Infrastructure healthy enhancement for saving resources in operation procedures is one of the most important objectives for owners on their decision support system based on cost management. In this manner, finding the intervention action priority, as well as the inspection method and maintenance system for each component, with regard to a limited resources amount is investigated in this paper. Defects on infrastructure components create data and these data are undoubtedly useful to increase the knowledge in decision making in practice. In that sense, risk analysis and value of information can be applied using decision trees together with Bayesian networks. For data filtering and noise reduction, a principal component analysis may also be applied to manage a database and prepare useful input variables for the decision tree system.
This paper presented an approach for the maintenance managers to prepare their infrastructure available with a sustainable index with minimum cost. This index would be a tool for decision-makers with regard to the cost management and sustainability aspects.This work was partly financed by FCT / MCTES through national funds (PIDDAC) under the R&D 
Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under reference UIDB 
/ 04029/2020.
This project has received funding from the European Union’s Horizon 2020 
research and innovation programme under grant agreement No769255

Lajevardi, Seyed Mohammad Sadegh

Lourenço, Paulo B.

Matos, José C.

Sousa, Hélder S.

Infrastructure healthy enhancement for saving resources in operation procedures is one of the most important objectives for owners on their decision support system based on cost management. In this manner, finding the intervention action priority, as well as the inspection method and maintenance system for each component, with regard to a limited resources amount is investigated in this paper. Defects on infrastructure components create data and these data are undoubtedly useful to increase the knowledge in decision making in practice. In that sense, risk analysis and value of information can be applied using decision trees together with Bayesian networks. For data filtering and noise reduction, a principal component analysis may also be applied to manage a database and prepare useful input variables for the decision tree system. This paper presented an approach for the maintenance managers to prepare their infrastructure available with a sustainable index with minimum cost. This index would be a tool for decision-makers with regard to the cost management and sustainability aspects

CTU Open Journal Systems (Czech Technical University, Prague / České vysoké učení technické v Praze)

https://doi.org/10.14311/APP.2022.33.0316Acta Polytechnica CTU Proceedings 33:316–321, 2022 © 2022 The Author(s). Licensed under a CC-BY 4.0 licencePublished by the Czech Technical University in PragueRAILWAY REINFORCED CONCRETE INFRASTRUCTURE LIFEMANAGEMENT AND SUSTAINABILITY INDEXSeyed Mohammad Sadegh Lajevardi∗, Paulo B. Lourenço,Hélder S. Sousa, José C. MatosUniversity of Minho, ISISE, Department of Civil Engineering, Campus of Azurém, 4800-058, Guimarães,Portugal∗ corresponding author: sadegh.lajevardi@gmail.comAbstract.Infrastructure healthy enhancement for saving resources in operation procedures is one of themost important objectives for owners on their decision support system based on cost management. Inthis manner, finding the intervention action priority, as well as the inspection method and maintenancesystem for each component, with regard to a limited resources amount is investigated in this paper.Defects on infrastructure components create data and these data are undoubtedly useful to increasethe knowledge in decision making in practice. In that sense, risk analysis and value of information canbe applied using decision trees together with Bayesian networks. For data filtering and noise reduction,a principal component analysis may also be applied to manage a database and prepare useful inputvariables for the decision tree system. This paper presented an approach for the maintenance managersto prepare their infrastructure available with a sustainable index with minimum cost. This index wouldbe a tool for decision-makers with regard to the cost management and sustainability aspects.Keywords: Life management, maintenance planing, sustainability index.1. IntroductionMost of the transportation modes are made throughrailway networks and road infrastructures. Passen-gers’ expectation is directly affected by the quality ofstructures and equipment during the operation. Sav-ing quality level requires planning and predicting re-source