Technical Papers
Jun 27, 2019

Optimal Groundwater-Use Strategy for Saltwater Intrusion Management in a Pacific Island Country

Publication: Journal of Water Resources Planning and Management
Volume 145, Issue 9

Abstract

Escalating salinity levels in the Bonriki aquifer due to unplanned groundwater extraction is a major concern for the people of Kiribati. A multiobjective management model capable of providing sustainable optimal groundwater pumping strategies and simultaneously confining salinity concentrations in the aquifer within specified limits is needed for the Bonriki aquifer system. This study applies a regional-scale linked simulation-optimization (S/O) methodology with a Pareto front clustering technique to prescribe optimal groundwater withdrawal patterns from the Bonriki aquifer. A numerical simulation model is calibrated and validated using available field data. For computational feasibility, support vector regression (SVR) surrogate models are trained and tested utilizing input-output data sets generated using the numerical flow and transport simulation model. The developed surrogate models were externally coupled with the multiobjective genetic algorithm optimization (MOGA) model as a substitute for the numerical model. The study area consisted of freshwater pumping wells for extracting groundwater for beneficial use. Pumping from barrier wells installed along the coastlines were also considered as a management option to hydraulically control saltwater intrusion. The executed multiobjective linked S/O model generated 700 Pareto-optimal solutions. Analyzing a large set of Pareto-optimal solutions is a challenging task for the decision maker. Hence, the k-means clustering technique is utilized to reduce the larger original Pareto-optimal solution set for solving the large-scale saltwater intrusion management problem in Bonriki.

Get full access to this article

View all available purchase options and get full access to this article.

Acknowledgments

The authors are grateful to the Bonriki Inundation Vulnerability Assessment (BIVA) project reports developed by the Secretariat of the Pacific Community (SPC) in partnership with the Government of Kiribati under the Australian Government Pacific Australia Climate Change Science and Adaptation Planning Program (PACCSAP). Three reviewers of the original manuscript and the associate editor of JWRPM provided invaluable constructive and detailed comments, which helped us improve the manuscript, and provided an opportunity to clarify various related issues. The authors would like to acknowledge their inputs.

