estimation. In this study, the optimization process was also conducted, using genetic algorithms that reflect the changes in the number of project characteristics and in the database of the model according to the owner's decision making. Two optimization parameters were established: (1) the minimum criteria for scoring attribute similarity (MCAS); and (2) the range of attribute weights (RAW). The cost model proposed in this study can help building owners and managers estimate the project budget at the business planning stage. Santruka Sprendimu priemimas ankstyvuoju statybos projekto etapu turi didele itaka projektui ir ivairiems scenarijams, remiantis savininko reikalavimais, kuriu turi būti laikomasi priimant sprendimus. Ankstyvaisiais statybos projekto etapais informacijos apie projekta paprastai yra nedaug ir ji nera patikima. Del to sudetinga planuoti ir taisyti projekta (ypač išlaidu planavima). Todel šio tyrimo metu buvo sukurtas kainos modelis, kuris galetu būti keičiamas atsižvelgiant i savininko poreikius. Kainos modelis, kuris buvo sukurtas šio tyrimo metu, remiasi atveju analize, pagrista argumentu metodika (angl. CBR). Modelis siūlo samatinius skaičiavimus su panašiausiais ankstesniais atvejais, kurie yra skaičiavimo pagrindas. Šio tyrimo metu procesas buvo optimizuotas naudojant genetinius algoritmus, rodančius projektu skaičiaus kitima tam tikro modelio duomenu bazeje pagal savininko priimamus sprendimus. Buvo nustatyti du optimizavimo parametrai: 1) minimalūs kriterijai veiksniu panašumui ivertinti (angl. MCAS); 2) veiksniu svoriu vertinimo intervalas (angl. RAW). Kainos modelis, pasiūlytas šiame tyrime, gali padeti pastatu savininkams ir valdytojams ivertinti projekto biudžeta verslo planavimo etape.

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

2020 ◽  
Vol 12 (19) ◽  
pp. 7920
Author(s):  
Sangsun Jung ◽  
Jae-Ho Pyeon ◽  
Hyun-Soo Lee ◽  
Moonseo Park ◽  
Inseok Yoon ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Local Search ◽  
Cost Estimation ◽  
Hybrid Genetic Algorithm ◽  
Construction Cost ◽  
Case Based Reasoning ◽  
Construction Costs ◽  
Initial Stage ◽  
Case Based

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.

The attribute impact concept: Applications in case-based reasoning and parametric cost estimation

2014 ◽  
Vol 43 ◽  
pp. 195-203 ◽  
Author(s):  
Joseph Ahn ◽  
Sae-Hyun Ji ◽  
Moonseo Park ◽  
Hyun-Soo Lee ◽  
Sooyoung Kim ◽  
...  
Keyword(s):  
Cost Estimation ◽  
Case Based Reasoning ◽  
Parametric Cost Estimation ◽  
Case Based

Retrieving Assembly Part Design Using Case-Based Reasoning and Genetic Algorithms

2005 ◽  
Author(s):  
Guanghsu A. Chang ◽  
Cheng-Chung Su ◽  
John W. Priest
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithms ◽  
Nearest Neighbor ◽  
Similarity Measures ◽  
Experimental Results ◽  
Case Based Reasoning ◽  
Feature Weight ◽  
Similar Part ◽  
Case Base ◽  
Case Based

Artificial intelligence (AI) approaches have been successfully applied to many fields. Among the numerous AI approaches, Case-Based Reasoning (CBR) is an approach that mainly focuses on the reuse of knowledge and experience. However, little work is done on applications of CBR to improve assembly part design. Similarity measures and the weight of different features are crucial in determining the accuracy of retrieving cases from the case base. To develop the weight of part features and retrieve a similar part design, the research proposes using Genetic Algorithms (GAs) to learn the optimum feature weight and employing nearest-neighbor technique to measure the similarity of assembly part design. Early experimental results indicate that the similar part design is effectively retrieved by these similarity measures.

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