consumption based on the status of their ma-terials in their degradation model and maintenanceapproach. The maintenance approach would be re-newed or retrofitted during the operation. Qualitywill explain by performance indicator which has beenaffected by several attitudes, especially peripheral cli-mate such as strong corrosion processes in the coastalarea or freezes and thaw in deserts that result fromtemperature degree changes or moisture percentagechanges in terms of sustainability and environments[1]. Therefore, there is a wide range of alternativesand conflicting criteria are involved as the root offailure on infrastructure. Therefore, Multi-CriteriaDecision Making (MCDM) has been applied to esti-mate the infrastructure’s element performance dur-ing the operation [2]. Recent research, for findingthe sustainability index has been focused on severalsubsets of MCDM to prepare a decision support sys-tem such as Preference Selection Index (PSI) [2, 3].This method is typically chosen when there are nosignificant weights for attributes. In other research,In order to mine the effective parameters for the in-dexing model based on Pareto concept and makingpair-wise comparison questionnaire serving Analyti-cal Hierarchy Process (AHP) requirements to find theweights of them [4]. AHP method has been adoptedfor the calculation of the Sustainability AssessmentIndex (SAI) based on normalized key performanceindicators and their weight, which has been calcu-lated by AHP [5]. The theoretical framework devel-oped for extracting the Sustainability Index (SI) is afunction that comprises Wellbeing, Resource, Com-pliance, and resources [6]. For qualitative assessmentin the AHP process, recent research has been usedthe Likert scale and extract sustainability level [7].The results led to the development of a Sustainabil-ity Index model (SIM) useful to assess manufacturingperformances or maintenance procedures during theoperation with rank alternatives [8]. Also, these re-sults have been developed with respect to the evalu-ation criteria and the weights of the criteria to prior-itize the network systems [9]. These indexes could beextracted for prediction by neural network method todecision making with considering probable scenarios[10]. Since, future generations are important for thesestudies, prediction, and monitoring of the results withany scenario is necessary for decision-makers. There-fore, sustainable development is a comprehensive so-lution for the present and future of humans [11].Based on recent research, the sustainable index isan important index for the bridge management sys-tem and green infrastructures and green cities.2. Aim of the researchThis paper presents an approach to developing a prac-tical indexing model in terms of resource management316vol. 33/2022 Railway Reinforced Concrete InfrastructureSubject Extracting indexmethod Subset methodCoastal climate adaptation planning and evolutionarygovernance: Insights from Homer, Alaska. [1]Interviews withkey informants -Development of Sustainable Performance Index (SPI)for Self-Compacting Concretes (SCC) [2] MCDM SPIApplication of preference selection index methodfor decision making over the design stage ofproduction system life cycle [3]MCDM PSISustainability index for highway construction projects [4] MCDM AHPDevelopment of sustainability assessmentindex for machine tools [5] MCDM AHPDevelopment of a multidisciplinary approach to computesustainability index for manufacturing plants -Singapore Assessing the feasibility of using theheat demand-outdoor perspectivetemperature function for a long-termdistrict heat demand forecast [6]MCDM AHPGroundwater sustainability assessment framework:A demonstration of environmental sustainabilityindex for Hanoi [7]MCDM AHPDeveloping a sustainability index for Mauritianmanufacturing companies [8]Rankcorrelation-OrdinalassociationKendall coefficientLife cycle aggregated sustainability index for theprioritization of industrial systems under datauncertainties [9]LCCNet present value(NPV), internalreturn rate (IRR),and pay- back time(PT)).Predicting subjective measures of walkability index fromobjective measures using artificial neural networks [10] ANN -Comparison of sustainability models in development ofelectric vehicles in Tehran using fuzzy TOPSIS method [11] MCDM fuzzy TOPSISThe Lisbon ranking for smart sustainable cities in Europe [12] Featureselectionprincipalcomponentanalysis-SensitivityanalysisTable 1. Research method and index extract.and optimization. It will prepare a decision supportsystem for managers that leads to wider uptake wel-fare during the infrastructure life cycle for their realowner, people. Since this plan optimizes resourceconsumption, therefore it will keep the environmentsafe and prevent nature from contamination. Basedon this framework, the manager will make an opti-mized decision for the material and shape of theirstructures’ element during the design. This frame-work would also be useful for maintenance managersto decide about their maintenance plan with a com-parison between the elements and components of therailway network.3. Research contributionThis paper is structured as follows:• Data collection from overall monitoring, such asvisual inspection.