Integrating Life Cycle Cost Analysis into Pipeline Asset Integrity Management: A Comprehensive Approach in Decision Support Systems
Abstract
Pipelines serve as the foundation of the global oil and gas industry and play an essential role in transporting hydrocarbons over long distances. Ensuring the continuous and dependable operation of these pipelines is vital not only for meeting global energy needs but also for protecting the environment and the communities through which they pass. This paper emphasizes the importance of proactively managing the integrity of pipeline assets to prevent catastrophic incidents resulting from pipeline failures. Traditionally, the management of pipeline assets has been reactive, addressing problems only when they manifest as failures. Although this reactive approach can offer short-term fixes, it often leads to operational inefficiencies, increased expenses, unexpected downtime, and safety compromises. Aging pipeline infrastructure compounds these challenges, underscoring the need for a forward-looking strategy focused on the entire lifecycle of pipelines. To tackle these issues and revolutionize asset integrity management practices, life cycle cost analysis-based decision support systems (DSSs) have emerged as an innovative solution. An LCCA-based DSS integrates advanced data analytics, risk assessment techniques, and optimization algorithms to facilitate proactive decision-making that accounts for the full pipeline lifecycle. Thereby, construction, operation, maintenance, and decommissioning costs are considered. This review article delves into the historical context and the crucial role of managing pipeline asset integrity while highlighting the drawbacks of conventional asset management methods. It introduces LCCA-based DSS as a transformative approach that bridges the gap between asset integrity management and comprehensive financial considerations. By shedding light on the potential advantages and practical implications of an LCCA-based DSS, this review advances pipeline management practices. Ultimately, the adoption of an LCCA-based DSS holds the promise of enhancing safety, environmental protection, and economic efficiency in the energy sector, ensuring a sustainable and dependable future for pipeline infrastructure.
Keywords: pipeline, life cycle cost analysis, decision support system, asset management.
Full Text:
PDFReferences
MARFO S., APPAU P. O., and KPAMI L. Subsea Pipeline Design for Natural Gas Transportation: A Case Study of Côte D’ivoire’s Gazelle Field. International Journal of Petroleum and Petrochemical Engineering, 2018, 4(3): 21-34. http://dx.doi.org/10.20431/2454-7980.0403003
DORIAN J. P., FRANSSEN H. T., and SIMBECK D. R. Global challenges in energy. Energy Policy, 2006, 34(15): 1984-1991. https://doi.org/10.1016/j.enpol.2005.03.010
UGARELLI R., & SÆGROV S. Infrastructure asset management: historic and future perspective for tools, risk assessment, and digitalization for competence building. Water, 2022, 14(8): 1236. https://doi.org/10.3390/w14081236
KISHAWY H. A., & GABBAR H. A. Review of pipeline integrity management practices. International Journal of Pressure Vessels and Piping, 2010, 87(7): 373-380. https://doi.org/10.1016/j.ijpvp.2010.04.003
COSTELLO S., CHAPMAN D., ROGERS C., and METJE N. Underground asset location and condition assessment technologies. Tunnelling and Underground Space Technology, 2007, 22(5-6): 524-542. https://doi.org/10.1016/j.tust.2007.06.001
UDDIN W., HUDSON W. R., and HAAS R. Public infrastructure asset management. McGraw-Hill Education, New York, 2013.
