Time–cost relationship of building projects: statistical adequacy of categorization with respect to project location

Dursun, Onur; Stoy, Christian

doi:10.1080/01446193.2010.528437

Cited by 15 publications

(9 citation statements)

References 17 publications

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“…erefore, the existing models are not universally applicable and must be defined according to structure categories for each country separately. Consequently, similar studies have been conducted in many countries in order to obtain adequate and corresponding time assessment models [15,18,35,55,56]. e most representative variables that influence the construction duration are as follows: type of structure, contracted time, real construction time, contracted price, and real construction price; these were chosen for building the model.…”

Section: Process-basedmentioning

confidence: 99%

Implementation of Process‐Based and Data‐Driven Models for Early Prediction of Construction Time

Petruseva

Žileska-Pančovska

Car-Pušić

2019

Advances in Civil Engineering

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The need of respecting the construction time as one of the construction contract elements points out that early prediction of construction time is of crucial importance for the construction project participants’ business. Thus, having a model for early prediction of construction time is useful not only for the participants involved in the construction contracting process, but also for other participants in the construction project realization. Regarding that, this paper aims to present a hybrid method for predicting construction time in the early project phase, which is a combination of process-based and data-driven models. Five hybrid models have been developed, and the most accurate one was the BTC-GRNN model, which uses Bromilow’s time-cost (BTC) model as a process-based model and the general regression neural network (GRNN) as a data-driven model. For evaluating the quality of the models, the 10-fold cross-validation method has been used. The mean absolute percentage error (MAPE) of the BTC-GRNN is 3.34% and the coefficient of determination R2, which reflects the global fit of the model, is 93.17%. These results show a drastic improvement of the accuracy in comparison to the model when only data-driven model (GRNN) has been used, where MAPE was 31.8% and R2 was 75.64%. This model can be useful to the investors, the contractors, the project managers, and other project participants for construction time prediction in the early project phases, especially in the phases of bidding and contracting, when many factors, that can determine the construction project realization, are unknown.

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Section: Process-basedmentioning

confidence: 99%

Implementation of Process‐Based and Data‐Driven Models for Early Prediction of Construction Time

Petruseva

Žileska-Pančovska

Car-Pušić

2019

Advances in Civil Engineering

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“…For example Bromilow (1969;1974) developed a model known as Bromilow's time-cost (BTC) model that uses duration as a function of cost to predict project duration [18,19]. Although the model was adapted by other researchers and used in several countries, the results of many applications of the model were reported as being unreliable, see for examples Odabasi (2009), Dursun & Stoy (2011), Ogunsemi & Jagboro (2006), Walker (1994) [20,21,22,23].…”

