An approximate cost estimation model based on standard quantities of steel box girder bridge substructure

Oh, Chi Don; Park, Chan-Sik; Kim, Kyong Ju

doi:10.1007/s12205-013-0287-z

Cited by 6 publications

(4 citation statements)

References 14 publications

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“…RA is one of the most commonly used techniques in statistical modeling and has been used to make both direct (Bowen & Edwards, 1985;Khosrowshahi & Kaka, 1996;Kim et al, 2004;Lowe et al, 2006) and indirect construction cost estimates based on CMQ estimates (Bakhoum et al, 1998;Chou et al, 2006;Du & Bormann, 2014;Fragkakis et al, 2011;Kim et al, 2009;Oh et al, 2013;Singh, 1990Singh, , 1991Son et al, 2013;Yeh, 1998). RA is one of the most commonly used techniques in statistical modeling and has been used to make both direct (Bowen & Edwards, 1985;Khosrowshahi & Kaka, 1996;Kim et al, 2004;Lowe et al, 2006) and indirect construction cost estimates based on CMQ estimates (Bakhoum et al, 1998;Chou et al, 2006;Du & Bormann, 2014;Fragkakis et al, 2011;Kim et al, 2009;Oh et al, 2013;Singh, 1990Singh, , 1991Son et al, 2013;Yeh, 1998).…”

Section: Model Types Included In Proposed Processmentioning

confidence: 99%

“…One way to make preliminary estimates is to determine the amount of construction material quantities (CMQs) to be used in the project (Bakhoum, Morcous, Taha, & El-Said, 1998;Chou, Peng, Persad, & O'Connor, 2006;Du & Bormann, 2014;Fragkakis, Lambropoulos, & Tsiambaos, 2011;Kim, Kim, & Kang, 2009;Oh, Park, & Kim, 2013;Singh, 1990Singh, , 1991 Background One way to make preliminary estimates is to determine the amount of construction material quantities (CMQs) to be used in the project (Bakhoum, Morcous, Taha, & El-Said, 1998;Chou, Peng, Persad, & O'Connor, 2006;Du & Bormann, 2014;Fragkakis, Lambropoulos, & Tsiambaos, 2011;Kim, Kim, & Kang, 2009;Oh, Park, & Kim, 2013;Singh, 1990Singh, , 1991 Background…”

Section: Introductionmentioning

confidence: 99%

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A Process for the Development and Evaluation of Preliminary Construction Material Quantity Estimation Models Using Backward Elimination Regression and Neural Networks

Soto

Adey

Fernando

2014

Journal of Cost Analysis and Parametrics

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During the early stages of a project, it is beneficial to have an accurate preliminary estimate of its cost. One way to make those estimates is by determining the amount of construction material quantities that are required and then multiplying the estimated construction material quantities by the corresponding unit cost. One advantage of making estimates in this way is that it allows for the segregation of quantities and costs. This way they can be updated separately as new information becomes available. They can also be tracked separately allowing decision makers to make better decisions about the project during its conceptual phase. There are several techniques that can be used to develop estimation models. The most common include regression analysis and artificial intelligence, such as neural networks. Work has been done, however, in a non-standardized way, leaving practitioners without guidance as to how to develop and evaluate models for their specific purposes. This can be seen in particular in the many different types of metrics used for the evaluation of models. The goal of the work presented in this article was to create a process to (1) develop models to be used to prepare preliminary estimates of construction material quantities taking into consideration the available data during the early stages of a project, and (2) evaluate the developed models using the Akaike information criterion. The proposed process is illustrated with an example in which data from 58 storage buildings was used to develop models to estimate the amount of concrete and reinforcement required using backward elimination regression analysis and neural network techniques. The developed models were then evaluated using a second-order correction Akaike information criterion (AIC c ) to select the most accurate model for each construction material quantity. The proposed process is useful for practitioners in need of developing robust estimation models in a consistent and systematic way, and the AIC c metric proved to be effective for selecting the most accurate models from a set. ) and then to multiply these estimates by the unit costs, which typically include fabrication and delivery, erection, installation, insurance, site indirect costs, supervision, and overhead and profit. One advantage of making estimates in this way, as compared to estimating cost directly, is that either the CMQs or their unit costs can be updated separately as new information becomes available. It also allows managers to make better decisions and keep a better track of the project (Chou et al., 2006;Yu, 2006) by controlling the changes in quantities and costs separately.During the early stages of a project, CMQs are generally estimated by finding existing structures that are similar to the structures to be constructed and by adjusting the CMQs used in the former to estimate the CMQs to be used in the latter. The adjustments are done based on the differences in the values of key parameters (e.g., height), taking into consideration the experience of the estimato...

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Section: Model Types Included In Proposed Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Process for the Development and Evaluation of Preliminary Construction Material Quantity Estimation Models Using Backward Elimination Regression and Neural Networks

Soto

Adey

Fernando

2014

Journal of Cost Analysis and Parametrics

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show abstract

“…The accuracy of initial cost estimation is necessary to reach success in project budgeting. This depends on the available information and data related to the project (Oh, et al, 2013). The initial cost estimation often not accurate because of the incompleteness of the information and data.…”

Section: Introductionmentioning

confidence: 99%

“…A study about a model to an approximate cost estimate for a railway bridge project in the planning phase was done by Kim (2011) using CBR (Case Based Reasoning) method. 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.…”

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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Seismic assessment and parametric analysis on a multi-level aseismic simply-supported bridge with cable-restraining composite rubber bearings and dissipative controlled rocking piers

Zhong,

Guo,

Yuan

et al. 2024

Soil Dynamics and Earthquake Engineering

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An approximate cost estimation model based on standard quantities of steel box girder bridge substructure

Cited by 6 publications

References 14 publications

A Process for the Development and Evaluation of Preliminary Construction Material Quantity Estimation Models Using Backward Elimination Regression and Neural Networks

A Process for the Development and Evaluation of Preliminary Construction Material Quantity Estimation Models Using Backward Elimination Regression and Neural Networks

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

Seismic assessment and parametric analysis on a multi-level aseismic simply-supported bridge with cable-restraining composite rubber bearings and dissipative controlled rocking piers

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