• Variance measurement and extracting the datavalue• Defining the reliability index• Define the effective parameters in terms of sustain-ability with a questionnaire form• Correlation analysis between the reliability indexand sustainable parameters• Prepare a sustainable model for maintenance man-agers317S. M. S. Lajevardi, P. B. Lourenço, H. S. Sousa, J. C. Matos Acta Polytechnica CTU Proceedings 	 	                 !  !!	  !   "#Figure 1. Flow diagram of extracting sustainable index.4. MethodologyThis research would establish a comprehensive math-ematical method for extracting the sustainability in-dex for infrastructure as follows.• Defect detection and extracting the probability offailure• Principal Component Analysis (PCA) and theValue of Information for reducing the useless data• Environment features and extracting the sustain-ability parameters• Replacing the roots of the defect (environment fea-tures) by the defect and extracting the sustainabil-ity index for case study• The weight of each parameter shows the impor-tance of the index elements. Based on theseweights, a new construction plan and materials re-garding their maintenance method will be updated.The framework of extracting sustainable index hasbeen shown as follows diagram.5. Data collectionThis research would be establishing a comprehensivemathematical method for extracting the sustainabil-ity index to assess infrastructure in terms of sustain-ability. This research has been focused on the TehranSubway Bridge for monitoring and quality assessmentduring the operation. Based on the inspection check-list, five bridges are determined and inspected as asample by expert inspectors.These bridges have been assessed by the items ofthe national code [14]. Parameters with subjective at-titudes have been ranked based on their environmen-tal status and the other geotechnical features between1 to 4 or 1 to 5. Some of these parameters would bedetermined according to the observed defects in eachelement of the bridge. Since these parameters are notthe same range, they will be normalized by formulaas follows.Z = x − µσ(1)In this formula, µ is the arithmetic mean and σ isthe standard deviation of the distribution. Row dataafter normalization process transfer to the Principalcomponent analysis (PCA) framework. Based on theScree plot and their Eigenvalue, three componentshave the most value of information.Observation from inspection determines the statusof the bridge’s elements as follows. Some of theseelements related to defects and they were affected byenvironments’ features according to the last table.Sustainable structures during their life cycle haveto fulfill the thresholds regarding their componentsand the environment’s features based on their weight.By considering these tools, it will be possible to man-age assets along their lifetime in a more sustainableand efficient way [15]. Therefore, PCA has been used318vol. 33/2022 Railway Reinforced Concrete InfrastructureComponent1 2 3Liquefaction (Li) .825 .448 -.084Land slide (Ls) -.012 -.681 .258Rock fall (Rf) -.811 -.252 .031Thunderstorm (Th) .313 .034 .931Flood (Fl) .675 -.475 .484Climate (Cl) .825 .448 -.084Density Of Soil (Ds) -.558 .648 .306Underground void Distance (Ud) -.553 .506 .508Table 2. Component Matrix.Defects type Dependent element Cl Fl Th Ls Li Ds Ud RfDeck stability Deck • •Drainage Drainage/Pier/Deck/ Foundation • • •Foundation/embankmentsettlements Foundation/Abutment • • • • • •Railway track geometry defect(Gage widening, Alignment,Twist, Longitudinal level [13])Railway track •Bridge thermal movement Deck/Railway track •Temperature stress cycling All bridge’s element •Deterioration All bridge’s element •Scour Pier/Foundation • •Table 3. Impact of environment features on bridge.Bridge number Defect onrailway trackDefect ondeckDefect onAbutmentDefect onPierDefect onfoundation1 6 0 0 0 02 3 1 0 0 03 7 4 1 0 04 1 3 0 0 05 0 4 1 0 06 1 3 0 0 07 1 0 0 0 0Table 4. Number of registered defects based on bridge’s elements.BridgenumberNumberof defecton element[Table 4]DefecttypeApproximatevolume (m3)Probabilityof defect[Table 