FODCHUK M., & GARACI M. Asset Management Best Practices: Keeping Ahead of the Curve. Journal - American Water Works Association, 2019, 111(8): 46–55. https://doi.org/10.1002/awwa.1342
MUÑOZ E., CAPON-GARCIA E., MUÑOZ E. M., and PUIGJANER L. A systematic model for process development activities to support process intelligence. Processes, 2021, 9(4): 600. https://doi.org/10.3390/pr9040600
CHANDIMA RATNAYAKE R., & MARKESET T. Asset integrity management for sustainable industrial operations: measuring the performance. International Journal of Sustainable Engineering, 2012, 5(2): 145-158. https://doi.org/10.1080/19397038.2011.581391
CHANDIMA RATNAYAKE R., & MARKESET T. Technical integrity management: measuring HSE awareness using AHP in selecting a maintenance strategy. Journal of Quality in Maintenance Engineering, 2010, 16(1): 44-63. https://doi.org/10.1108/13552511011030327
ENDRENYI J., ABORESHEID S., ALLAN R. N., ANDERS G. J., ASGARPOOR S., BILLINTON R., CHOWDHURY N., DIALYNAS E. N., FIPPER M., FLETCHER R. H., GRIGG C., MCCALLEY J., MELIOPOULOS S., MIELNIK T. C., NITU P., RAU N., REPPEN N. D., SALVADERI L., SCHNEIDER A., and SINGH C. The present status of maintenance strategies and the impact of maintenance on reliability. IEEE Transactions on Power Systems, 2001, 16(4): 638-646. https://doi.org/10.1109/59.962408
ACHEBE C., NNEKE U., and ANISIJI O. Analysis of Oil Pipeline Failures in the Oil and Gas Industries in the Niger Delta Area of Nigeria. Proceedings of the International MultiConference of Engineers and Computer Scientists, 2012, II. https://www.researchgate.net/publication/280156861_Analysis_of_Oil_Pipeline_Failures_in_the_Oil_and_Gas_Industries_in_the_Niger_Delta_Area_of_Nigeria
WANG H., & PHAM H. Reliability and optimal maintenance. Springer, London, 2006. https://doi.org/10.1007/b138077
ALAWODE A. J., & OGUNLEYE I. O. Maintenance, Security and Environmental Implications of Pipeline Damage and Ruptures in the Niger Delta Region. Pacific Journal of Science and Technology, 2011, 12(1): 565-573. http://eprints.abuad.edu.ng/302/
ZARTE M., PECHMANN A., and NUNES I. L. Decision support systems for sustainable manufacturing surrounding the product and production life cycle – A literature review. Journal of Cleaner Production, 2019, 219: 336-349. https://doi.org/10.1016/j.jclepro.2019.02.092
KHAN F. I., & AMYOTTE P. R. I2SI: a comprehensive quantitative tool for inherent safety and cost evaluation. Journal of Loss Prevention in the Process Industries, 2005, 18(4-6): 310-326. https://doi.org/10.1016/j.jlp.2005.06.022
SHIELDS M. D., & YOUNG S. M. Managing product life cycle costs: an organizational model. Journal of Cost Management, 1991, 5(3): 39-52. https://www.researchgate.net/publication/313724559_Managing_product_life_cycle_costs_an_organizational_model
GHOSH C., MAITI J., SHAFIEE M., and KUMARASWAMY K. Reduction of life cycle costs for a contemporary helicopter through improvement of reliability and maintainability parameters. International Journal of Quality & Reliability Management, 2018, 35(2): 545-567. https://doi.org/10.1108/IJQRM-11-2016-0199
SMITH L. M., & CELANT M. Life Cycle Costing - Are Duplex Stainless Steel Pipelines the Cost-Effective Choice? Proceedings of the Offshore Technology Conference, Houston, Texas, 1995. https://doi.org/10.4043/7789-MS
WINKEL J. D. Use of Life Cycle Costing in New and Mature Applications. Proceedings of the European Production Operations Conference and Exhibition, Stavanger, 1996. https://doi.org/10.2118/35565-MS
PAULA M. T. R., LABANCA E. L., and CHILDS P. Subsea Manifolds Design Based on Life Cycle Cost. Proceedings of the Offshore Technology Conference, Houston, Texas, 2001. https://doi.org/10.4043/12942-MS
IWAWAKI H., KAWAUCHI Y., MURAKI M., EVANS D., and MATSUOKA S. Life Cycle Costing (LCC) Based Decision Making for Reactor Effluent Air Coolers in Refineries. Proceedings of the CORROSION 2002, Denver, Colorado, 2002. https://onepetro.org/NACECORR/proceedings-abstract/CORR02/All-CORR02/NACE-02483/114869
VORARAT S., & AL-HAJJ A. Developing a model to suit life cycle costing analysis for assets in the oil and gas industry. Proceedings of the SPE Asia Pacific Conference on Integrated Modelling for Asset Management, Kuala Lumpur, 2004. https://doi.org/10.2118/87028-MS
KAYRBEKOVA D., & MARKESET T. Economic Decision Support for Offshore Oil and Gas Production in Arctic Conditions: Identifying the Needs. Proceedings of the European Safety and Reliability Conference, 2010, pp. 5-9.
