Lifecycle Cost Study of Underground Freight Transportation Systems in Texas

Janbaz, Saeed; Shahandashti, Mohsen; Najafi, Mohammad; Tavakoli, Razieh

doi:10.1061/(asce)ps.1949-1204.0000322

Cited by 5 publications

(2 citation statements)

References 9 publications

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“…The costs used in this regression model are based on the contract costs of the existing tunnels. Costs of several tunneling activities, such as handling the spoils from tunnel excavation and construction of access shafts, are incorporated in the tunneling contract costs (Janbaz et al , 2018).…”

Section: Life-cycle Cost Estimation Of the Airport Uft Systemmentioning

confidence: 99%

Life-cycle cost analysis of a short-haul underground freight transportation system for the DFW Airport

Zahed

Shahooei

Farooghi

et al. 2019

BEPAM

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Purpose The purpose of this paper is to conduct life-cycle cost analysis of a short-haul underground freight transportation (UFT) system for the Dallas Fort Worth international airport. Design/methodology/approach The research approach includes: identifying the cost components of the proposed airport UFT system; estimating life-cycle cost (LCC) of system components using various methods; determining life-cycle cash flows; evaluating the reliability of the results using sensitivity analysis; and assessing the validity of the results using analogues cases. Findings Although the capital cost of constructing an airport UFT system seems to be the largest cost of such innovative projects, annual costs for running the system are more significant, taking a life-cycle perspective. System administrative cost, tunnel operation and maintenance, and tunnel construction cost are the principle cost components of the UFT system representing approximately 46, 24 and 19 percent of the total LCC, respectively. The shipping cost is estimated to be $4.14 per ton-mile. Although this cost is more than the cost of transporting cargos by trucks, the implementation of UFT systems could be financially justified considering their numerous benefits. Originality/value This paper, for the first time, helps capital planners understand the LCC of an airport UFT system with no or limited past experience, and to consider such innovative solutions to address airport congestion issues.

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Section: Life-cycle Cost Estimation Of the Airport Uft Systemmentioning

confidence: 99%

Life-cycle cost analysis of a short-haul underground freight transportation system for the DFW Airport

Zahed

Shahooei

Farooghi

et al. 2019

BEPAM

Self Cite

View full text Add to dashboard Cite

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“…Te research of tunnel asset cost involves three key aspects, including TMC prediction [2,3,32], the maintenance costs model based on life-cycle cost [33][34][35][36][37], and the maintenance cost model developed in the tunnel maintenance management system [5,33,[38][39][40]. Compared to the methods suggested in the literature, many of the case study applications estimated the costs using cost estimation methods based on expert opinion rather than statistical methods [37,41].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Annual Routine Maintenance Cost for Highway Tunnels

Xiao-li

Li³

et al. 2022

Advances in Civil Engineering

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In highway management, the prediction of the routine maintenance cost of tunnels is an important issue in saving tunnel maintenance costs due to its uncertainty, and the influencing factors should be carefully selected because too many variables could not be involved in the model. The complicated relationship between variables may lead to the inconsistency of model coefficients with the actual situation even though the goodness of fit of the model constructed with more variables is higher. This paper presents an approach in which quantitative analysis is combined with qualitative analysis to quickly select the independent variables of the tunnel routine maintenance cost (TMC) model. Based on the routine maintenance data collection of nine highway tunnels in Shaanxi province from 2007 to 2016, the independent variables of the models are determined with one-way ANOVA, Pearson correlation, partial correlation, and hierarchical regression. Afterwards, a fixed-effect regression model which can reflect the overall regional features is developed. Results show that tunnel age (Age) and tunnel length proportion (PET) have less effect on TMC among the main influencing factors such as district, Age, annual average daily traffic volume (AADT), truck traffic volume proportion (PTT), PET, and number of ventilation facilities (NVF), while the NVF makes a positive contribution to the TMC. Compared with grouped regression models, the fixed-effect regression model has higher fitting accuracy and a better regression coefficient significance. The quick independent variable selection method can shorten the time of establishing the model and determine the influencing factors of the research object effectively. The established model is suitable for forecasting the TMC and budget arrangement. In addition, the elastic analysis results of regression coefficients are helpful to the decision of maintenance strategy and the allocation of maintenance funds.

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