4] (%)Impact[Table 2 & 3]Sustainabilityindex4 3 Instabilityof deck 709.8 0.42 0.931Rf+.031Th0.42(0.931Rf+.031Th)Table 5. Sustainability index for a bridge in case study.319S. M. S. Lajevardi, P. B. Lourenço, H. S. Sousa, J. C. Matos Acta Polytechnica CTU ProceedingsFigure 2. Scree plot for component’s eigenvaluebased on PCA analysis.and 3 components have been extracted by the PCAmethod for 8 environment features with their weightsas follows. Based on Table 2, Li represents the prob-ability of liquefaction and also other environmentalfeatures such as Ls and Rf and the other. Thismethod will keep the valuable data and reduce themain by ranking the parameters in each component[16].It is necessary for monitoring the mean value andvariance changes for each feature, which would be im-portant for sustainable structures. For example, in-creasing the mean value for flood shows the increasingprobability of the Scour surrounding the piers of thebridge. Since climate change would be effective in in-creasing the crack growth of concrete elements, thesefeatures would be related to these types of defects.These relations are shown as follows.For finding the sustainability index, it is necessaryto find the risk according to weight for their defects.The probability of failure will be extracted as the fol-lowing formula [17]. For each bridge (see Table 4), Vis an approximate measure representative of their vol-ume regarding length (L) and width (W). The num-ber of defects in each element has been determinedby visual inspection [18] (F) based on Table 4. Thisshows that a larger bridge has more capacity for a de-fect in its components when compared with a smallerone. Probability of failure (P-f) are normalized by di-viding them with the average of all failure densities.Pf =!F"V dv(2)V ≂ L × V (3)Based on Table 3 and Table 2, the sustainabilityindex has been presented as follows. Bridge number4 in this step is a sample for calculating the sustain-ability index for the instability of the deck. Withthis result, it is necessary to consider the risk of re-inforced concrete elements and their defects duringthe operation with regard to rockfall and thunder. Itis obvious for other bridges in the same area, impactfactor is the same but the probability of defect willchange based on their structural features. Therefore,the sustainability index will change for each bridge,and the final grade will determine by their environ-ment features and structures.6. ConclusionThe sustainability index would be a combination ofenvironmental features to make a decision during theoperation or designing of the structure. In this ap-proach after putting up the probability of environ-ment features such as rockfall or climate, finding thelocation, material and bridge structure type wouldbe possible by benchmarking and expert judgments.With this index it is possible to locate the bridgewith a minimum life cycle cost and maximum sus-tainability. Finding the optimum plan with regardto climate change and seasons is also possible duringthe operation. For example, in winter railway trackface with a wide range of changes in some area andthis issue consequently effects on increasing the rail-way track failure such as rail and fastening systembreakage. Therefore, finding the proper place, thebest material and optimum maintenance plan wouldbe possible with the sustainability index.AcknowledgementsAcknowledgments This work was partly financed by FCT/ MCTES through national funds (PIDDAC) under theR&D Unit Institute for Sustainability and Innovation inStructural Engineering (ISISE), under reference UIDB /04029/2020. This project has received funding from theEuropean Union’s Horizon 2020 research and innovationprogramme under grant agreement No769255. The soleresponsibility for the content of this publication lies withthe authors. It does not necessarily reflect the opinionof the European Union. Neither the Innovation and Net-works Executive Agency (INEA) nor the European Com-mission are responsible for any use that may be made ofthe information contained therein.References[1] S. Jeff Birchall. Coastal climate adaptation planningand evolutionary governance: Insights from Homer,Alaska. Marine Policy 112, 2020.https://doi.org/10.1016/j.marpol.2018.12.029.[2] A. S. Reddy, P. R. Kumar, P. A. Raj. Development ofSustainable Performance Index (SPI) forSelf-Compacting Concretes. Journal of BuildingEngineering 27, 2020.https://doi.org/10.1016/j.jobe.2019.100974.