NAM K., CHANG D., CHANG K., RHEE T., and LEE I.-B. Methodology of life cycle cost with risk expenditure for offshore process at conceptual design stage. Energy, 2011, 36(3): 1554-1563. https://doi.org/10.1016/j.energy.2011.01.005
ORTIZ VOLCAN J. L., & ISKANDAR R. A. A life cycle approach for assessing production technologies in heavy oil well construction projects. Proceedings of the SPE Heavy Oil Conference and Exhibition, Kuwait, 2011. https://doi.org/10.2118/150709-MS
BURLINI P. S., & ARARUNA J. T. Life Cycle Concept (LCC) in Waste Management in the O&G Offshore Exploration. Proceedings of the North Africa Technical Conference and Exhibition, Cairo, 2013. https://doi.org/10.2118/164787-MS
WANG H., & WENG D. Life-cycle cost assessment of seismically base-isolated large tanks in liquefied natural gas plants. Journal of Pressure Vessel Technology, 2015, 137(1): 011801. https://doi.org/10.1115/1.4027461
MARTEN C., & GATZEN M. M. Decreasing operational cost of high performance oilfield services by lifecycle driven trade-offs in development. CIRP Annals, 2014, 63(1): 29-32. https://doi.org/10.1016/j.cirp.2014.03.062
AL-SAADI T., CHEREPOVITSYN A., and SEMENOVA T. Iraq oil industry infrastructure development in the conditions of the global economy turbulence. Energies, 2022, 15(17): 6239. https://doi.org/10.3390/en15176239
KINEBER A. F., MOHANDES S. R., ELBEHAIRY H., CHILESHE N., ZAYED T., and FATHY U. Towards smart and sustainable urban management: A novel value engineering decision-making model for sewer projects. Journal of Cleaner Production, 2022, 375: 134069. https://doi.org/10.1016/j.jclepro.2022.134069
DZIEDZIC R., AMADOR L., AN C., CHEN Z., EICKER U., HAMMAD A., NASIRI F., NIK-BAKHT M, OUF M., and MOSELHI O. A framework for asset management planning in sustainable and resilient cities. Proceedings of IEEE International Symposium on Technology and Society, Waterloo, 2021, pp. 1-10. https://doi.org/10.1109/ISTAS52410.2021.9629158
MOON F. L., AKTAN A. E., FURUTA H., and DOGAKI M. Governing issues and alternate resolutions for a highway transportation agency's transition to asset management. Structures & Infrastructure Engineering, 2009, 5(1): 25-39. https://doi.org/10.1080/15732470701322768
HANSKI J. Supporting strategic asset management in complex and uncertain decision contexts. Lappeenranta-Lahti University of Technology, 2019. https://lutpub.lut.fi/handle/10024/160082
ANIMAH I., SHAFIEE M., SIMMS N., ERKOYUNCU J. A., and MAITI J. Selection of the most suitable life extension strategy for ageing offshore assets using a life-cycle cost-benefit analysis approach. Journal of Quality in Maintenance Engineering, 2018, 24(3): 311-330. https://doi.org/10.1108/JQME-09-2016-0041
ADA Ş., & GHAFFARZADEH M. Decision making based on management information system and decision support system. European Researcher, 2015, 4: 260-269. https://doi.org/10.13187/er.2015.93.260
FRAZZETTO D., NIELSEN T. D., PEDERSEN T. B., and ŠIKŠNYS L. Prescriptive analytics: a survey of emerging trends and technologies. The VLDB Journal, 2019, 28: 575-595. https://doi.org/10.1007/s00778-019-00539-y
ILORA O. N. New Perspectives for an Intelligent Risk-Based Decision Support Framework for Asset Integrity Management. Means for Sustainable Asset Integrity Management in the Norwegian High-North. Master's thesis, UiT The Arctic University of Norway, 2018. https://munin.uit.no/handle/10037/15554
