CBR-based cost prediction model-II of the design phase for multi-family housing projects

Hong, Taehoon; Hyun, Chang-Taek; Moon, Hee-Sung

doi:10.1016/j.eswa.2010.08.071

Cited by 27 publications

(25 citation statements)

References 15 publications

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“…A few CFs or case descriptors are of greater importance than others in judging the usefulness of matching (Ji et al 2010;Koo et al 2010;Hong et al 2011). The development process involves defining the process for comparing the extent of similarity between a new problem description and existing cases.…”

Section: Similarity Measurementmentioning

confidence: 99%

“…This includes the development step's formalization of SS by nearest-neighbour retrieval (NNR) because the degree of similarity is calculated by using the distance between two cases. However, previous studies show that little attention has been given to current SS formulas (Koo et al 2010;Hong et al 2011;Kim 2013). Consequently, previous efforts have rarely studied SS in depth.…”

Section: Introductionmentioning

confidence: 95%

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

KimSangyong

Heon

2014

Can. J. Civ. Eng.

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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.Résumé : Nous proposons dans cet article un système hybride de raisonnement par cas pour prédire le coût de construction des immeubles de grande hauteur, à l'étape de la conception préliminaire. En premier lieu, les facteurs de coût extraits d'un immeuble de grande hauteur ont amélioré de manière significative le rendement du système d'estimation des coûts. Pour le développement d'un système de raisonnement par cas, nous avons adopté une approche hybride qui combine le raisonnement par cas à des algorithmes génétiques d'estimation des coûts. Les algorithmes génétiques ont généré une optimisation pondérale et ils ont été appliqués à des projets réels. De plus, cet article propose une autre formule similaire de mesure du pointage. La formule proposée évalue le contraste entre l'approche alternative de concordance des cas et la formule classique d'un scénario qui utilise des facteurs de coût pour décrire un cas. Les résultats indiquent que le système de raisonnement par cas basé sur les algorithmes génétiques peut (sujet système) réduire uniformément les erreurs et être potentiellement utile aux propriétaires et aux entrepreneurs aux premières étapes de la planification financière. De même, on s'attend à ce que ce système de raisonnement par cas fournisse aux décideurs une information précise sur les coûts afin d'évaluer et de comparer plusieurs options dans le but de trouver la solution optimale et de contrôler les coûts. [Traduit par la Rédaction]

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Section: Similarity Measurementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 95%

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

KimSangyong

Heon

2014

Can. J. Civ. Eng.

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“…Numerous conceptual cost estimation methods, such as unit cost, cost indices, cost‐capacity factors, component ratios, and parametric estimation, have been designed to quickly compute a total project cost in the construction industry (Sarma and Adeli, 1998; Barrie and Paulson, 1992; Hendrickson and Au, 2003; Hong et al, 2011). Cost indices focus on cost changes over time, whereas cost‐capacity factors apply to changes in size, scope, or the capacity of similar projects (Barrie and Paulson, 1992).…”

Section: Review Of Cost Estimation Modelsmentioning

confidence: 99%

Neuro‐Fuzzy Cost Estimation Model Enhanced by Fast Messy Genetic Algorithms for Semiconductor Hookup Construction

Hsiao

Wang

et al. 2012

Computer aided Civil Eng

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Semiconductor hookup construction (i.e., constructing process tool piping systems) is critical to semiconductor fabrication plant completion. During the conceptual project phase, it is difficult to conduct an accurate cost estimate due to the great amount of uncertain cost items. This study proposes a new model for estimating semiconductor hookup construction project costs. The developed model, called FALCON‐COST, integrates the component ratios method, fuzzy adaptive learning control network (FALCON), fast messy genetic algorithm (fmGA), and three‐point cost estimation method to systematically deal with a cost‐estimating environment involving limited and uncertain data. In addition, the proposed model improves the current FALCON by devising a new algorithm to conduct building block selection and random gene deletion so that fmGA operations can be implemented in FALCON. The results of 54 case studies demonstrate that the proposed model has estimation accuracy of 83.82%, meaning it is approximately 22.74%, 23.08%, and 21.95% more accurate than the conventional average cost method, component ratios method, and modified FALCON‐COST method, respectively. Providing project managers with reliable cost estimates is essential for effectively controlling project costs.

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“…Many works were reported on developing models based on CBR. For instance, An et al [15] proposed a CBR model based on analytic hierarchy process (AHP), which included all the processes of cost estimation; few similar models were developed by Koo et al [16], Hong et al [17] and Ji et al [18]. An advanced CBR model was presented by Koo et al [19], containing multi-family housing projects with 101 cases; the model incorporated optimization process, ANN and multiple regression analysis (MRA), using GA.…”

Section: Introductionmentioning

confidence: 99%

Model for Estimating Construction Costs for Low-Rise Residential Buildings

Alshibani

Ashamrani

Shaawat

2018

WIT Transactions on the Built Environment

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This paper introduces a multi-regression model for estimating construction cost of various structure and envelope types of low-rise residential buildings in Canada. The model is capable of predicting construction cost per square feet for six envelope and structure alternatives in various combinations. The structure types were of wood and steel, and the envelope systems included wood, veneer brick and concrete cavity wall. The predictor variables were building envelope type, area, number of story and story height. The model was developed in four main steps: literature review of the existing methods, real construction cost data collection of completed projects, preliminary diagnostics over data quality, generation and verification of the model. The developed model was successfully tested and validated with real-time data. This model can provide reliable conceptual cost estimate of low-rise residential buildings at the early design stage with reasonable accuracy. It helps owners for budget allocation and/or conducting economic analysis with less effort and complexity.

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CBR-based cost prediction model-II of the design phase for multi-family housing projects

Cited by 27 publications

References 15 publications

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

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

Neuro‐Fuzzy Cost Estimation Model Enhanced by Fast Messy Genetic Algorithms for Semiconductor Hookup Construction

Model for Estimating Construction Costs for Low-Rise Residential Buildings

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