Recent progress in the monitoring and prediction of the condition of infrastructure using sensing technologies has motivated researchers and infrastructure owners to explore the benefits of asset predictive maintenance, as an alternative to reactive maintenance. However, the application of predictive group maintenance for multi-system multi-component networks (MSMCN) has not received much attention in the literature or in practice. The paper presents an approach that prioritizes the maintenance of MSMCN of bridges, using a deterioration model of components with uncertainty, a lifecycle cost model, a predictive model for the optimal time for maintenance based on the latest inspection, a group maintenance model to reduce setup cost, and a scheduling model considering budget constraints. This model has been applied to a network of 15 bridges constituted by multiple heterogeneous components, and, compared with the Structures Investment Toolkit, it showed potential for a substantial decrease in maintenance costs, thus highlighting the practical significance of the presented approach.
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References
1.
IvanováE.MasárováJ.Importance of Road Infrastructure in the Economic Development and Competitiveness. Economics and Management, Vol. 18, No. 2, 2013. https://doi.org/10.5755/j01.em.18.2.4253.
2.
ChanC. Y.HuangB.YanX.RichardsS.Investigating Effects of Asphalt Pavement Conditions on Traffic Accidents in Tennessee Based on the Pavement Management System (PMS). Journal of Advanced Transportation, Vol. 44, No. 3, 2010, pp. 150–161. https://doi.org/10.1002/atr.129.
FrangopolD. M.EnrightM. P.GharaibehE.EstesA. C.Towards Optimal Reliability-Based Bridge Maintenance Planning. In Case Studies in Optimal Design and Maintenance Planning of Civil Infrastructure Systems (FrangopolD. M., ed.), American Society of Civil Engineers, Reston, Va., 1999, pp. 73–88.
6.
LiuM.FrangopolD. M.Multiobjective Maintenance Planning Optimization for Deteriorating Bridges Considering Condition, Safety, and Life-Cycle Cost. Journal of Structural Engineering, Vol. 131, No. 5, 2005, pp. 833–842. https://doi.org/10.1061/(ASCE)0733-9445(2005)131:5(833).
7.
OrcesiA. D.CremonaC. F.A Bridge Network Maintenance Framework for Pareto Optimization of Stakeholders/Users Costs. Reliability Engineering & System Safety, Vol. 95, No. 11, 2010, pp. 1230–1243. https://doi.org/10.1016/j.ress.2010.06.013.
MinL.FrangopolD. M.Optimizing Bridge Network Maintenance Management under Uncertainty with Conflicting Criteria: Life-Cycle Maintenance, Failure, and User Costs. Journal of Structural Engineering, Vol. 132, No. 11, 2006, pp. 1835–1845. https://doi.org/10.1061/(ASCE)0733-9445(2006)132:11(1835).
10.
HuX.SamerM.Determination of Optimal MR&R Policies for Retaining Life-Cycle Connectivity of Bridge Networks. Journal of Infrastructure Systems, Vol. 21, No. 2, 2015, p. 04014042. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000226.
11.
ChenA.YangH.LoH. K.TangW. H.Capacity Reliability of a Road Network: An Assessment Methodology and Numerical Results. Transportation Research Part B: Methodological, Vol. 36, No. 3, 2002, pp. 225–252. https://doi.org/10.1016/S0191-2615(00)00048-5.
12.
NojimaN.Performance-Based Prioritization for Upgrading Seismic Reliability of a Transportation Network. Journal of Natural Disaster Science, Vol. 20, No. 2, 1998, pp. 57–66.
13.
HadjidemetriouG. M.ChristodoulouS. E.Vision- and Entropy-Based Detection of Distressed Areas for Integrated Pavement Condition Assessment. Journal of Computing in Civil Engineering, Vol. 33, No. 3, 2019, p. 04019020. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000836.
14.
ChuangT. Y.PerngN. H.HanJ. Y.Pavement Performance Monitoring and Anomaly Recognition Based on Crowdsourcing Spatiotemporal Data. Automation in Construction, Vol. 106, 2019, p. 102882. https://doi.org/10.1016/j.autcon.2019.102882.
15.
DaoC. D.ZuoM. J.Selective Maintenance for Multistate Series Systems with S-Dependent Components. IEEE Transactions on Reliability, Vol. 65, No. 2, 2016, pp. 525–539. https://doi.org/10.1109/TR.2015.2494689.