[3] Attri, R., & Grover, S. (2015). Application ofpreference selection index method for decision makingover the design stage of production system life cycle.Journal of King Saud University - EngineeringSciences, 27(2), 207-216.https://doi.org/10.1016/j.jksues.2013.06.003.320vol. 33/2022 Railway Reinforced Concrete Infrastructure[4] A. H. Ibrahim, M. A. Shaker. Sustainability index forhighway construction projects. Alexandria EngineeringJournal 58(4):1399-411, 2019.https://doi.org/10.1016/j.aej.2019.11.011.[5] N. Sihag, K. Singh Sangwan. Development of asustainability assessment index for machine tools.Procedia CIRP 80:156-61, 2019.https://doi.org/10.1016/j.procir.2019.01.018.[6] N. Venkataraman, K. Muthusamy. Development of amultidisciplinary approach to compute sustainabilityindex for manufacturing plants - Singaporeperspective. Energy Procedia 143:327-35, 2017.https://doi.org/10.1016/j.egypro.2017.12.692.[7] N. T. Bui, A. Kawamura, D. D. Bui, et al.Groundwater sustainability assessment framework: Ademonstration of environmental sustainability indexfor Hanoi, Vietnam. Journal of EnvironmentalManagement 241:479-87, 2019.https://doi.org/10.1016/j.jenvman.2019.02.117.[8] D. Beekaroo, D. S. Callychurn, D. K. Hurreeram.Developing a sustainability index for Mauritianmanufacturing companies. Ecological Indicators96:250-7, 2019.https://doi.org/10.1016/j.ecolind.2018.09.003.[9] J. Ren. Life cycle aggregated sustainability index forthe prioritization of industrial systems under datauncertainties. Computers & Chemical Engineering113:253-63, 2018. https://doi.org/10.1016/j.compchemeng.2018.03.015.[10] A. Sabzali Yameqani, A. A. Alesheikh. Predictingsubjective measures of walkability index from objectivemeasures using artificial neural networks. SustainableCities and Society 48, 2019.https://doi.org/10.1016/j.scs.2019.101560.[11] F. Samaie, H. Meyar-Naimi, S. Javadi, et al.Comparison of sustainability models in development ofelectric vehicles in Tehran using fuzzy TOPSISmethod. Sustainable Cities and Society 53, 2020.https://doi.org/10.1016/j.scs.2019.101912.[12] A. Akande, P. Cabral, P. Gomes, et al. The Lisbonranking for smart sustainable cities in Europe.Sustainable Cities and Society 44:475-87, 2019.https://doi.org/10.1016/j.scs.2018.10.009.[13] I. Soleimanmeigouni, A. Ahmadi, A. Nissen, et al.Prediction of railway track geometry defects: a casestudy. Structure and Infrastructure Engineering16(7):987-1001, 2019.https://doi.org/10.1080/15732479.2019.1679193.[14] Transportation research center. TechnicalIdentification of bridge, Iranian national code 367,Department of strategic planning, 2007.[15] J. Matos, A. Solgaard, C. Santos, et al. Life CycleCost, As a Tool for Decision Making on ConcreteInfrastructures. High Tech Concrete: WhereTechnology and Engineering Meet2018. p. 1832-9.https://doi.org/10.1007/978-3-319-59471-2_210.[16] C. Hanley, J. C. Matos, D. Kelliher, et al.Integrating multivariate techniques in bridgemanagement systems. Journal of Structural Integrityand Maintenance 2(3):143-51, 2017.https://doi.org/10.1080/24705314.2017.1354280.[17] B. Paulo. P. B. Lourenço, J. M. Lajevardi. Qualitycontrol index survey for railway bridge healthmonitoring. IABSE Symposium, Guimaraes 2019:Towards a Resilient Built Environment Risk and AssetManagement - Report, 2019.[18] M. Moore, B. Phares, B. A. Graybeal, et al.Reliability of Visual Inspection for Highway Bridges,Volume I: Final Report, Research, Development, andTechnology Turner-Fairbank Highway ResearchCenter, 2001.321

English

Railway reinforced concrete infrastructure life management and sustainability index

Infrastructure healthy enhancement for saving resources in operation procedures is one of the most important objectives for owners on their decision support system based on cost management. In this manner, finding the intervention action priority, as well as the inspection method and maintenance system for each component, with regard to a limited resources amount is investigated in this paper. Defects on infrastructure components create data and these data are undoubtedly useful to increase the knowledge in decision making in practice. In that sense, risk analysis and value of information can be applied using decision trees together with Bayesian networks. For data filtering and noise reduction, a principal component analysis may also be applied to manage a database and prepare useful input variables for the decision tree system.
This paper presented an approach for the maintenance managers to prepare their infrastructure available with a sustainable index with minimum cost. This index would be a tool for decision-makers with regard to the cost management and sustainability aspects.This work was partly financed by FCT / MCTES through national funds (PIDDAC) under the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under reference UIDB/04029/2020.