References

Aguirre, O., and H. Taboada. 2011. “A clustering method based on dynamic self organizing trees for post-Pareto optimality analysis.” Procedia Comput. Sci. 6: 195–200. https://doi.org/10.1016/j.procs.2011.08.037.
Akhtar, T., and C. A. Shoemaker. 2016. “Multi objective optimization of computationally expensive multi-modal functions with RBF surrogates and multi-rule selection.” J. Global Optim. 64 (1): 17–32. https://doi.org/10.1007/s10898-015-0270-y.
Al-Anazi, A., and I. Gates. 2012. “Support vector regression to predict porosity and permeability: Effect of sample size.” Comput. Geosci. 39 (Feb): 64–76. https://doi.org/10.1016/j.cageo.2011.06.011.
Alexander, K., and J. West. 2011. ‘Water’-resource efficiency in Asia and the pacific. Bangkok, Thailand: United Nations Environment Programme.
Alpaydin, E. 2014. Introduction to machine learning. Cambridge, MA: MIT Press.
AquaVeo. 2011. Groundwater modelling system (GMS). Provo, UT: AquaVeo.
Ataie-Ashtiani, B., H. Ketabchi, and M. M. Rajabi. 2013. “Optimal management of a freshwater lens in a small island using surrogate models and evolutionary algorithms.” J. Hydrol. Eng. 19 (2): 339–354. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000809.
Ayers, J. F., and H. Vacher. 1986. “Hydrogeology of an atoll island: A conceptual model from detailed study of a Micronesian example.” Groundwater 24 (2): 185–198. https://doi.org/10.1111/j.1745-6584.1986.tb00994.x.
Bailey, R. T., J. Jenson, and A. Olsen. 2009. “Numerical modeling of atoll island hydrogeology.” Groundwater 47 (2): 184–196. https://doi.org/10.1111/j.1745-6584.2008.00520.x.
Bailey, R. T., J. W. Jenson, and A. E. Olsen. 2010. “Estimating the ground water resources of atoll islands.” Water 2 (1): 1–27. https://doi.org/10.3390/w2010001.
Bandyopadhyay, S., and U. Maulik. 2002. “An evolutionary technique based on K-means algorithm for optimal clustering in RN.” Inf. Sci. 146 (1): 221–237. https://doi.org/10.1016/S0020-0255(02)00208-6.
Basak, D., S. Pal, and D. C. Patranabis. 2007. “Support vector regression.” Neural Inf. Process. Lett. Rev. 11 (10): 203–224.
Bosserelle, A., D. Jakovovic, V. Post, S. G. Rodriguez, A. Werner, and P. Sinclair. 2015. Bonriki Inundation Vulnerability Assessment (BIVA): Assessment of sea-level rise and inundation effects on Bonriki Freshwater Lens, Tarawa Kiribati—Groundwater Modelling report. Fiji: Secretariat of the Pacific Community (SPC).
Branke, J., and K. Deb. 2005. “Integrating user preferences into evolutionary multi-objective optimization.” In Knowledge incorporation in evolutionary computation, 461–477. Heidelberg: Springer.
Bui, L. T., and S. Alam. 2008. “An introduction to multi-objective optimization.” In Multi-objective optimization in computational intelligence: Theory and practice, 1–19. Hershey PA: Information Science Reference.
Byvatov, E., U. Fechner, J. Sadowski, and G. Schneider. 2003. “Comparison of support vector machine and artificial neural network systems for drug/nondrug classification.” J. Chem. Inf. Comput. Sci. 43 (6): 1882–1889. https://doi.org/10.1021/ci0341161.
Chaudhari, P., R. Dharaskar, and V. Thakare. 2010. “Computing the most significant solution from Pareto front obtained in multi-objective evolutionary.” Int. J. Adv. Comput. Sci. Appl. 1 (4): 63–68.
Cheikh, M., B. Jarboui, T. Loukil, and P. Siarry. 2010. “A method for selecting Pareto optimal solutions in multiobjective optimization.” J. Inf. Math. Sci. 2 (1): 51–62.
Chevalier, R. F., G. Hoogenboom, R. W. McClendon, and J. A. Paz. 2011. “Support vector regression with reduced training sets for air temperature prediction: A comparison with artificial neural networks.” Neural Comput. Appl. 20 (1): 151–159. https://doi.org/10.1007/s00521-010-0363-y.
Coello, C. A. C., G. B. Lamont, and D. A. Van Veldhuizen. 2007. Evolutionary algorithms for solving multi-objective problems. New York: Springer.