FOTOPOULOU M., RAKOPOULOS D., and PETRIDIS S. Decision Support System for Emergencies in Microgrids. Sensors, 2022, 22(23): 9457. https://doi.org/10.3390/s22239457
NIAN V., LIU Y., and ZHONG S. Life cycle cost-benefit analysis of offshore wind energy under the climatic conditions in Southeast Asia – Setting the bottom-line for deployment. Applied Energy, 2019, 233: 1003-1014. https://doi.org/10.1016/j.apenergy.2018.10.042
LI X., CHALVATZIS K. J., and STEPHANIDES P. Innovative energy islands: life-cycle cost-benefit analysis for battery energy storage. Sustainability, 2018, 10(10): 3371. https://doi.org/10.3390/su10103371
LAM C.-M., IRIS K., MEDEL F., TSANG D. C., HSU S.-C., and POON C. S. Life-cycle cost-benefit analysis on sustainable food waste management: The case of Hong Kong International Airport. Journal of Cleaner Production, 2018, 187: 751-762. https://doi.org/10.1016/j.jclepro.2018.03.160
NORRIS G. A. Integrating life cycle cost analysis and LCA. The International Journal of Life Cycle Assessment, 2001, 6: 118-120. https://doi.org/10.1007/BF02977849
THOFT-CHRISTENSEN P. Life-cycle cost-benefit (LCCB) analysis of bridges from a user and social point of view. Structures & Infrastructure Engineering, 2009, 5(1): 49-57. https://doi.org/10.1080/15732470701322818
SANTA-CRUZ S., & HEREDIA-ZAVONI E. Maintenance and decommissioning real options models for life-cycle cost-benefit analysis of offshore platforms. Structure and Infrastructure Engineering, 2011, 7(10): 733-745. https://doi.org/10.1080/15732470902842903
NEWELL S., & MARABELLI M. Strategic opportunities (and challenges) of algorithmic decision-making: A call for action on the long-term societal effects of ‘datification’. In: GALLIERS R. D., LEIDNER D. E., and SIMEONOVA B. (eds.) Strategic Information Management: Theory and Practice. Routledge, New York, 2020: 430-449. https://doi.org/10.4324/9780429286797-19
AQEL M. J., NAKSHABANDI O. A., and ADENIYI A. Decision support systems classification in industry. Periodicals of Engineering and Natural Sciences, 2019, 7(2): 774-785. https://doi.org/10.21533/pen.v7i2.550
LAYOUNI M., HAMDI M. S., and TAHAR S. Detection and sizing of metal-loss defects in oil and gas pipelines using pattern-adapted wavelets and machine learning. Applied Soft Computing, 2017, 52: 247-261. https://doi.org/10.1016/j.asoc.2016.10.040
MOHAMED A., HAMDI M. S., and TAHAR S. A Hybrid Intelligent Approach for Metal-Loss Defect Depth Prediction in Oil and Gas Pipelines. In: BI Y., KAPOOR S., and BHATIA R. (eds.) Intelligent Systems and Applications. Studies in Computational Intelligence, Vol. 650. Springer, Cham, 2016: 1-18. https://doi.org/10.1007/978-3-319-33386-1_1
MAO B., LU Y., WU P., MAO B., and LI P. Signal processing and defect analysis of pipeline inspection applying magnetic flux leakage methods. Intelligent Service Robotics, 2014, 7: 203-209. https://doi.org/10.1007/s11370-014-0158-6
HWANG K., MANDAYAM S., UDPA S., UDPA L., LORD W., and ATZAL M. Characterization of gas pipeline inspection signals using wavelet basis function neural networks. NDT & E International, 2000, 33(8): 531-545. https://doi.org/10.1016/S0963-8695(00)00008-6
ACCIANI G., BRUNETTI G., FORNARELLI G., and GIAQUINTO A. Angular and axial evaluation of superficial defects on non-accessible pipes by wavelet transform and neural network-based classification. Ultrasonics, 2010, 50(1): 13-25. https://doi.org/10.1016/j.ultras.2009.07.003