This project has received funding from the European Union’s Horizon 2020 
research and innovation programme under grant agreement No769255

Universidade do Minho: RepositoriUM

      1  Railway Reinforced Concrete Infrastructure Life Management and Sustainability Index Seyed Mohammad Sadegh Lajevardi University of Minho, ISISE, Department of Civil Engineering, Guimarães, Portugal  Paulo B. Lourenço University of Minho, ISISE, Department of Civil Engineering, Guimarães, Portugal  Hélder S. Sousa University of Minho, ISISE, Department of Civil Engineering, Guimarães, Portugal  José C. Matos University of Minho, ISISE, Department of Civil Engineering, Guimarães, Portugal sadegh.lajevardi@gmail.com Abstract. Infrastructure healthy enhancement for saving resources in operation procedures is one of the most important objectives for owners on their decision support system based on cost management. In this manner, finding the intervention action priority, as well as the inspection method and maintenance system for each component, with regard to a limited resources amount is investigated in this paper. Defects on infrastructure components create data and these data are undoubtedly useful to increase the knowledge in decision making in practice. In that sense, risk analysis and value of information can be applied using decision trees together with Bayesian networks. For data filtering and noise reduction, a principal component analysis may also be applied to manage a database and prepare useful input variables for the decision tree system. This paper presented an approach for the maintenance managers to prepare their infrastructure available with a sustainable index with minimum cost. This index would be a tool for decision-makers with regard to the cost management and sustainability aspects. 1.  Introduction Most of the transportation modes are made through railway networks and road infrastructures. Passengers’ expectation is directly affected by the quality of structures and equipment during the operation. Saving quality level requires planning and predicting resource consumption based on the status of their materials in their degradation model and maintenance approach. The maintenance approach would be renewed or retrofitted during the operation. Quality will explain by performance indicator which has been affected by several attitudes, especially peripheral climate such as strong corrosion processes in the coastal area or freezes and thaw in deserts that result from temperature degree changes or moisture percentage changes in terms of sustainability and environments [1]. Therefore, there is a wide range of alternatives and conflicting criteria are involved as the root of failure on infrastructure.       2  Therefore, Multi-Criteria Decision Making (MCDM) has been applied to estimate the infrastructure’s element performance during the operation [2]. Recent research, for finding the sustainability index has been focused on several subsets of MCDM to prepare a decision support system such as Preference Selection Index (PSI) [2, 3]. This method is typically chosen when there are no significant weights for attributes. In other research, In order to mine the effective parameters for the indexing model based on Pareto concept and making pair-wise comparison questionnaire serving Analytical Hierarchy Process (AHP) requirements to find the weights of them [4]. AHP method has been adopted for the calculation of the Sustainability Assessment Index (SAI) based on normalized key performance indicators and their weight, which has been calculated by AHP [5]. The theoretical framework developed for extracting the Sustainability Index (SI) is a function that comprises Wellbeing, Resource, Compliance, and resources [6]. For qualitative assessment in the AHP process, recent research has been used the Likert scale and extract sustainability level [7]. The results led to the development of a Sustainability Index model (SIM) useful to assess manufacturing performances or maintenance procedures during the operation with rank alternatives [8]. Also, these results have been developed with respect to the evaluation criteria and the weights of the criteria to prioritize the network systems [9]. These indexes could be extracted for prediction by neural network method to decision making with considering probable scenarios [10]. Since, future generations are important for these studies, prediction, and monitoring of the results with any scenario is necessary for decision-makers. Therefore, sustainable development is a comprehensive solution for the present and future of humans [11]. Table 1. Research method and index extract. Subject Extracting index method Subset method Coastal climate adaptation planning and evolutionary governance: Insights from Homer, Alaska. [1] Interviews with key informants - Development of Sustainable Performance Index (SPI) for Self-Compacting Concretes (SCC) [2] MCDM SPI Application of preference selection index method for decision making over the design stage of production system life cycle [3] MCDM PSI Sustainability index for highway construction projects [4] MCDM AHP Development of sustainability assessment index for machine tools [5] MCDM AHP Development of a multidisciplinary approach to compute sustainability index for manufacturing plants - Singapore Assessing the feasibility of using the heat demand-outdoor perspective temperature function for a long-term district heat demand forecast [6] MCDM AHP Groundwater sustainability assessment framework : A demonstration of environmental sustainability index for Hanoi [7] MCDM AHP Developing a sustainability index for Mauritian manufacturing companies [8] Rank correlation-Ordinal association Kendall coefficient Life cycle aggregated sustainability index for the prioritization of industrial systems under data uncertainties[9] LCC Net present value (NPV), internal return rate (IRR), and pay- back time (PT)).       