Section: Research Background and Literature Reviewmentioning

confidence: 99%

Reference Class Forecasting Method in Predicting Construction Project Duration

Bayram

Al-Jibouri

2018

Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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Project delay is a global problem affecting construction and other industries in many countries. Its impact on planning and budgeting can be serious for all stakeholders involved and difficult to resolve. The purpose of this study is to analyze the reliability of duration estimates of public building projects based on actual duration of similar projects carried out in the past.Turkey is used as a case study for this purpose and data from 643 public building projects completed in Turkey were collected. The data include contract durations, actual durations as well as the total construction areas for all projects. Reference Class Forecasting (RCF) method is proposed and used to investigate whether it would be possible to produce reliable and realistic project duration forecasts based on such data. RCF can realistically predict the actual final duration of the projects of different reference classes for various levels of acceptable risks. Original estimates of the contract durations in Turkey are generally optimistic or underestimated. Government buildings with the highest average construction area required lower uplift values on the estimated durations to produce accurate and realistic forecasts.So far the RCF method has been broadly applied to predict project cost rather than duration. This paper describes it use for forecasting duration in building projects.Özet: Projelerde yaşanan süresel gecikmeler, pek çok ülkede inşaat sektörünü ve diğer sektörleri etkileyen küresel bir sorundur. Süresel gecikmelerin planlama ve bütçeleme üzerindeki etkisi, ilgili tüm paydaşlar için ciddi ve çözülmesi zor olmaktadır. Bu çalışmanın amacı, geçmişte tamamlanan benzer projelerin gerçekleşen sürelerine dayalı olarak, kamu binası projelerinin süresel tahminlerinin güvenilirliğini analiz etmektir. Bu amaçla Türkiye'de tamamlanan 643 kamu inşaat projesinin verileri temin edilmiştir. Proje verileri; sözleşme süreleri, fiili gerçekleşme süreleri ve toplam inşaat alanlarından oluşmaktadır. Bu verilere dayalı olarak güvenilir ve gerçekçi proje süresi tahminleri üretmenin mümkün olup olmayacağını irdelemek için Referans Sınıf Tahmin (RST) yöntemi önerilmiş ve kullanılmıştır. RST, kabul edilebilir risklerin farklı seviyeleri için değişik referans sınıflarının fiili proje sürelerini gerçekçi bir şekilde tahmin edebilen bir yaklaşımdır. Sonuç olarak; Türkiye'de gerçekleştirilen kamu inşaat projelerinin sözleşme süresi öngörülerinin genellikle iyimser veya olması gerekenden düşük olduğu belirlenmiştir. Doğru ve gerçekçi tahminler üretmek adına, en yüksek ortalama inşaat alanına sahip olan devlet binalarının, öngörülen proje süreleri üzerinden nispeten daha düşük yükseltme değerlerine sahip oldukları tespit edilmiştir. RST yöntemi, önceki çalışmalarda proje süresinden ziyade yapım maliyetini tahmin etmek için yaygın bir şekilde kullanılmıştır. Bu çalışmada ise RST yönteminin bina projelerinin süresel tahmininde kullanılabilirliği esas alınmıştır.

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“…Multiple linear regression and factor analysis models are used by several authors for estimating project duration and cost in the construction industry (Skitmore, Ng 2003;Chan, Kumaraswamy 1999 Hammad et al 2008;Dursun, Stoy 2011;Doloi et al 2012). Bromilow (1969) developed a regression model (commonly known as Bromilow's time-cost (BTC) model for estimating contract duration based on the estimated final cost of a construction project (Skitmore, Ng 2003).…”

Section: Multiple Linear Regression and Factor Analysismentioning

confidence: 99%

The Effect of Current Public Procurement Law on Duration and Cost of Construction Projects in Turkey

Erdiş

2013

Journal of Civil Engineering and Management

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The current public procurement law (Law No. 4734) was established in Turkey in 2003. The current law has fundamental differences from the previous one, Law No. 2886, in that the current law's main objective is to increase the effective use of public resources. Although the current law was enacted nine years ago, no in-depth research has been undertaken related to the extent of public savings. Thus, the aim of this research is to analyze the performance of public investments for construction with respect to their success in achieving on time and within budget completion. Additionally, a comparison between the completion duration and budget of construction projects undertaken under the current and the previous law is presented. To achieve these goals, historical contract documents addressing 878 and 575 public construction projects undertaken under two laws, respectively, were analyzed. In this context, the data mining method, including decision trees, artificial neural network, and support vector machines, was applied to predict the duration and cost deviations of the construction projects during the tender process, and the results were compared. It was demonstrated that the current law has contributed substantially towards the completion of the projects within estimated or envisaged durations and costs. The findings of this research can be generalized to countries with similar economical and organizational structures with Turkey.

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Time–cost relationship of building projects: statistical adequacy of categorization with respect to project location

Cited by 15 publications

References 17 publications

Implementation of Process‐Based and Data‐Driven Models for Early Prediction of Construction Time

Implementation of Process‐Based and Data‐Driven Models for Early Prediction of Construction Time

Reference Class Forecasting Method in Predicting Construction Project Duration

The Effect of Current Public Procurement Law on Duration and Cost of Construction Projects in Turkey

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