BETTAYEB F., RACHEDI T., and BENBARTAOUI H. An improved automated ultrasonic NDE system by wavelet and neuron networks. Ultrasonics, 2004, 42(1-9): 853-858. https://doi.org/10.1016/j.ultras.2004.01.064
SIMONE G., MORABITO F., POLIKAR R., RAMUHALLI P., UDPA L., and UDPA S. Feature extraction techniques for ultrasonic signal classification. International Journal of Applied Electromagnetics and Mechanics, 2002, 15(1-4): 291-294. http://dx.doi.org/10.3233/JAE-2002-462
CRUZ F., SIMAS FILHO E., ALBUQUERQUE M., SILVA I., FARIAS C., and GOUVÊA L. Efficient feature selection for neural network based detection of flaws in steel welded joints using ultrasound testing. Ultrasonics, 2017, 73: 1-8. https://doi.org/10.1016/j.ultras.2016.08.017
ROSADO L. S., JANEIRO F. M., RAMOS P. M., and PIEDADE M. Defect characterization with eddy current testing using nonlinear-regression feature extraction and artificial neural networks. IEEE Transactions on Instrumentation and Measurement, 2013, 62(5): 1207-1214. https://doi.org/10.1109/TIM.2012.2236729
LIU B., HOU D., HUANG P., LIU B., TANG H., ZHANG W., CHEN P., and ZHANG G. An improved PSO-SVM model for online recognition defects in eddy current testing. Nondestructive Testing and Evaluation, 2013, 28(4): 367-385. https://doi.org/10.1080/10589759.2013.823608
ZADKARAMI M., SHAHBAZIAN M., and SALAHSHOOR K. Pipeline leakage detection and isolation: An integrated approach of statistical and wavelet feature extraction with multi-layer perceptron neural network (MLPNN). Journal of Loss Prevention in the Process Industries, 2016, 43: 479-487. https://doi.org/10.1016/j.jlp.2016.06.018
SAADE M., & MUSTAPHA S. Assessment of the structural conditions in steel pipeline under various operational conditions – A machine learning approach. Measurement, 2020, 166: 108262. https://doi.org/10.1016/j.measurement.2020.108262
ABDULLA M. B., & HERZALLAH R. Probabilistic multiple model neural network based leak detection system: Experimental study. Journal of Loss Prevention in the Process Industries, 2015, 36: 30-38. https://doi.org/10.1016/j.jlp.2015.05.009
MANDAL S. K., CHAN F. T., and TIWARI M. Leak detection of pipeline: An integrated approach of rough set theory and artificial bee colony trained SVM. Expert Systems with Applications, 2012, 39(3): 3071-3080. https://doi.org/10.1016/j.eswa.2011.08.170
CHEN H., YE H., CHEN L., and SU H. Application of support vector machine learning to leak detection and location in pipelines. Proceedings of the 21st IEEE Instrumentation and Measurement Technology Conference (IEEE Cat. No.04CH37510), Como, 2004, pp. 2273-2277. https://doi.org/10.1109/IMTC.2004.1351546
FERRAZ I. N., GARCIA A. C. B., and BERNARDINI F. C. Artificial Neural Networks Ensemble Used for Pipeline Leak Detection Systems. Proceedings of the 2008 7th International Pipeline Conference, Volume 1, Calgary, 2008, pp. 739-747. https://doi.org/10.1115/IPC2008-64664
VALIZADEH S., MOSHIRI B., and SALAHSHOOR K. Leak detection in transportation pipelines using feature extraction and KNN classification. In: Pipelines 2009: Infrastructure's Hidden Assets, 2009, pp. 580-589. https://doi.org/10.1061/41069(360)53
