Planning-Phase Estimation of Construction Time for a Large Portfolio of Highway Projects

Son, Junghye; Khwaja, Nabeel; Milligan, Duane S.

doi:10.1061/(asce)co.1943-7862.0001637

Cited by 13 publications

(16 citation statements)

References 14 publications

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“…After conducting the VIF assessment, none of the variables’ VIF exceed 4, therefore the data used for the model remained unchanged. The multicollinearity assessment removes the concerns about multicolliniearity when using design parameters, which have been pointed out by authors in the past ( 7 , 8 )…”

Section: Methodsmentioning

confidence: 99%

“…This concept refers to how the percent change of the response (dependent) variable relates to one percent of the predictor (independent) variable. Another benefit of using log transformations for all the variables is not having to face issues of inverse data transformations, like the ones faced by Son et al ( 8 ). For example, Equation 3 shows a log-log transformation equation used in the analyses.…”

Section: Methodsmentioning

confidence: 99%

“…Son et al studied the impact of cost, project type, and county size in the duration for large portfolios of construction projects ( 8 ). They explored different models with different data transformations, including log transformations and square root transformations, and found that the results and predictability of transformed and inverse transormed data (i.e., reversing the transformation) have no statistical difference in the amount of explained variability of the models.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Understanding the Effect of Bid Quantities, Project Characteristics, and Project Locations on the Duration of Road Transportation Construction Projects during Early Stages

Nevett

Goodrum

Littlejohn

2020

Transportation Research Record

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Creating accurate estimates for the duration of road transportation projects is a challenging task that estimators and engineers have to face daily, and a lack of understanding of what drives such durations is a big contributor to this challenge. This paper expands on the efforts created by previous research efforts, not only by adding more variables to the model, but also by combining all project sizes and types into a single model. These additions increased the amount of explained variability, and the range of projects that can be studied with one model. This paper aims to determine which of the variables analyzed best predict project durations.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Understanding the Effect of Bid Quantities, Project Characteristics, and Project Locations on the Duration of Road Transportation Construction Projects during Early Stages

Nevett

Goodrum

Littlejohn

2020

Transportation Research Record

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show abstract

“…Several studies have attempted to model the relationship between highway projects features and their durations in early stages (Nevett et al, 2021, Titirla and Aretoulis 2019, Son et al, 2019, Okere, 2019, Nani et al, 2017, Pesko et al, 2017. Pesko et al, (2017) concluded that integrating the estimation of duration and cost can compromise the significance of input data, leading to reduction of estimating accuracy compared to that based on separate estimation.…”

Section: Cost and Duration Estimationmentioning

confidence: 99%

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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“…Regression modeling is a form of predictive modeling that investigates the relationship between the dependent and variables. In construction engineering and management, regression modeling has been used to provide decision support insights for construction operations (Bowen et al, 2014;Son et al, 2019). In this research, regression modeling is used to model the scaffolding productivity from onsite operations.…”

Section: Productivity Regression Modellingmentioning

confidence: 99%

An earned-value-analysis (EVA)-based project control framework in large-scale scaffolding projects using linear regression modeling

Lei¹,

Hu²,

Hua³

et al. 2022

ITcon

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In large-scale industrial construction projects, scaffolding activities account for a large amount of the construction budget, and overlooking the scaffolding management can lead to budget overruns and schedule delays. The scaffolding activities can be categorized by classifications and types based on the nature of the scaffold builds. To ensure the project progress on track, it is critical to measure project performance based on project progress data. However, given the nature of scaffolding activities, it has been challenging to track and utilize the scaffolding data for analytical purposes. Therefore, this paper proposes a project control framework based on Earned-value analysis (EVA), in which linear regression models are used for productivity prediction. Three scenarios of productivity based on historical data (i.e., low, medium, and high productivity) are introduced. The proposed framework is implemented in a real construction project for validation. The results have shown that the proposed framework can efficiently evaluate the project progresses integrated with the EVA. The construction companies, such as general contractors and scaffolding sub-contractors, can use this method for site progress tracking. For future work, the EVA can be integrated with other non-linear predictive models (e.g., neural network) for productivity prediction. The EVA results can be integrated with data visualization to create situational awareness for construction practitioners.

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Planning-Phase Estimation of Construction Time for a Large Portfolio of Highway Projects

Cited by 13 publications

References 14 publications

Understanding the Effect of Bid Quantities, Project Characteristics, and Project Locations on the Duration of Road Transportation Construction Projects during Early Stages

Understanding the Effect of Bid Quantities, Project Characteristics, and Project Locations on the Duration of Road Transportation Construction Projects during Early Stages

Conceptual estimation of construction duration and cost of public highway projects

An earned-value-analysis (EVA)-based project control framework in large-scale scaffolding projects using linear regression modeling

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