16.
LiuY.ChenY.JiangT.On Sequence Planning for Selective Maintenance of Multi-State Systems under Stochastic Maintenance Durations. European Journal of Operational Research, Vol. 268, No. 1, 2018, pp. 113–127. https://doi.org/10.1016/j.ejor.2017.12.036.
17.
ZhouX.LuZ.XiL.Preventive Maintenance Optimization for a Multi-Component System under Changing Job Shop Schedule. Reliability Engineering & System Safety, Vol. 101, 2012, pp. 14–20. https://doi.org/10.1016/j.ress.2012.01.005.
18.
XiaT.JinX.XiL.NiJ.Production-Driven Opportunistic Maintenance for Batch Production Based on MAM–APB Scheduling. European Journal of Operational Research, Vol. 240, No. 3, 2015, pp. 781–790. https://doi.org/10.1016/j.ejor.2014.08.004.
19.
AizpuruaJ. I.CattersonV. M.PapadopoulosY.ChiacchioF.D’UrsoD.Supporting Group Maintenance through Prognostics-Enhanced Dynamic Dependability Prediction. Reliability Engineering & System Safety, Vol. 168, 2017, pp. 171–188. https://doi.org/10.1016/j.ress.2017.04.005.
20.
SheuS. H.JhangJ. P.A Generalized Group Maintenance Policy. European Journal of Operational Research, Vol. 96, No. 2, 1997, pp. 232–247. https://doi.org/10.1016/S0377-2217(96)00073-2.
21.
ScarfP. A.CavalcanteC. A. V.Hybrid Block Replacement and Inspection Policies for a Multi-Component System with Heterogeneous Component Lives. European Journal of Operational Research, Vol. 206, No. 2, 2010, pp. 384–394. https://doi.org/10.1016/j.ejor.2010.02.024.
22.
ChalabiN.DahaneM.BeldjilaliB.NekiA.Optimisation of Preventive Maintenance Grouping Strategy for Multi-Component Series Systems: Particle Swarm Based Approach. Computers & Industrial Engineering, Vol. 102, 2016, pp. 440–451. https://doi.org/10.1016/j.cie.2016.04.018.
23.
Ruiz-CastroJ. E.Complex Multi-State Systems Modelled Through Marked Markovian Arrival Processes. European Journal of Operational Research, Vol. 252, No. 3, 2016, pp. 852–865. https://doi.org/10.1016/j.ejor.2016.02.007.
24.
CesareM. A.SantamarinaC.TurkstraC.VanmarckeE. H.Modeling Bridge Deterioration with Markov Chains. Journal of Transportation Engineering, Vol. 118, No. 6, 1992, pp. 820–833. https://doi.org/10.1061/(ASCE)0733-947X(1992)118:6(820).
25.
MorcousG.Performance Prediction of Bridge Deck Systems Using Markov Chains. Journal of Performance of Constructed Facilities, Vol. 20, No. 2, 2006, pp. 146–155. https://doi.org/10.1061/(ASCE)0887-3828(2006)20:2(146).
26.
RanjithS.SetungeS.GravinaR.VenkatesanS.Deterioration Prediction of Timber Bridge Elements Using the Markov Chain. Journal of Performance of Constructed Facilities, Vol. 27, No. 3, 2013, pp. 319–325. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000311.
27.
WellalageN. K. W.ZhangT.DwightR.Calibrating Markov Chain-Based Deterioration Models for Predicting Future Conditions of Railway Bridge Elements. Journal of Bridge Engineering, Vol. 20, No. 2, 2015, p. 04014060. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000640.
28.
LiangZ.ParlikadA. K.Predictive Group Maintenance for Multi-System Multi-Component Networks. Reliability Engineering & System Safety, Vol. 195, 2020, p. 106704. https://doi.org/10.1016/j.ress.2019.106704.
KreiplS.PinedoM.Planning and Scheduling in Supply Chains: An Overview of Issues in Practice. Production and Operations Management, Vol. 13, No. 1, 2004, pp. 77–92. https://doi.org/10.1111/j.1937-5956.2004.tb00146.x.
31.
LiangZ.ParlikadA. K.A Condition-Based Maintenance Model for Assets with Accelerated Deterioration Due to Fault Propagation. IEEE Transactions on Reliability, Vol. 64, No. 3, 2015, pp. 972–982. https://doi.org/10.1109/TR.2015.2439132.
