Duration prediction models for construction projects: In terms of cost or physical characteristics?

Bayram, Savaş

doi:10.1007/s12205-016-0691-2

Cited by 27 publications

(18 citation statements)

References 16 publications

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“…Third, based on the case studies applied to local office buildings, the accuracy and validity of the developed model was verified by comparison analysis between the actual construction duration and the predicted construction duration calculated from an approximate construction duration prediction model. Generally, the construction duration for office buildings are determined in two ways: the owner defines the project duration directly (Jin et al 2016;Bayram 2017;Peško et al 2017) or the contractor suggests construction duration to the owner. On the other hand, the construction duration computation model utilized by contractors during the project planning stage (Table 1) is not a specialized standard for general office buildings Koo et al 2010;Jin et al 2016;Kim et al 2016;Thomas, N., Thomas, A. V. 2016).…”

Section: Scope and Methods Of The Studymentioning

confidence: 99%

Development of an Approximate Construction Duration Prediction Model During the Project Planning Phase for General Office Buildings

Yeom

Seo

Kim

et al. 2018

Journal of Civil Engineering and Management

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Abstract. Accurate prediction of the construction duration is imperative to the reliable cash flow analysis during the project planning phase when feasibility analysis is carried out. However, lack of information and frequent changes that occur as a result of a negotiation process between the owner and the designer in defining the project scope make it difficult to compute real-time construction duration. Domestic and foreign models for calculating the construction durations cannot be readily applied to computation of construction duration for general office buildings in Korea specifically during the project planning phase as there is a limit in its applicability due to numerous restrictions. Moreover, there are no preceding studies suggesting different computational approaches to predict the entire construction duration for office buildings with the approximate construction duration concept during planning phase. Therefore, based on the collected performance data, this study proposes a multiple linear regression model that facilitates reliable prediction of approximate construction duration for office buildings in the project planning phase. The model will allow the owner and other stakeholders to predict the real-time construction duration using the basic information on office buildings and to assess the construction durations incorporating frequent changes during the project planning phase.

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Section: Scope and Methods Of The Studymentioning

confidence: 99%

Development of an Approximate Construction Duration Prediction Model During the Project Planning Phase for General Office Buildings

Yeom

Seo

Kim

et al. 2018

Journal of Civil Engineering and Management

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“…As a result, although coefficient K, obtained from Bayram (2017), is similar to coefficient K for the educational buildings in this study, coefficient B, obtained from Bayram (2017)'s study is similar to coefficient B of hospital projects in this study. It is worthwhile to mention that in Bayram's (2017) data set is a composition of 530 public projects which mainly consist of educational buildings (432) and hospital projects (56), therefore direct comparison with this study will not yield accurate results, since all the projects are analyzed together [3].…”

Section: Basic Analysismentioning

confidence: 97%

“…This relationship proposed by the researcher is known as Bromilow's time -cost (BTC) model in the literature. This result is undoubtedly a very important guide both for the investors and the contractors in estimating the duration of the project as far as possible [3]. Bromilow has also stated that the time and cost variables of the BTC model can be affected and varied at various levels by a number of factors such as the type of project, the climate conditions of the location where the project is realized, the changes in the exchange rate, and the terms of the contract [2].…”

Section: Introductionmentioning

confidence: 97%

“…BTC Model Analysis Results for All Projects BTC Model Curves According to Project GroupsAs mentioned earlier Bayram (2017) studied time-cost relationships of public buildings in Turkey. While 82% of his data set consisted of educational buildings, 11% of the data set was hospital projects[3]. The equations obtained for the projects of the educational building (Equation 6), the hospitals (Equation 7) and the equation proposed by Bayram (2017) (Equation 5) can be compared as follows;…”

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confidence: 99%

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Time - Cost Relationships for Superstructure Projects in Turkey

2020

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The concept of time-cost relationship in construction projects was first introduced by Bromilow, using the data of the 328 superstructure projects completed in Australia. The aim of this study is to determine the time-cost relationship of superstructure projects in Turkey. Time and cost data of 460 superstructure projects completed between the years of 1999-2018 was used in the study. Data was grouped primarily on the basis of the intended use of buildings (individual buildings, educational buildings, hospitals, industrial buildings and social housing), and then time-cost relationships were separately determined for each group through statistical analysis. In addition, the effects of the parameters such as exchange rates and the number of non-working days on the time-cost relationship were investigated statistically, as well. As a result of this study, highly meaningful time-cost relationships (R 2 =0,60) are determined for the construction projects in Turkey, excluding disaster relief projects, which is not part of this study. It has also been determined that while the hospital projects in Turkey have the highest coefficient of determination (R 2 = 0,87), social housing projects have the lowest (R 2 = 0,54).

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“…The proposed integrated approach accounts for an alternative for conceptual estimation of costs. Furthermore, Bayram (2017) aimed at investigating and validating the 'Bromilow's time-cost (BTC) model' and the 'Love et al's time-floor (LTF) model' for Turkish public building projects, with emphasis on project duration. At the same time, the current research also provides 'best-fit models' as a benchmark for the BTC and the LTF models in order to fill the gap in construction duration predictions.…”

Section: Project Performance Prediction Modelsmentioning

confidence: 99%

Neural network models for actual cost prediction in Greek public highway projects

Aretoulis

2019

IJPOM

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Selected public Greek highway projects are examined in order to produce models to predict their actual construction cost based on data available at the bidding stage. Twenty highway projects, constructed in Greece, with similar type of available data were examined. Considering each project's attributes and the actual cost, correlation analysis is implemented, with the aid of SPSS. Correlation analysis identified the most predictive project variables. Additionally, the WEKA application, through its attribute selection function, highlighted the most efficient subset of variables. These selected variables through correlation analysis and WEKA are used as input neurons for neural network models. FANN Tool is used to construct neural network models. The optimum neural network model produced a mean squared error with a value of 7.68544E-05 and was based on budgeted cost, lowest awarding bid, technical work cost and electromechanical work cost.

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Duration prediction models for construction projects: In terms of cost or physical characteristics?

Cited by 27 publications

References 16 publications

Development of an Approximate Construction Duration Prediction Model During the Project Planning Phase for General Office Buildings

Development of an Approximate Construction Duration Prediction Model During the Project Planning Phase for General Office Buildings

Time - Cost Relationships for Superstructure Projects in Turkey

Neural network models for actual cost prediction in Greek public highway projects

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