Comparison of construction cost estimating models based on regression analysis, neural networks, and case-based reasoning

Kim, Gwang‐Hee; An, Sung Hoon; Kang, Kyung-In

doi:10.1016/j.buildenv.2004.02.013

Cited by 365 publications

(224 citation statements)

References 35 publications

Supporting

Mentioning

218

Contrasting

Unclassified

Order By: Relevance

“…The CBR models used for cost estimates may be based on both quantitative and qualitative data [29]. In [30,31], it was noticed that, although some information is not specified, CBR models for long periods of use maintain quality and ability to solve problems and work better than other models. The use of CBR to estimate construction costs is based on searching for similar investments already completed.…”

Section: Literature Reviewmentioning

confidence: 99%

Cost Calculation of Construction Projects Including Sustainability Factors Using the Case Based Reasoning (CBR) Method

Leśniak

Zima

2018

Sustainability

View full text Add to dashboard Cite

Abstract:The idea of sustainable development and the resulting environmentally friendly attitudes are increasingly used in construction projects. Designing in accordance with the principles of sustainable development has an impact on the costs of construction works. The authors of this paper proposed an approach to estimate the costs of sports field construction using the Case Based Reasoning method. In their analysis, they distinguished 16 factors that affect the cost of a construction project and are possible to already be described at an early stage of its preparation. The original elements of the work include: consideration of such environmental factors as the environmental impact of the building, materials used, the impact of the facility on the surroundings affecting the amount of implementation costs and development of own database containing 143 construction projects that are related to sports fields. In order to calculate the similarity of cases, different calculation formulas were applied depending on the type of data (quantitative, qualitative, uncertain, no data). The obtained results confirmed that the CBR method based on historical data and using criteria related to sustainable development may be useful in cost estimation in the initial phase of a construction project. Its application to the calculation of the costs that are related to the implementation of sports fields generates an error of 14%, which is a very good result for initial calculations. In the short run, such factors as the impact of the object and the type of materials that are used from the perspective of their influence on the environment may be decisive as far as the costs determined in the life cycle of the building are concerned, as well as the lowest costs of the building construction ensuring the appropriate quality and respect for the environment.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Cost Calculation of Construction Projects Including Sustainability Factors Using the Case Based Reasoning (CBR) Method

Leśniak

Zima

2018

Sustainability

View full text Add to dashboard Cite

show abstract

“…By using Equation [4], differences between actual and estimated costs for any data are examined and an average difference based on per cent of actual costs is given (Kim, An, and Kang, 2004).…”

Section: Cost Modelling For Flotation Machinesmentioning

confidence: 99%

Cost modelling for flotation machines

Arfania

Sayadi

Khalesi

2017

J. South. Afr. Inst. Min. Metall.

View full text Add to dashboard Cite

Mineral processing is a vital part of mining projects and mainly involves comminution, sizing, concentration, extractive metallurgical processes, and dewatering. Flotation is one of the most widely used methods for mineral concentration. Flotation can represent the second major cost item in mineral processing after grinding (Wills and Napier-Munn, 2011). Accordingly, it is a main concern of mining project managers to select and optimize flotation circuits in order to decrease costs and increase productivity. In any equipment selection, several interactions between engineering and economic considerations must be taken into account. Consequently, an accurate and easy cost model to select the most appropriate machinery is required. Moreover, cost models could be used in flow sheet simulations applied in design and optimization. Models of unit operations built into the simulators could be improved by linking the equipment cost models (Khalesi et al., 2015).A number of approaches can be employed with the aim of developing the cost models. A review of these methods can be found in recent papers by Niazi et al. (2006) and Huang, Newnes, and Parry (2012). Regression is one the most frequently applied techniques for cost modelling (Smith and Mason, 1997). Several cost models have been established related to mining and milling projects ( Table I). One of the preliminary works was undertaken by Prasad (1969) and has been carried on in the recent work of Sayadi, Khalesi, and Khosfarman (2014). Almost all of these models have been developed based on exponential single regression approaches; correlating only one independent variable to a cost value (Stebbins, 1987). Consequently, in spite of the usefulness of these models in preliminary cost estimation, the role of other effective parameters has simply been overlooked. Some of these models have become old and updating them also may cause significant errors. Furthermore, these models mainly estimate total operating cost, and estimation of detailed operating cost items such as maintenance, lubrication, etc. is not possible. To overcome these deficiencies, this paper aims to introduce up-to-date capital and detailed operating cost models considering multiple effective factors of flotation machines. Two sets of single (SRA) and multiple regression (MRA) cost functions are presented. The first set is suitable for cost estimation at the initial phases of a project and is mainly appropriate for building rapid cost estimates where only one particular design factor of a flotation machine is accessible. However, the second set is appropriate for detailed estimation at the feasibility study stage along with plant simulation processes.Cost modelling for flotation machines by S. Arfania*, A.R. Sayadi* † , and M.R. Khalesi* Flotation is one of the most widely used operations in mineral processing plants and assumes a significant share of the total milling costs. The purpose of this paper is to introduce a new set of capital and operating cost models for major flotation machines based on th...

show abstract

“…A neural network is modeled in a mathematical manner to implement an intelligent form as shown in the human brain, for utilization in engineering or in other fields [3]. The structure of an NN is as shown in Figure 1.…”

Section: Neural Networkmentioning

confidence: 99%

“…And the accuracy of construction costs estimation is a key factor in the success of a construction project, and also affects the decision-making by the owners [2][3][4]. But it is difficult to quickly and accurately estimate the construction costs at the planning stage, because the drawings and documentation are generally incomplete [5].…”

Section: Introductionmentioning

confidence: 99%

Comparison of School Building Construction Costs Estimation Methods Using Regression Analysis, Neural Network, and Support Vector Machine

Kim¹,

Shin

Kim³

et al. 2013

JBCPR

Self Cite

View full text Add to dashboard Cite

Accurate cost estimation at the early stage of a construction project is key factor in a project's success. But it is difficult to quickly and accurately estimate construction costs at the planning stage, when drawings, documentation and the like are still incomplete. As such, various techniques have been applied to accurately estimate construction costs at an early stage, when project information is limited. While the various techniques have their pros and cons, there has been little effort made to determine the best technique in terms of cost estimating performance. The objective of this research is to compare the accuracy of three estimating techniques (regression analysis (RA), neural network (NN), and support vector machine techniques (SVM)) by performing estimations of construction costs. By comparing the accuracy of these techniques using historical cost data, it was found that NN model showed more accurate estimation results than the RA and SVM models. Consequently, it is determined that NN model is most suitable for estimating the cost of school building projects.

show abstract

Comparison of construction cost estimating models based on regression analysis, neural networks, and case-based reasoning

Cited by 365 publications

References 35 publications

Cost Calculation of Construction Projects Including Sustainability Factors Using the Case Based Reasoning (CBR) Method

Cost Calculation of Construction Projects Including Sustainability Factors Using the Case Based Reasoning (CBR) Method

Cost modelling for flotation machines

Comparison of School Building Construction Costs Estimation Methods Using Regression Analysis, Neural Network, and Support Vector Machine

Contact Info

Product

Resources

About