Preliminary Engineering Cost Estimation Model for Bridge Projects

Hollar, Donna A.; Rasdorf, William; Liu, Min; Hummer, Joseph E.; Arocho, Ingrid; Hsiang, Simon M.

doi:10.1061/(asce)co.1943-7862.0000668

Cited by 25 publications

(20 citation statements)

References 7 publications

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“…Part of the research is focused on the development of models for estimating the costs of either selected cost components or elements of bridge structures. In [1], the costs of doing preliminary engineering as cost components of the total costs of newly built bridges are addressed. The authors introduced statistical models that link variation in preliminary engineering costs with specific parameters.…”

Section: Literature Reviewmentioning

confidence: 99%

On the Search of Models for Early Cost Estimates of Bridges: An SVM-Based Approach

Juszczyk

2019

Buildings

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The completion of a bridge construction project within budget is one of the project’s key factors of success. This prerequisite is more likely to be achieved if the cost estimates, especially those provided in the early stage of a project, are realistic and close to the actual costs. The paper presents the research results on the development of a cost prediction model based on machine learning, namely the support vector machines (SVM) method, for which the input represents basic information and parameters of bridges, available in the early stage of projects. Several SVM-based regression models were investigated with the use of data collected for a number of bridge construction projects completed in Poland. Having finished the machine learning and testing processes, five of the models, of satisfying knowledge generalization ability and comparable performance, were preselected. The final selection of the best model was based on the comparison and analysis ability to predict bridge construction costs with accuracy appropriate for the early stage of projects. The general testing metrics of the finally selected model, named BCCPMSVR2, were as follows: root mean square error: 1.111; correlation coefficient of real-life bridge construction costs and costs predicted by the model: 0.980; and mean absolute percentage error: 10.94%. The research resulted in the development and introduction of an original model capable of providing early estimates of bridge construction costs with satisfactory accuracy.

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Section: Literature Reviewmentioning

confidence: 99%

On the Search of Models for Early Cost Estimates of Bridges: An SVM-Based Approach

Juszczyk

2019

Buildings

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“…There are also studies that focused on modeling particular bridge costs. For example, Hollar et al (2013) investigated 461 bridge projects let by the North Carolina DOT between 2001 and 2009 and developed statistical models linking variation in the preliminary engineering costs with distinctive project parameters. The authors found that the preliminary engineering cost estimates for bridge projects were commonly and significantly underestimated.…”

Section: Bridge Agency Cost Models and User Cost Issuesmentioning

confidence: 99%

Strategic Scheduling of Infrastructure Repair and Maintenance: Volume 2—Developing Condition-Based Triggers for Bridge Maintenance and Rehabilitation Treatments

Zhang¹,

Labi²,

Fricker³

et al. 2018

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Even for the right treatment, improper timing can have consequences: premature application (treatment is applied too early) could mean wasteful spending even if users enjoy the benefits of higher pavement condition; deferred or delayed application (treatment is applied too late) could result in higher user costs due to poor condition and even reduced asset longevity. The objectives of this research were to establish the optimal condition or timing for each of the standard M&R treatment types typically used by INDOT; quantify the consequences of departures from such optimal conditions or timings; and to establish the optimal M&R treatment schedule for each asset family. The study focused on three asset types and their treatments: 1. Painting of Steel Bridges. A painting decision tree was developed, to serve as a framework that would enable INDOT to consider other maintenance treatment types, namely spot repair/painting and overcoating. 2. Bridge Deck Maintenance and Rehabilitation. Life-cycle condition-based deck M&R strategies based on different trigger results were proposed and presented. 3. Pavement Maintenance, Rehabilitation, and Replacement. A framework was established to find the optimal scheduling for multiple treatments and recommend appropriate long-term M&R strategies for flexible and rigid pavements on different functional classes.

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“…A study about abutment, pier, and foundation cost estimation model was carried out by Oh, et al (2013) based on standard quantity. The preliminary engineering cost estimation model for bridge projects was developed by Hollar, et al (2013) based on preliminary cost estimation of completed projects using linear regression. A study to determine the best estimating techniques for building construction cost was conducted by Kim, et al (2013).…”

Section: Introductionmentioning

confidence: 99%

Estimation Modeling of Abutment Volume with Variations of Bridge Span, Abutment Height, and Seismic Zone

Mahendra

Triwiyono

2020

Journal of the Civil Engineering Forum

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The initial cost estimate for a bridge project using an estimation model can be done based on the dimensions, type, and material of the bridge. Research that has included bridge location as a determinant variable for initial bridge cost estimation has not been carried out much. The different locations of the bridge project will have different seismic accelerations and seismic load analysis. This study aims to create a model used to calculate the quantity needed for the construction of abutment in various locations with a PCI-Girder superstructure. The data used for the quantity estimation model was derived from the abutment design results. The data obtained from the calculation of concrete and reinforcing steel quantities from the abutment design with variations in bridge span i.e. 20 m; 25 m; 30 m; 35 m; and 40 m, abutment height i.e. 4 m; 6 m; and 8 m, and seismic zone 1, 2, 3, and 4. Volume estimation models are obtained by multiple linear regression analysis. The results show a very strong correlation between the span of the bridge and the height of abutment with the dependent variables. While the seismic zone has a strong correlation with the dependent variables. However, the seismic zone in this study did not meet the linear regression assumptions. This study developed 8 models to estimate abutment volumes. The R2 values of these models are 0.983 – 0.997. These showed that the models are properly made to be used to estimate abutment volumes with a PCI-Girder superstructure.

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Preliminary Engineering Cost Estimation Model for Bridge Projects

Cited by 25 publications

References 7 publications

On the Search of Models for Early Cost Estimates of Bridges: An SVM-Based Approach

On the Search of Models for Early Cost Estimates of Bridges: An SVM-Based Approach

Strategic Scheduling of Infrastructure Repair and Maintenance: Volume 2—Developing Condition-Based Triggers for Bridge Maintenance and Rehabilitation Treatments

Estimation Modeling of Abutment Volume with Variations of Bridge Span, Abutment Height, and Seismic Zone

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