Cui, X., P. Zhu, X. Yang, K. Li, and C. Ji. 2014. “Optimized big data K-means clustering using MapReduce.” J. Supercomputing 70 (3): 1249–1259. https://doi.org/10.1007/s11227-014-1225-7.
Das, A., and B. Datta. 1999. “Development of multiobjective management models for coastal aquifers.” J. Water Resour. Plann. Manage. 125 (2): 76–87. https://doi.org/10.1061/(ASCE)0733-9496(1999)125:2(76).
Datta, B., and R. C. Peralta. 1986. “Interactive computer graphics-based multiobjective decision-making for regional groundwater management.” Agric. Water Manage. 11 (2): 91–116.
Deb, K. 2001. Multi-objective optimization using evolutionary algorithms. Chichester, UK: Wiley.
Deb, K., A. Pratap, S. Agarwal, and T. Meyarivan. 2002. “A fast and elitist multiobjective genetic algorithm: NSGA-II.” IEE Trans. Evol. Comput. 6 (2): 182–197. https://doi.org/10.1109/4235.996017.
Dhar, A., and B. Datta. 2009. “Saltwater intrusion management of coastal aquifers. I: Linked simulation-optimization.” J. Hydrol. Eng. 14 (12): 1263–1272. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000097.
Duvat, V., A. Magnan, and F. Pouget. 2013. “Exposure of atoll population to coastal erosion and flooding: A South Tarawa assessment, Kiribati.” Sustainability Sci. 8 (3): 423–440. https://doi.org/10.1007/s11625-013-0215-7.
Falkland, A. 1992. Review of Tarawa freshwater lenses, Republic of Kiribati. Canberra, Australia: Hydrology and Water Resources Branch, ACT Electricity and Water.
Falkland, A., and E. Custodio. 1991. Hydrology and water resources of small islands: A practical guide. Paris: UNESCO.
Geiger, M. J. 2006. “Solving multi-objective scheduling problems-An integrated systems approach.” In Proc., IFIP Int. Conf. on Artificial Intelligence in Theory and Practice. Boston: Springer.
Ghassemi, F., A. Jakeman, and G. Jacobson. 1990. “Mathematical modelling of sea water intrusion, Nauru Island.” Hydrol. Process. 4 (3): 269–281. https://doi.org/10.1002/hyp.3360040307.
Ghassemi, F., A. Jakeman, G. Jacobson, and K. Howard. 1996. “Simulation of seawater intrusion with 2D and 3D models: Nauru Island case study.” Hydrogeol. J. 4 (3): 4–22. https://doi.org/10.1007/s100400050251.
Gizaw, M. S., and T. Y. Gan. 2016. “Regional flood frequency analysis using support vector regression under historical and future climate.” J. Hydrol. 538 (Jul): 387–398. https://doi.org/10.1016/j.jhydrol.2016.04.041.
He, Z., X. Wen, H. Liu, and J. Du. 2014. “A comparative study of artificial neural network, adaptive neuro fuzzy inference system and support vector machine for forecasting river flow in the semiarid mountain region.” J. Hydrol. 509 (Feb): 379–386. https://doi.org/10.1016/j.jhydrol.2013.11.054.
Holding, S., D. Allen, S. Foster, A. Hsieh, I. Larocque, J. Klassen, and S. Van Pelt. 2016. “Groundwater vulnerability on small islands.” Nat. Clim. Change 6 (12): 1100–1103. https://doi.org/10.1038/nclimate3128.
Hussain, M. S., A. A. Javadi, A. Ahangar-Asr, and R. Farmani. 2015. “A surrogate model for simulation–optimization of aquifer systems subjected to seawater intrusion.” J. Hydrol. 523 (Apr): 542–554. https://doi.org/10.1016/j.jhydrol.2015.01.079.
Jafari, S., P. Khalaf, and M. Montazeri-Gh. 2014. “Multi-objective meta heuristic optimization algorithm with multi criteria decision making strategy for aero-engine controller design.” Int. J. Aerosp. Sci. 3 (1): 6–17.
Kallioras, A., F.-K. Pliakas, C. Schuth, and R. Rausch. 2013. “Methods to countermeasure the intrusion of seawater into coastal aquifer systems.” In Wastewater reuse and management, 479–490. Dordrecht, Netherlands: Springer.
Kao, C. 2010. “Weight determination for consistently ranking alternatives in multiple criteria decision analysis.” Appl. Math. Modell. 34 (7): 1779–1787. https://doi.org/10.1016/j.apm.2009.09.022.
Ketabchi, H., and B. Ataie-Ashtiani. 2015. “Evolutionary algorithms for the optimal management of coastal groundwater: A comparative study toward future challenges.” J. Hydrol. 520 (Jan): 193–213. https://doi.org/10.1016/j.jhydrol.2014.11.043.