3  Predicting subjective measures of walkability index from objective measures using artificial neural networks[10] ANN - Comparison of sustainability models in development of electric vehicles in Tehran using fuzzy TOPSIS method [11] MCDM fuzzy TOPSIS The Lisbon ranking for smart sustainable cities in Europe [12] Feature selection principal component analysis- Sensitivity analysis  Based on recent research, the sustainable index is an important index for the bridge management system and green infrastructures and green cities.  2.   Aim of the research This paper presents an approach to developing a practical indexing model in terms of resource management and optimization. It will prepare a decision support system for managers that leads to wider uptake welfare during the infrastructure life cycle for their real owner, people. Since this plan optimizes resource consumption, therefore it will keep the environment safe and prevent nature from contamination. Based on this framework, the manager will make an optimized decision for the material and shape of their structures’ element during the design. This framework would also be useful for maintenance managers to decide about their maintenance plan with a comparison between the elements and components of the railway network. 3.  Research contribution This paper is structured as follows: 1. Data collection from overall monitoring, such as visual inspection. 2. Variance measurement and extracting the data value 3. Defining the reliability index 4. Define the effective parameters in terms of sustainability with a questionnaire form 5. Correlation analysis between the reliability index and sustainable parameters 6. Prepare a sustainable model for maintenance managers 4.  Methodology This research would establish a comprehensive mathematical method for extracting the sustainability index for infrastructure as follows. 1. Defect detection and extracting the probability of failure   2. Principal Component Analysis (PCA) and the Value of Information for reducing the useless data 3. Environment features and extracting the sustainability parameters 4. Replacing the roots of the defect (environment features) by the defect and extracting the sustainability index for case study 5. The weight of each parameter shows the importance of the index elements. Based on these weights, a new construction plan and materials regarding their maintenance method will be updated. The framework of extracting sustainable index has been shown as follows diagram.        4  Principal Component Analysis (PCA)Extract component analysis and component s weightStartDetermine priority of each elements and critical zones in each structureAssign component s environment featuresReplace environment feature by component failure probabilityComponent s data entryExtract sustainable indexDetermine maintenance planningUpdate/End Figure 1. Flow diagram of extracting sustainable index.  5.  Data collection This research would be establishing a comprehensive mathematical method for extracting the sustainability index to assess infrastructure in terms of sustainability. This research has been focused on the Tehran Subway Bridge for monitoring and quality assessment during the operation. Based on the inspection checklist, five bridges are determined and inspected as a sample by expert inspectors. These bridges have been assessed by the items of the national code [13]. Parameters with subjective attitudes have been ranked based on their environmental status and the other geotechnical features between 1 to 4 or 1 to 5. Some of these parameters would be determined according to the observed defects in each element of the bridge. Since these parameters are not the same range, they will be normalized by formula as follows.  𝑍 =𝑥 − 𝜇𝜎 (1)  In this formula, 𝜇 is the arithmetic mean and 𝜎 is the standard deviation of the distribution. Row data after normalization process transfer to the Principal component analysis (PCA) framework. Based on the Scree plot and their Eigenvalue, three components have the most value of information.        5   Figure 2. Scree plot for component’s eigenvalue based on PCA analysis.  