Improving the Accuracy of Early Cost Estimates on Transportation Infrastructure Projects

Karaca, Ilker; Gransberg, Douglas D.; Jeong, H. David

doi:10.1061/(asce)me.1943-5479.0000819

Cited by 32 publications

(34 citation statements)

References 44 publications

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“…The present research is concerned with the first type, which is the conceptual estimation of highway construction cost using known data at this stage. It is also known as the Top-Down Estimation method (Karaca et al, 2020), which only relies on the high-level project features and does not require detailed information about the project, such as the items quantities.…”

Section: Cost and Duration Estimationmentioning

confidence: 99%

“…Compared to duration estimation studies, more research was done for cost estimation in the pre-construction phase of projects (Elmouslami, 2020, Dehghan et al, 2020, Meharie and Shaik, 2020, Juszczyk, 2020, Tijanic et al, 2020, Karaca et al, 2020, Aretoulis, 2019, Meharie et al, 2019, Cao et al, 2018, Zhang et al, 2017, Gardner et al, 2017, Chou et al, 2015. Juszczk (2020) applied SVM on bridge projects completed between 2005 and 2018 to estimate their cost in the pre-construction phase by delivery method, length, width, and structural properties, and the optimum model had a MAPE of 10.94%.…”

Section: Cost and Duration Estimationmentioning

confidence: 99%

“…However, no recent study is comprehensive and combines project characteristics (Sharma et al, 2021, Meharie and Shaik, 2020, Son et al, 2019, project scope (Qiao et al, 2019, Okere, 2019, Juszczyk, 2020, cost and schedule information (Sharma et al, 2021, Titirla andAretoulis, 2019), level of technical complexity (Karaca et al, 2020, Gardner et al, 2017 and expected risk profile for different project types in one robust general model with acceptable accuracy. Some studies even use item quantities for duration and/or cost prediction in the conceptual phase when they may not be precisely determined (Nevett et al 2021;Pesko et al 2017;Nani et al 2017).…”

Section: Cost and Duration Estimationmentioning

confidence: 99%

“…ANNs have been repeatedly cited in the literature. For example, they have been used for the conceptual estimation of highway projects cost especially when limited information is available (Meharie and Shaik, 2020, Tijanic et al, 2019, Karaca et al, 2020, Aretoulis, 2019, Cao et al, 2018, Gardner et al, 2017, Chou et al, 2015. Additionally, they have been applied to estimate the duration of highway projects in their early stages, though not as widely as cost (Titirla and Aretoulis, 2019, Nani et al, 2017, Pesko et al, 2017, Mensah et al, 2016.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

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Conceptual estimation of construction duration and cost of public highway projects

Mohamed¹,

Moselhi²

2022

ITcon

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State Highway Agencies (SHAs) and Departments of Transportation (DOTs) allocate their limited resources to thousands of competing projects in multi-year transportation programs using expert judgement for the expected construction costs and durations. Such estimates overlook influencing parameters known in the planning phase and the importance of building reliable databases to support decision making. Meanwhile, it is possible to generate meaningful predictions in early stages of project development based on historical data gathering and analysis. The present research introduces a newly developed method for conceptual cost and duration estimation for public highway projects utilizing an ensemble of machine learning (ML) models and data collected for projects completed between 2004 and 2015 (roads, bridges, and drainage projects). Unlike previous studies, the proposed method includes project parameters that affect construction durations and costs and were not studied simultaneously before. The parameters considered are facility type, project scope, highway type, length, width, location, level of technical complexity, and new parameters pertinent to payment and procurement methods. The developed method was tested using 29 and 56 randomly selected projects, and the results yielded a Mean Absolute Percentage Error (MAPE) of 7.4% and 4.5% for the duration and cost, respectively, which are lower than the estimation errors of methods reported in recent literature. Additionally, the generalization abilities were assessed by the Mann-Whitney test, and the developed method is found to successfully handle diverse projects. Thus, machine learning models can assist agencies in the review process of competing projects from a high-level management perspective to ultimately develop better management execution programs.

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Section: Cost and Duration Estimationmentioning

confidence: 99%

Section: Cost and Duration Estimationmentioning

confidence: 99%

Section: Cost and Duration Estimationmentioning

confidence: 99%

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

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Conceptual estimation of construction duration and cost of public highway projects

Mohamed¹,

Moselhi²

2022

ITcon

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“…The review presented was focused on cost estimation for building projects and it is not intended to be exhaustive, but rather illustrate that different tools, sample sizes and variables have been used. There is also an extensive literature on other types of projects (e.g., transportation infrastructure projects - Karaca et al 2020;Swei et al, 2017;Flyvbjerg et al, 2016;Gunduz et al, 2011;Al-Tabtabai et al 1999).…”

Section: Literature Reviewmentioning

confidence: 99%

Cost Modelling from the Contractor Perspective: Application to Residential and Office Buildings

Monteiro¹,

Sousa²,

Meireles³

et al. 2021

Preprint

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For the majority of the contractual arrangements used in construction projects, the owner is not responsible for the cost deviations due to the variability of labor productivity or material price, amongst many other aspects. Consequently, the cost performance of a project may be entirely distinct for the owner and the contractor. Since the majority of the quantitative research on cost estimation and deviation found in the literature adopts the owners’ perspective, this research provides a contribution towards modelling costs and cost deviation from a contractors’ perspective. From an initial sample of 13 residential building and 10 office building projects, it was possible to develop models for cost estimation at the early stage of development including both endogenous and exogenous variables. Although the sample is relatively small, the authors were able to fully analyze all the cost data, using no secondary sources of data (very frequent in cost modelling studies). The statistically significant variables in the cost estimation models were the areas above and below ground and the years following the 2008 financial crisis, including the international bailout (2011-2014) period. For estimating the unit cost, a nonlinear model was obtained with the number of underground and total floor, the floor ratio and the years following the 2008 financial crisis, including the international bailout (2011-2014) period as predictors. For the office buildings, it was also found a statistically significant correlation between the cost deviation and the number of underground floors.

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A novel construction cost prediction model using hybrid natural and light gradient boosting

Chakraborty

Elhegazy

Elzarka

et al. 2020

Advanced Engineering Informatics

114

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Improving the Accuracy of Early Cost Estimates on Transportation Infrastructure Projects

Cited by 32 publications

References 44 publications

Conceptual estimation of construction duration and cost of public highway projects

Conceptual estimation of construction duration and cost of public highway projects

Cost Modelling from the Contractor Perspective: Application to Residential and Office Buildings

A novel construction cost prediction model using hybrid natural and light gradient boosting

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