Log in

Bridge life cycle assessment with data uncertainty

  • UNCERTAINTIES IN LCA
  • Published:
The International Journal of Life Cycle Assessment Aims and scope Submit manuscript

Abstract

Purpose

Although life cycle assessment (LCA) has been employed to analyze the environmental impacts of bridges, the uncertainties associated to LCA have not been studied, which have a profound effect on the LCA results. This paper is intended to provide a comprehensive environmental impact assessment of bridge with data uncertainty, by assigning probability distributions on the considered parameters, assessing the variability in the acquisition of inventory and identifying the key parameters with significant environmental impacts.

Methods

A life cycle assessment of a bridge in Shanxi Province of China was conducted in a cradle-to-grave manner, by considering the source of the uncertainty of LCA. A statistical method was applied to quantify the uncertainty of measured inventory data and to calculate the probability distribution of the data. The uncertainty propagation was conducted through using a Monte Carlo simulation. Finally, the factor which is of vital importance to the assessment result was identified by sensitivity analysis.

Results and discussion

For the case of bridge, normal distribution can be adopted to fit environmental substances and environmental impact in steel production. The distributions of the weighted value of human health damage, ecological system damage, and resource and energy consumption can be represented by an approximate similar normal distribution function. The coefficient of variance (COV) of each weighted value is about 40, 30 to 40, and about 6 %, respectively. The COV for the total environmental impact is about 10 % in all stages of the bridge’s life cycle, except the operation stage, which is up to 22.67 %. By conducting sensitivity analysis, PM10, NOx, and oil consumption was found to have a great influence on the result of human health damage, ecological system damage, and resource and energy consumption, respectively.

Conclusions

The COV for the total environmental impact is 22.67 % in the bridge’s operation stage; it is important to establish a reasonable maintenance strategy to decrease the uncertainty of the bridge’s environmental impact. The COVs of the weighted value for human health damage and resource and energy consumption have a quite modest difference among the four stages of the bridge’s life cycle. However, The COV of the weighted value for ecological system damage shows large difference among the four stages of the bridge’s life cycle; construction stage has the greatest uncertainty. In addition, different values of PM10, NOx, and oil consumption have a profound influence on the result of human health damage, ecological system damage, and resource and energy consumption, respectively.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others

References

  • Agusdinata B (2008) Exploratory modeling and analysis: a promising method to deal with deep uncertainty. TU Delft, Delft University of Technology

  • Bankes S (1993) Exploratory modeling for policy analysis. Oper Res 41(3):435–449

    Article  Google Scholar 

  • Bankes S, Walker WE, Kwakkel JH (2013) Exploratory modeling and analysis. In: Encyclopedia of operations research and management science. Springer US, p 532–537

  • Basset-Mens C, Van Der Werf HM, Durand P, Leterme P (2006) Implications of uncertainty and variability in the life cycle assessment of pig production systems. Int J Life Cycle Assess 11(5):298–304

    Article  CAS  Google Scholar 

  • Basson L, Petrie JG (2007) An integrated approach for the consideration of uncertainty in decision making supported by Life Cycle Assessment. Environ Model Software 22(2):167–176

    Article  Google Scholar 

  • Canter KG, Kennedy DJ, Montgomery DC, Keats JB, Carlyle WM (2002) Screening stochastic life cycle assessment inventory models. Int J Life Cycle Assess 7(1):18–26

    Article  Google Scholar 

  • Cellura M, Longo S, Mistretta M (2011) Sensitivity analysis to quantify uncertainty in life cycle assessment: the case study of an Italian tile. Renew Sust Energ Rev 15(9):4697–4705

    Article  Google Scholar 

  • Goedkoop M, Spriensma R (2001) The eco-indicator99: a damage oriented method for life cycle impact assessment: Methodology report. Pre Consultants, Amersfoort, pp 1–144

    Google Scholar 

  • Guo M, Murphy RJ (2012) LCA data quality: sensitivity and uncertainty analysis. Sci Total Environ 435:230–243

    Article  Google Scholar 

  • Heijungs R (1996) Identification of key issues for further investigation in improving the reliability of life-cycle assessments. J Clean Prod 4(3):159–166

    Article  Google Scholar 

  • Huijbregts MA (1998) Application of uncertainty and variability in LCA. Int J Life Cycle Assess 3(5):273–280

    Article  Google Scholar 

  • Huijbregts MA, Norris G, Bretz R, Ciroth A, Maurice B, von Bahr B, de Beaufort AS (2001) Framework for modelling data uncertainty in life cycle inventories. Int J Life Cycle Assess 6(3):127–132

    Article  Google Scholar 

  • Hung ML, Ma HW (2009) Quantifying system uncertainty of life cycle assessment based on Monte Carlo simulation. Int J Life Cycle Assess 14(1):19–27

    Article  Google Scholar 

  • ISO (2006a) ISO 14040: environmental management-life cycle assessment-principles and framework. International Organization for Standardization, Geneva

    Google Scholar 

  • ISO (2006b) ISO 14044: environmental management-life cycle assessment-requirements and guidelines. International Organization for Standardization, Geneva

    Google Scholar 

  • Kucukvar M, Noori M, Egilmez G, Tatari O (2014) Stochastic decision modeling for sustainable pavement designs. Int J Life Cycle Assess 19(6):1185–1199