ABDULLA M. B., HERZALLAH R. O., and HAMMAD M. A. Pipeline leak detection using artificial neural network: Experimental study. Proceedings of the 5th International Conference on Modelling, Identification and Control, Cairo, 2013, pp. 328-332. https://ieeexplore.ieee.org/document/6642219
JINHAI L., HUAGUANG Z., JIAN F., and HENG Y. A New Fault Detection and Diagnosis Method for Oil Pipeline Based on Rough Set and Neural Network. In: LIU D., FEI S., HOU Z., ZHANG H., and SUN C. (eds.) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, Vol. 4493. Springer, Berlin, Heidelberg, 2007: 561-569. https://doi.org/10.1007/978-3-540-72395-0_70
XU Q., ZHANG L., and LIANG W. Acoustic detection technology for gas pipeline leakage. Process Safety and Environmental Protection, 2013, 91(4): 253-261. https://doi.org/10.1016/j.psep.2012.05.012
QU Z., FENG H., ZENG Z., ZHUGE J., and JIN S. A SVM-based pipeline leakage detection and pre-warning system. Measurement, 2010, 43(4): 513-519. https://doi.org/10.1016/j.measurement.2009.12.022
ISA D., & RAJKUMAR R. Pipeline defect prediction using support vector machines. Applied Artificial Intelligence, 2009, 23(8): 758-771. https://doi.org/10.1080/08839510903210589
YING Y., GARRETT JR. J. H., HARLEY J., OPPENHEIM I. J., SHI J., and SOIBELMAN L. Damage detection in pipes under changing environmental conditions using embedded piezoelectric transducers and pattern recognition techniques. Journal of Pipeline Systems Engineering and Practice, 2013, 4(1): 17-23. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000106
AKRAM N. A., ISA D., RAJKUMAR R., and LEE L. H. Active incremental Support Vector Machine for oil and gas pipeline defects prediction system using long range ultrasonic transducers. Ultrasonics, 2014, 54(6): 1534-1544. https://doi.org/10.1016/j.ultras.2014.03.017
TEJEDOR J., MARTINS H. F., PIOTE D., MACIAS-GUARASA J., PASTOR-GRAELLS J., MARTIN-LOPEZ S., GUILLÉN P. C., DE SMET F., POSTVOLL W., and GONZÁLEZ-HERRÁEZ M. Toward prevention of pipeline integrity threats using a smart fiber-optic surveillance system. Journal of Lightwave Technology, 2016, 34(19): 4445-4453. https://doi.org/10.1109/JLT.2016.2542981
LIAO K., YAO Q., WU X., and JIA W. A Numerical Corrosion Rate Prediction Method for Direct Assessment of Wet Gas Gathering Pipelines Internal Corrosion. Energies, 2012, 5(10): 3892-3907. https://doi.org/10.3390/en5103892
HE S., ZOU Y., QUAN D., and WANG H. Application of RBF Neural Network and ANFIS on the Prediction of Corrosion Rate of Pipeline Steel in Soil. In: QIAN Z., CAO L., SU W., WANG T., and YANG H. (eds.) Recent Advances in Computer Science and Information Engineering. Lecture Notes in Electrical Engineering, Vol. 124. Springer, Berlin, Heidelberg, 2012: 639-644. https://doi.org/10.1007/978-3-642-25781-0_93
BASSAM A., ORTEGA-TOLEDO D., HERNANDEZ J., GONZALEZ-RODRIGUEZ J., and URUCHURTU J. Artificial neural network for the evaluation of CO2 corrosion in a pipeline steel. Journal of Solid State Electrochemistry, 2009, 13: 773-780. https://doi.org/10.1007/s10008-008-0588-1
CHAMKALANI A., NAREH'EI M. A., CHAMKALANI R., ZARGARI M. H., DEHESTANI-ARDAKANI M. R., and FARZAM M. Soft computing method for prediction of CO2 corrosion in flow lines based on neural network approach. Chemical Engineering Communications, 2013, 200(6): 731-747. https://doi.org/10.1080/00986445.2012.717311
OSSAI C. I. Corrosion defect modelling of aged pipelines with a feed-forward multi-layer neural network for leak and burst failure estimation. Engineering Failure Analysis, 2020, 110: 104397. https://doi.org/10.1016/j.engfailanal.2020.104397
