Istrazivanja i projektovanja za privreduJournal of Applied Engineering Science


DOI: 10.5937/jaes0-43947 
This is an open access article distributed under the CC BY 4.0
Creative Commons License

Volume 22 article 1165 pages: 29-37

Civil Engineering Department, Faculty of Engineering, Bina Nusantara University, Jakarta, Indonesia; Professional Engineering Program, Faculty of Engineering, Bina Nusantara University, Jakarta, Indonesia

Sofia Alisjahbana
Civil Engineering, Faculty of Engineering and Computer Science, Bakrie University, Jakarta, Indonesia

Yureana Wijayanti
Civil Engineering Department, Faculty of Engineering, Bina Nusantara University, Jakarta, Indonesia

Dadang Mohamad Ma’soem
Ma’soem University, Bandung, Indonesia

Oki Setyandito
Civil Engineering Department, Faculty of Engineering, Bina Nusantara University, Jakarta, Indonesia

Potential risks to people exist if a dam collapses and has a significant impact on the downstream area. Many countries are now facing the problem of having to deal with deteriorated infrastructure due to a lack of maintenance budgeting. This paper presents the dominant parameter in an Operational and Maintenance (OM) dam to build a cost estimation model to maintain the service life of the dam. The method used to identify cost-triggering parameters is based on activity-based costing and dam performance assessments using a combination of the modified Andersen, International Commission of Large Dams (ICOLD), and Dam Commission. The parameter was collected from fourteen independent variables, namely: dam height, irrigation area, sedimentation volume, grass area, wood vegetation area, corrosion area, concrete area, daily worker, corrosion expert, concrete deterioration, hydromechanical, physical performance, operation performance, and safety performance. The results of the model indicate that height, wood vegetation area, concrete maintenance area, hydromechanical deterioration, and safety performance are variables that affect OM costs. The OM costs can be reduced if the safety performance variable increases. This condition implies that if the safety performance component consists of dam monitoring activities, periodic inspections, green belt maintenance, water quality maintenance, and public awareness, the OM costs will decrease by 10%.

View article

1.     Okello C, Tomasello B, Greggio N, Wambiji N, Antonellini M. Impact of population growth and climate change on the freshwater resources of Lamu Island, Kenya. Water (Switzerland). 2015;7(3):1264–90.

2.      Liu S, Wang N, Xie J, Jiang R, Zhao M. Optimal scale of urbanization with scarcewater resources: A case study in an Arid and Semi-Arid Area of China. Water (Switzerland). 2018 Nov 8;10(11).

3.      El Gohary R. Water quality mathematical modelling for drainage water in rural communities. Journal of Applied Engineering Science. 2021;19(3):712–30. DOI:10.5937/jaes0-29770.

4.      Wijayanti Y, Anda M, Safitri L, Tarmadja S, Juliastuti, Setyandito O. Water-energy nexus development for sustainable water management in Indonesia. IOP Conf Ser Earth Environ Sci. 2020;426:12058. DOI:10.1088/1755-1315/426/1/012058.

5.      Made N, Budi N, Sudiajeng L, Made Tapayasa I. Perspective of Population Growth and Clean Water Supply in Denpasar, Bali-Indonesia. 2021.

6.      Juliastuti, Alisjahbana S, Ma’soem D, Setyandito O, Suangga M. Dam failure model to predict inundation hazard map for emergency plan. Int J Eng Adv Technol. 2019;9(1):249–55. DOI:10.1063/1.5011615.

7.      Annys S, Van Passel S, Dessein J, Adgo E, Nyssen J. From fast-track implementation to livelihood deterioration: The dam based Ribb Irrigation and Drainage Project in Northwest Ethiopia. Agric Syst. 2020 Sep;184:102909.

8.      Brewitt PK, Colwyn CLM. Little dams, big problems: The legal and policy issues of nonjurisdictional dams. WIREs Water. 2020 Jan 25;7(1):e1393.

9.      Malik Sadat Idris A, Christian Permadi AS, Merlin Sianturi U, Astrianty Hazet F. Strategic Issues in Dam Operation and Maintenance in Indonesia. Jurnal Perencanaan Pembangunan: The Indonesian Journal of Development Planning. 2019 Sep 17;3(2).

10.   Yudianto D, Ginting BM, Sanjaya S, Rusli SR, Wicaksono A. A Framework of Dam-Break Hazard Risk Mapping for a Data-Sparse Region in Indonesia. ISPRS Int J Geoinf. 2021 Feb 26;10(3):110.

11.   Ara Z, Zakwan M. Reservoir sedimentation analysis: A case study. In: Proceedings of the 5th National Conference on Water, Environment \& Society (NCWES-2018), JNTU, Hyderabad, India. 2018. p. 4–6.

12.   Augusto E, Ikhsan C, Hadiani R. The Assessment of Physical Condition of Delingan Dam in 2019 as an Evaluation on Dam Maintenance. IOP Conf Ser Mater Sci Eng. 2020 Jun 1;858(1):012003.

13.   Yun S. Performance Analysis of Construction Cost Prediction Using Neural Network for Multioutput Regression. Applied Sciences (Switzerland). 2022 Oct 1;12(19).