    Article  CAS  Google Scholar 

  • Kwakkel JH, Pruyt E (2013) Exploratory modeling and analysis, an approach for model-based foresight under deep uncertainty. Technol Forecast Soc Chang 80(3):419–431

    Article  Google Scholar 

  • Lee Rodgers J, Nicewander WA (1988) Thirteen ways to look at the correlation coefficient. Am Stat 42(1):59–66

    Article  Google Scholar 

  • Li P (2014) Life cycle assessment model for municipal solid waste management system under uncertainty. North China Electric Power University (in Chinese)

  • Lilliefors HW (1967) On the Kolmogorov-Smirnov test for normality with mean and variance unknown. J Am Stat Assoc 62(318):399–402

    Article  Google Scholar 

  • Liu XL, Wang HT, Chen J, He Q, Zhang H, Jiang R, Hou P (2010) Method and basic model for development of Chinese reference life cycle database. Acta Sci Circumst 30(10):2136–2144 (in Chinese)

    CAS  Google Scholar 

  • Lo SC, Ma HW, Lo SL (2005) Quantifying and reducing uncertainty in life cycle assessment using the Bayesian Monte Carlo method. Sci Total Environ 340(1):23–33

    Article  CAS  Google Scholar 

  • Malekzadeh M, Atia G, Catbas FN (2015a) Performance-based structural health monitoring through an innovative hybrid data interpretation framework. J Civ Struct Heal Monit 5(3):287–305

    Article  Google Scholar 

  • Malekzadeh M, Atia G, Catbas FN (2015b) A hybrid data interpretation framework for automated performance monitoring of infrastructure. Struct Congr 2015:426–434

    Google Scholar 

  • Marceau M, Nisbet MA, Van Geem MG (2007) Life cycle inventory of Portland cement Concrete. Portland Cement Association

  • Noori M, Tatari O, Nam B, Golestani B, Greene J (2014) A stochastic optimization approach for the selection of reflective cracking mitigation techniques. Transp Res A Policy Pract 69:367–378

    Article  Google Scholar 

  • Noori M, Kucukvar M, Tatari O (2015a) A macro-level decision analysis of wind power as a solution for sustainable energy in the USA. Int J Sustainable Energy 34(10):629–644

    Article  Google Scholar 

  • Noori M, Gardner S, Tatari O (2015b) Electric vehicle cost, emissions, and water footprint in the United States: development of a regional optimization model. Energy 89:610–625

    Article  Google Scholar 

  • Noshadravan A, Wildnauer M, Gregory J, Kirchain R (2013) Comparative pavement life cycle assessment with parameter uncertainty. Transp Res Part D: Transp Environ 25:131–138

    Article  Google Scholar 

  • NREL (2012) US Life Cycle Inventory Database. Colorado: National Renewable Energy Laboratory. Available at https://www.lcacommons.gov/nrel/search

  • Ross S, Evans D, Webber M (2002) How LCA studies deal with uncertainty. Int J Life Cycle Assess 7(1):47–52

    Article  Google Scholar 

  • Rypdal K, Flugsrud K (2001) Sensitivity analysis as a tool for systematic reductions in greenhouse gas inventory uncertainties. Environ Sci Pol 4(2):117–135

    Article  CAS  Google Scholar 

  • Seager TP, Linkov I (2009) Uncertainty in life cycle assessment of nanomaterials. In: Nanomaterials: risks and benefits. Springer Netherlands, p 423–436

  • Tatari O, Nazzal M, Kucukvar M (2012) Comparative sustainability assessment of warm-mix asphalts: a thermodynamic based hybrid life cycle analysis. Resour Conserv Recycl 58:18–24

    Article  Google Scholar 

  • Tukker A (1998) Uncertainty in life cycle impact assessment of toxic releases practical experiences-arguments for a reductionalistic approach? Int J Life Cycle Assess 3(5):246–258

    Article  CAS  Google Scholar 

  • Wang E, Shen Z (2012) Improving uncertainty estimate of embodied-energy of construction materials using analytical hierarchical process in weighted DQI method. Constr Res Congr 2012:1840–1849

    Google Scholar 

  • Weidema BP, Wesnaes MS (1996) Data quality management for life cycle inventories—an example of using data quality indicators. J Clean Prod 4(3):167–174

    Article  Google Scholar 

  • World Steel Association (2010) LCI data for steel products. Available at http://www.worldsteel.org/zh/steel-by-topic/life-cycle-assessment/about-the-lci.html

  • Wu WJ (2013) Sustainability quantitative assessment of reinforced concrete bridges based on uncertainties. Bei**g Jiaotong University (in Chinese)

  • Wu WJ, Wang YF, **e HB (2013) Bridge maintenance strategies optimization based on life cycle assessment and time-dependent reliability analysis. J Highw Transp Res Dev 30(9):94–100 (in Chinese)

    Google Scholar 

  • Zheng Y, Zhang TZ (2006) Life cycle assessment under data uncertainty and its application. Chongqing Environ Sci 25(6):18-20 + 54 (in Chinese)

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yuan-Feng Wang.

Additional information

Responsible editor: Omer Tatari

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, YR., Wu, WJ. & Wang, YF. Bridge life cycle assessment with data uncertainty. Int J Life Cycle Assess 21, 569–576 (2016). https://doi.org/10.1007/s11367-016-1035-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11367-016-1035-7

Keywords

Navigation