Khadri, S., and C. Pande. 2016. “Ground water flow modeling for calibrating steady state using MODFLOW software: A case study of Mahesh River basin, India.” Model. Earth Syst. and Environ. 2 (1): 39. https://doi.org/10.1007/s40808-015-0049-7.
Kodinariya, T. M., and P. R. Makwana. 2013. “Review on determining number of cluster in K-means clustering.” Int. J. 1 (6): 90–95.
Kourakos, G., and A. Mantoglou. 2009. “Pumping optimization of coastal aquifers based on evolutionary algorithms and surrogate modular neural network models.” Adv. Water Resour. 32 (4): 507–521. https://doi.org/10.1016/j.advwatres.2009.01.001.
Kourakos, G., and A. Mantoglou. 2013. “Development of a multi-objective optimization algorithm using surrogate models for coastal aquifer management.” J. Hydrol. 479 (Feb): 13–23. https://doi.org/10.1016/j.jhydrol.2012.10.050.
Lal, A., and B. Datta. 2018a. “Development and implementation of support vector machine regression surrogate models for predicting groundwater pumping-induced saltwater intrusion into coastal aquifers.” Water Resour. Manage. 32 (7): 2405–2419. https://doi.org/10.1007/s11269-018-1936-2.
Lal, A., and B. Datta. 2018b. “Modelling saltwater intrusion processes and development of a multi-objective strategy for management of coastal aquifers utilizing planned artificial freshwater recharge.” Model. Earth Syst. Environ. 4 (1): 111–126. https://doi.org/10.1007/s40808-017-0405-x.
Lin, H.-C. J., D. R. Richards, G.-T. Yeh, J.-R. Cheng, and H.-P. Cheng. 1997. FEMWATER: A three-dimensional finite element computer model for simulating density-dependent flow and transport in variably saturated media, 39180–6199. Vicksburg, MS: DTIC Document.
Liong, S., A. Tariq, and K. Lee. 2004. “Application of evolutionary algorithm in reservoir operations.” J. Inst. Eng. Singapore 44 (1): 39–53.
Liu, S., H. Tai, Q. Ding, D. Li, L. Xu, and Y. Wei. 2013. “A hybrid approach of support vector regression with genetic algorithm optimization for aquaculture water quality prediction.” Math. Comput. Modell. 58 (3): 458–465. https://doi.org/10.1016/j.mcm.2011.11.021.
Mattson, C. A., A. A. Mullur, and A. Messac. 2004. “Smart Pareto filter: Obtaining a minimal representation of multiobjective design space.” Eng. Optim. 36 (6): 721–740. https://doi.org/10.1080/0305215042000274942.
McKay, M. D., R. J. Beckman, and W. J. Conover. 1979. “Comparison of three methods for selecting values of input variables in the analysis of output from a computer code.” Technometrics 21 (2): 239–245.
Metai, E. 2002.“Vulnerability of freshwater lens on Tarawa–the role of hydrological monitoring in determining sustainable yield.” In Proc., Case Study Presented as Part of Theme. Tarawa, Kiribati: Ministry of Works and Energy.
Oberdorfer, J. A., P. J. Hogan, and R. W. Buddemeier. 1990. “Atoll island hydrogeology: Flow and freshwater occurrence in a tidally dominated system.” J. Hydrol. 120 (1–4): 327–340. https://doi.org/10.1016/0022-1694(90)90157-S.
Papadopoulou, M. P., I. K. Nikolos, and G. P. Karatzas. 2010. “Computational benefits using artificial intelligent methodologies for the solution of an environmental design problem: Saltwater intrusion.” Water Sci. Technol. 62 (7): 1479–1490. https://doi.org/10.2166/wst.2010.442.
Park, N., and L. Shi. 2015. “A comprehensive sharp-interface simulation-optimization model for fresh and saline groundwater management in coastal areas.” Hydrogeol. J. 23 (6): 1195–1204. https://doi.org/10.1007/s10040-015-1268-8.
Pawar, P. J., U. S. Vidhate, and M. Y. Khalkar. 2018. “Improving the quality characteristics of abrasive water jet machining of marble material using multi-objective artificial bee colony algorithm.” J. Comput. Des. Eng. 5 (3): 319–328.
Rajabi, M. M., and H. Ketabchi. 2017. “Uncertainty-based simulation-optimization using Gaussian process emulation: Application to coastal groundwater management.” J. Hydrol. 555 (Dec): 518–534. https://doi.org/10.1016/j.jhydrol.2017.10.041.