14.   Hastak M, Association for the Advancement of Cost Engineering International. Skills and Knowledge of Cost Engineering 6th Edition. International A for the A of CE, editor. CreateSpace Independent Publishing Platform; 2015.

15.   Doğan NB, Ayhan BU, Kazar G, Saygili M, Ayözen YE, Tokdemir OB. Predicting the Cost Outcome of Construction Quality Problems Using Case-Based Reasoning (CBR). Buildings. 2022 Nov 1;12(11).

16.   Altavilla S, Montagna F, Cantamessa M. A Multilayer Taxonomy of Cost Estimation Techniques, Looking at the Whole Product Lifecycle. J Manuf Sci Eng. 2018 Mar 1;140(3).

17.   Meshref A, El-Dash K, Basiouny M, El-Hadidi O. Implementation of a Life Cycle Cost Deep Learning Prediction Model Based on Building Structure Alternatives for Industrial Buildings. Buildings. 2022 May 1;12(5).

18.   Woldemariam W, Murillo-Hoyos J, Labi S. Estimating Annual Maintenance Expenditures for Infrastructure: Artificial Neural Network Approach. Journal of Infrastructure Systems. 2016 Jun;22(2).

19.   Haroun AE. Maintenance cost estimation: application of activity-based costing as a fair estimate method. J Qual Maint Eng. 2015;21(3):258–70. DOI:10.1108/JQME-04-2015-0015.

20.   Ministry of Public Work and Housing. Regulation No. 27 on Dam. 2015.

21.   American Society on Civil Engineering A. Manuals and Reports on Engineering Practice No. 135. 2018.

22.   Ministry of Jal Shakti Department of Water Resources RD& GRG of I. Guidelines for Instrumentation of Large Dams Manual for Assessing Hydraulic Safety of Existing Dams-Volume II Central Water Commission. 2016.

23.   Washington State Department of Ecology. Guidelines for Developing Dam Operation and Maintenance Manuals [Internet]. 2022. Available from:

24.   Dam Engineering Center. Dam Operation and Maintenance Guidelines. Jakarta: The Ministry of Public Works and Housing; 2003.

25.   The Pennsylvania Department of Environmental Protection Division of Dam Safety. Operation and Maintenance of Dam manual.

26.   Ministry of Public Works and Housing. Regulation Number 27 / PRT / M / 2015. Ministry of Public Work and Housing. 2015.

27.   Zielinski PA, Narayan P, Donnelly CR, Halpin E, Quebbeman J, Qaddumi HM, et al. The development of a risk screening indexing tool for prioritizing dam safety remedial works. In: E3S Web of Conferences. EDP Sciences; 2022.

28.   Amekudzi AA, Shelton R, Bricker TR. Infrastructure Rating Tool: Using Decision Support Tools to Enhance ASCE Infrastructure Report Card Process. Leadership and Management in Engineering. 2013;13(2):76–82.

29.   Sadeghi JM, Askarinejad H. Development of track condition assessment model based on visual inspection. Structure and Infrastructure Engineering. 2011;7(12):895–905.

30.   ICOLD. Selecting Seismic Parameters for Large Dam. Bulletin 7. Paris; 1989.

31.   Andersen GR, Chouinard LE, Hover WH, Cox CW. Risk indexing tool to assist in prioritizing improvements to embankment dam inventories. Journal of geotechnical and geoenvironmental engineering. 2001;127(4):325–34. DOI:10.1061/(ASCE)1090-0241(2001)127:4(325).

32.   Andersen GR, Cox CW, Chouinard LE, Hover WH. Prioritization of ten embankment dams according to physical deficiencies. Journal of geotechnical and geoenvironmental engineering. 2001;127(4):335–45. DOI:10.1061/(ASCE)1090-0241(2001)127:4(335).

33.   Hong K, Wang H, Yuan B, Wang T. Multiple Defects Inspection of Dam Spillway Surface Using Deep Learning and 3D Reconstruction Techniques. Buildings. 2023 Jan 18;13(2):285.

34.   Ministry of Public Work and Housing. Ministry Regulation No.8 on Guidelines for Analysis of Unit Prices for Public Works. 2023.

35.   Ministry of Public Work and Housing. Regulation No. 28 on Guidelines for Analysis of Unit Prices. 2016

36.   Trafimow D. A Frequentist Alternative to Significance Testing, p-Values, and Confidence Intervals. Econometrics. 2019 Jun 4;7(2):26.

37.   Kumar S, Chong I. Correlation Analysis to Identify the Effective Data in Machine Learning: Prediction of Depressive Disorder and Emotion States. Int J Environ Res Public Health. 2018 Dec 19;15(12):2907.

38.   Chan JYL, Leow SMH, Bea KT, Cheng WK, Phoong SW, Hong ZW, et al. Mitigating the Multicollinearity Problem and Its Machine Learning Approach: A Review. Mathematics. 2022 Apr 12;10(8):1283.

39.   Soentoro EA, Purnomo AB, Sri &, Susantin H. The Second International Conference on Sustainable Infrastructure and Built Environment-Bandung Study on Dam Risk Assessment as a Decision-Making Tool to Assist Prioritizing Maintenance of Embankment Dam in Indonesia. 2013