Estimating Preconstruction Services for Bridge Design Projects

Abdelaty, Ahmed; Shrestha, Kishor; Jeong, H. David

doi:10.1061/(asce)me.1943-5479.0000793

Cited by 14 publications

(8 citation statements)

References 18 publications

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“…Accurate estimation and reliable optimization are provided by the ANN model to make the project more effective and smoother. Experience has proved that the predictive performance of ANN is indeed better than traditional methods such as multiple regression analysis [120], and the recently optimized ANN model has made great progress in prediction performance [97]. Reliable results can avoid potential errors and reduce unnecessary waste, which is essential to the success of the construction project.…”

Section: Document Co-citation Analysismentioning

confidence: 99%

Application of Artificial Neural Networks in Construction Management: A Scientometric Review

Chang

Pan³

et al. 2022

Buildings

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As a powerful artificial intelligence tool, the Artificial Neural Network (ANN) has been increasingly applied in the field of construction management (CM) during the last few decades. However, few papers have attempted to draw up a systematic commentary to appraise the state-of-the-art research on ANNs in CM except the one published in 2000. In the present study, a scientometric analysis was conducted to comprehensively analyze 112 related articles retrieved from seven selected authoritative journals published between 2000 and 2020. The analysis identified co-authorship networks, collaboration networks of countries/regions, co-occurrence networks of keywords, and timeline visualization of keywords, together with the strongest citation burst, the active research authors, countries/regions, and main research interests, as well as their evolution trends and collaborative relationships in the past 20 years. This paper finds that there is still a lack of systematic research and sufficient attention to the application of ANNs in CM. Furthermore, ANN applications still face many challenges such as data collection, cleaning and storage, the collaboration of different stakeholders, researchers and countries/regions, as well as the systematic design for the needed platforms. The findings are valuable to both the researchers and industry practitioners who are committed to ANNs in CM.

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Section: Document Co-citation Analysismentioning

confidence: 99%

Application of Artificial Neural Networks in Construction Management: A Scientometric Review

Chang

Pan³

et al. 2022

Buildings

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show abstract

“…Handling Missing Data and Outliers: The collected dataset had no missing values within the variables selected for the scope of the current research. On the other hand, the modified z-score, which is suitable for smaller datasets than z-score, was applied to eliminate the outliers using Equation 1 (Abdelaty et al, 2020). The data points whose modified z-score is more than 3.5 are eliminated from further analysis.…”

Section: Preliminary Data Analysis and Preparationmentioning

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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“…The strength of this method is its ease of use; it does not require complex calculations and modeling techniques. However, both the accuracy and reliability of the POCC method have been questioned since the use of a single number regardless of project characteristics may result in the under- or over-allocation of PE funding ( 4 , 14 , 15 ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…The next step was inflation adjustment. Previous studies on PE cost estimation either incorporated inflation ( 14 , 18 ) or removed the inflation effect by using the PE cost ratios ( 11 , 15 ). To assess the variance of PE cost by contract size more accurately, inflation was applied to both the PE cost and original contract cost of the projects.…”

Section: Data Collection and Processingmentioning

confidence: 99%

Developing a Preliminary Engineering Cost Estimation Method for a Portfolio of Bridge Construction Projects in Project Planning Phase

Son

Khwaja

2022

Transportation Research Record

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For state departments of transportation (DOTs), estimating preliminary engineering (PE) costs over a multiple-year horizon is critical for proper planning of engineering resources and budgets to ensure timely delivery of a portfolio of bridge construction projects, which incur PE costs that are relatively higher than those required for other highway project types. However, given the uncertainties in the planning phase, it is challenging to estimate PE costs effectively and efficiently. The purpose of this study was to provide DOTs with a PE cost estimation method using different ratios of PE cost to construction cost for bridge projects by project type and size. To that end, the study tested the variability of PE costs by analyzing 628 Texas DOT bridge construction projects divided into three project types: bridge maintenance (BMN), bridge replacement (BR), and bridge widening and rehabilitation (BWR). The analysis found that the ratios of PE cost to construction cost for the BMN and BR project types were significantly different by project size, but was not conclusive for the BWR project type because of limited sample size. The validation results indicate that the proposed method is effective for planning and estimating the PE costs of a portfolio of projects. The study findings can provide DOTs with an improved PE cost estimation process for a portfolio of bridge projects during the project planning phase.

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Estimating Preconstruction Services for Bridge Design Projects

Cited by 14 publications

References 18 publications

Application of Artificial Neural Networks in Construction Management: A Scientometric Review

Application of Artificial Neural Networks in Construction Management: A Scientometric Review

Conceptual estimation of construction duration and cost of public highway projects

Developing a Preliminary Engineering Cost Estimation Method for a Portfolio of Bridge Construction Projects in Project Planning Phase

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