Razavi, S., B. A. Tolson, and D. H. Burn. 2012. “Review of surrogate modeling in water resources.” Water Resour. Res. 48 (7): W07401. https://doi.org/10.1029/2011WR011527.
Roy, D. K., and B. Datta. 2017a. “Fuzzy C-mean clustering based inference system for saltwater intrusion processes prediction in coastal aquifers.” Water Resour. Manage. 31 (1): 355–376. https://doi.org/10.1007/s11269-016-1531-3.
Roy, D. K., and B. Datta. 2017b. “Multivariate adaptive regression spline ensembles for management of multilayered coastal aquifers.” J. Hydrol. Eng. 22 (9): 04017031. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001550.
Shu, C., and T. Ouarda. 2008. “Regional flood frequency analysis at ungauged sites using the adaptive neuro-fuzzy inference system.” J. Hydrol. 349 (1): 31–43. https://doi.org/10.1016/j.jhydrol.2007.10.050.
Sinclair, P., A. Singh, J. Leze, A. Bosserelle, A. Loco, M. Mataio, E. Bwatio, and S. G. Rodriguez. 2015. Bonriki inundation vulnerability assessment: Groundwater Field investigations bonriki water reserve, South Tarawa, Kiribati. Fiji: Secretariat of the Pacific Community (SPC).
Smola, A. J., and B. Schölkopf. 2004. “A tutorial on support vector regression.” Stat. and Comput. 14 (3): 199–222. https://doi.org/10.1023/B:STCO.0000035301.49549.88.
Sreekanth, J., and B. Datta. 2010. “Multi-objective management of saltwater intrusion in coastal aquifers using genetic programming and modular neural network based surrogate models.” J. Hydrol. 393 (3): 245–256. https://doi.org/10.1016/j.jhydrol.2010.08.023.
Sreekanth, J., and B. Datta. 2014. “Stochastic and robust multi-objective optimal management of pumping from coastal aquifers under parameter uncertainty.” Water Resour. Manage. 28 (7): 2005–2019. https://doi.org/10.1007/s11269-014-0591-5.
Storey, D., and S. Hunter. 2010. “Kiribati: An environmental ‘perfect storm’.” Aust. Geogr. 41 (2): 167–181. https://doi.org/10.1080/00049181003742294.
Suganyadevi, M., and C. Babulal. 2014. “Support vector regression model for the prediction of loadability margin of a power system.” Appl. Soft Comput. 24 (Nov): 304–315. https://doi.org/10.1016/j.asoc.2014.07.015.
Taboada, H. A., and D. W. Coit. 2007. “Data clustering of solutions for multiple objective system reliability optimization problems.” Qual. Technol. Quant. Manage. 4 (2): 191–210. https://doi.org/10.1080/16843703.2007.11673145.
Taboada, H. A., and D. W. Coit. 2008. “Multi-objective scheduling problems: Determination of pruned Pareto sets.” IIE Trans. 40 (5): 552–564. https://doi.org/10.1080/07408170701781951.
Taylor, R. 1990. “Interpretation of the correlation coefficient: A basic review.” J. Diagn. Med. Sonography 6 (1): 35–39. https://doi.org/10.1177/875647939000600106.
Terry, J. P., T. F. M. Chui, and A. Falkland. 2013. “Atoll groundwater resources at risk: Combining field observations and model simulations of saline intrusion following storm-generated sea flooding.” In Groundwater in the coastal zones of Asia-Pacific, 247–270. New York: Springer.
Todd, D. K. 1974. “Salt-water intrusion and its control.” J. Am. Water Works Assoc. 66 (3): 180–187. https://doi.org/10.1002/j.1551-8833.1974.tb01999.x.
Vapnik, V., S. E. Golowich, and A. J. Smola. 1997. “Support vector method for function approximation, regression estimation and signal processing.” In Proc., Neural Information Processing Systems 1996, 281–287. Cambridge: MIT Press.
Wang, C., Q. Duan, W. Gong, A. Ye, Z. Di, and C. Miao. 2014. “An evaluation of adaptive surrogate modeling based optimization with two benchmark problems.” Environ. Modell. Software 60 (Oct): 167–179.https://doi.org/10.1016/j.envsoft.2014.05.026.
Wang, Z., and G. P. Rangaiah. 2017. “Application and analysis of methods for selecting an optimal solution from the pareto-optimal front obtained by multiobjective optimization.” Ind. Eng. Chem. Res. 56 (2): 560–574. https://doi.org/10.1021/acs.iecr.6b03453.