OSSAI C. I. A data-driven machine learning approach for corrosion risk assessment—a comparative study. Big Data and Cognitive Computing, 2019, 3(2): 28. https://doi.org/10.3390/bdcc3020028
HOJJATI A., JEFFERSON I., METJE N., and ROGERS C. D. Sustainability assessment for urban underground utility infrastructure projects. in Proceedings of the Institution of Civil Engineers - Engineering Sustainability, 2017, 171(2): 68-80. https://doi.org/10.1680/jensu.16.00050
PERERA H. N., FAHIMNIA B., and TOKAR T. Inventory and ordering decisions: a systematic review on research driven through behavioral experiments. International Journal of Operations & Production Management, 2020, 40(7/8): 997-1039. https://doi.org/10.1108/IJOPM-05-2019-0339
DU C., DUTTA S., KURUP P., YU T., and WANG X. A review of railway infrastructure monitoring using fiber optic sensors. Sensors and Actuators A: Physical, 2020, 303: 111728. https://doi.org/10.1016/j.sna.2019.111728
CAMPBELL J. D., REYES-PICKNELL J. V., and KIM H. S. Uptime: Strategies for excellence in maintenance management. Productivity Press, New York, 2015. https://doi.org/10.1201/b18778
CHENG J. C., CHEN W., TAN Y., and WANG M. A BIM-based decision support system framework for predictive maintenance management of building facilities. Proceedings of the 16th International Conference on Computing in Civil and Building Engineering, 2016, pp. 711-718. https://www.researchgate.net/publication/316526301_A_BIM-based_Decision_Support_System_Framework_for_Predictive_Maintenance_Management_of_Building_Facilities
DABOUS S. A., & FEROZ S. Condition monitoring of bridges with non-contact testing technologies. Automation in Construction, 2020, 116: 103224. https://doi.org/10.1016/j.autcon.2020.103224
ARENA S., FLORIAN E., ZENNARO I., ORRÙ P. F., and SGARBOSSA F. A novel decision support system for managing predictive maintenance strategies based on machine learning approaches. Safety Science, 2022, 146: 105529. https://doi.org/10.1016/j.ssci.2021.105529
RUMSON A. G., HALLETT S. H., and BREWER T. R. Coastal risk adaptation: the potential role of accessible geospatial Big Data. Marine Policy, 2017, 83: 100-110. https://doi.org/10.1016/j.marpol.2017.05.032
REIDENBERG J. R., & SCHAUB F. Achieving big data privacy in education. Theory and Research in Education, 2018, 16(3): 263-279. https://doi.org/10.1177/1477878518805308
HOWARD J. Artificial intelligence: Implications for the future of work. American Journal of Industrial Medicine, 2019, 62(11): 917-926. https://doi.org/10.1002/ajim.23037
PAREEK D. Business Intelligence for Telecommunications. Auerbach Publications, New York, 2006. https://doi.org/10.1201/9780849387913
HALL W., & PESENTI J. Growing the artificial intelligence industry in the UK. Department for Science, Innovation and Technology, Department for Digital, Culture, Media & Sport, and Department for Business, Energy & Industrial Strategy, 2017. https://www.gov.uk/government/publications/growing-the-artificial-intelligence-industry-in-the-uk
OFFICE OF GOVERNMENT COMMERCE. Whole-Life Costing and Cost Management: Achieving Excellence in Construction Procurement Guide 07, 2007. https://www.sustainabilityexchange.ac.uk/files/cp0067aeguide7.pdf
Refbacks
- There are currently no refbacks.