Wen, Y., C. Cai, X. Liu, J. Pei, X. Zhu, and T. Xiao. 2009. “Corrosion rate prediction of 3C steel under different seawater environment by using support vector regression.” Corros. Sci. 51 (2): 349–355. https://doi.org/10.1016/j.corsci.2008.10.038.
White, I., and T. Falkland. 2010. “Management of freshwater lenses on small Pacific islands.” Hydrogeol. J. 18 (1): 227–246. https://doi.org/10.1007/s10040-009-0525-0.
White, I., T. Falkland, L. Crennan, P. Jones, T. Metutera, B. Etuati, and E. Metai. 1999. “Groundwater recharge in low coral island Bonriki, South Tarawa, Republic of Kiribati: Issues, traditions and conflicts in groundwater use and management.” In Technical documents in hydrology. Paris: UNESCO.
White, I., T. Falkland, and M. Rebgetz. 2008. “Report on the protection and management of water reserves, South Tarawa.” In Kiribati adaptation project phase. South Tarawa, Kiribati: Kiribati Adaptation Project.
Wu, C.-H., J.-M. Ho, and D.-T. Lee. 2004. “Travel-time prediction with support vector regression.” IEE Trans. Intell. Transp. Syst. 5 (4): 276–281. https://doi.org/10.1109/TITS.2004.837813.
Xia, Z., K. Mao, S. Wei, X. Wang, Y. Fang, and S. Yang. 2017. “Application of genetic algorithm-support vector regression model to predict damping of cantilever beam with particle damper.” J. Low Freq. Noise, Vib. Act. Control 36 (2): 138–147.
Yadollahi, M., M. Z. Abd Majid, and R. Mohamad Zin. 2015. “Post-Pareto optimality approach to enhance budget allocation process for bridge rehabilitation management.” Struct. Infrastruct. Eng. 11 (12): 1565–1582. https://doi.org/10.1080/15732479.2014.980833.
Yoon, H., S.-C. Jun, Y. Hyun, G.-O. Bae, and K.-K. Lee. 2011. “A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer.” J. Hydrol. 396 (1): 128–138. https://doi.org/10.1016/j.jhydrol.2010.11.002.
Yu, P.-S., S.-T. Chen, and I.-F. Chang. 2006. “Support vector regression for real-time flood stage forecasting.” J. Hydrol. 328 (3): 704–716. https://doi.org/10.1016/j.jhydrol.2006.01.021.
Yu, S., S. Zheng, S. Gao, and J. Yang. 2017. “A multi-objective decision model for investment in energy savings and emission reductions in coal mining.” Eur. J. Oper. Res. 260 (1): 335–347. https://doi.org/10.1016/j.ejor.2016.12.023.
Zhang, G., and H. Ge. 2012. “Prediction of xylanase optimal temperature by support vector regression.” Electron. J. Biotechnol. 15 (1): 7. https://doi.org/10.2225/vol15-issue1-fulltext-8.
Zhong, Z.-D., X.-J. Zhu, and G.-Y. Cao. 2006. “Modeling a PEMFC by a support vector machine.” J. Power Sources 160 (1): 293–298. https://doi.org/10.1016/j.jpowsour.2006.01.040.
Zio, E., and R. Bazzo. 2010. “Multiobjective optimization of the inspection intervals of a nuclear safety system: A clustering-based framework for reducing the Pareto Front.” Ann. Nucl. Energy 37 (6): 798–812. https://doi.org/10.1016/j.anucene.2010.02.020.

Information & Authors

Information

Published In

Go to Journal of Water Resources Planning and Management
Journal of Water Resources Planning and Management
Volume 145Issue 9September 2019

History

Received: May 11, 2018
Accepted: Jan 24, 2019
Published online: Jun 27, 2019
Published in print: Sep 1, 2019
Discussion open until: Nov 27, 2019

Permissions

Request permissions for this article.

Authors

Affiliations

Alvin Lal, S.M.ASCE [email protected]
Ph.D. Candidate, Discipline of Civil Engineering, College of Science and Engineering, James Cook Univ., Townsville, QLD 4811, Australia (corresponding author). Email: [email protected]
Bithin Datta, Ph.D. [email protected]
Senior Lecturer, Discipline of Civil Engineering, College of Science and Engineering, James Cook Univ., Townsville, QLD 4811, Australia. Email: [email protected]

Metrics & Citations

Metrics

Citations

Download citation

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.

Cited by

View Options

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Media

Figures

Other

Tables

Share

Share

Copy the content Link

Share with email

Email a colleague

Share