Sustainable structures during their life cycle have to fulfill the thresholds regarding their components and the environment's features based on their weight. By considering these tools, it will be possible to manage assets along their lifetime in a more sustainable and efficient way [15]. Therefore, PCA has been used and 3 components have been extracted by the PCA method for 8 environment features with their weights as follows. Based on Table 2, Li represents the probability of liquefaction and also other environmental features such as Ls and Rf and the other. This method will keep the valuable data and reduce the main by ranking the parameters in each component [17]. Table 2. Component Matrix  Component 1 2 3 Liquefaction (Li) .825 .448 -.084 Land slide (Ls) -.012 -.681 .258 Rock fall (Rf) -.811 -.252 .031 Thunderstorm (Th) .313 .034 .931 Flood (Fl) .675 -.475 .484 Climate (Cl) .825 .448 -.084 Density Of Soil (Ds) -.558 .648 .306 Underground void Distance (Ud) -.553 .506 .508  It is necessary for monitoring the mean value and variance changes for each feature, which would be important for sustainable structures. For example, increasing the mean value for flood shows the increasing probability of the Scour surrounding the piers of the bridge. Since climate change would be effective in increasing the crack growth of concrete elements, these features would be related to these types of defects. These relations are shown as follows.          6  Table 3. Impact of environment features on bridge. Defects type Dependent element Cl Fl Th Ls Li Ds Ud Rf Deck stability Deck         Drainage Drainage/Pier/ Deck/ Foundation         Foundation/embankment settlements Foundation/Abutment         Railway track geometry defect (Gage widening, Alignment, Twist, Longitudinal level[14]) Railway track         Bridge thermal movement Deck/Railway track         Temperature stress cycling All bridge’s element         Deterioration All bridge’s element         Scour Pier/Foundation          Observation from inspection determines the status of the bridge’s elements as follows. Some of these elements related to defects and they were affected by environments’ features according to the last table. Table 4. Number of registered defects based on bridge’s elements Bridge number Defect on railway track Defect on deck Defect on Abutment Defect on Pier Defect on foundation 1 6 0 0 0 0 2 3 1 0 0 0 3 7 4 1 0 0 4 1 3 0 0 0 5 0 4 1 0 0 6 1 3 0 0 0 7 1 0 0 0 0  For finding the sustainability index, it is necessary to find the risk according to weight for their defects. The probability of failure will be extracted as the following formula [16]. For each bridge (see table 4), V is an approximate measure representative of their volume regarding length (L) and width (W). The number of defects in each element has been determined by visual inspection [18] (F) based on table 4. This shows that a larger bridge has more capacity for a defect in its components when compared with a smaller one. Probability of failure (P-f) are normalized by dividing them with the average of all failure densities. 𝑃𝑓 =Σ𝐹∫ 𝑉𝑑𝑣 (2) 𝑉 ≃ 𝐿 ×𝑊 (3)       7  Based on Table 3 and Table 2, the sustainability index has been presented as follows. Bridge number 4 in this step is a sample for calculating the sustainability index for the instability of the deck. With this result, it is necessary to consider the risk of reinforced concrete elements and their defects during the operation with regard to rockfall and thunder. It is obvious for other bridges in the same area, impact factor is the same but the probability of defect will change based on their structural features. Therefore, the sustainability index will change for each bridge, and the final grade will determine by their environment features and structures.  Table 5. Sustainability index for a bridge in case study 6.  Conclusion The sustainability index would be a combination of environmental features to make a decision during the operation or designing of the structure. In this approach after putting up the probability of environment features such as rockfall or climate, finding the location, material and bridge structure type would be possible by benchmarking and expert judgments. With this index it is possible to locate the bridge with a minimum life cycle cost and maximum sustainability.  Finding the optimum plan with regard to climate change and seasons is also possible during the operation. For example, in winter railway track face with a wide range of changes in some area and this issue consequently effects on increasing the railway track failure such as rail and fastening system breakage. Therefore, finding the proper place, the best material and optimum maintenance plan would be possible with the sustainability index. Acknowledgments This work was partly financed by FCT / MCTES through national funds (PIDDAC) under the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under reference UIDB / 04029/2020. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No769255. The sole responsibility for the content of this publication lies with the authors. It does not necessarily reflect the opinion of the European Union. Neither the Innovation and Networks Executive Agency (INEA) nor the European Commission are responsible for any use that may be made of the information contained therein. References [1] Jeff Birchall, S. (2019). Coastal climate adaptation planning and evolutionary governance: Insights from Homer, Alaska. Marine Policy, (May), 103410. https://doi.org/10.1016/j.marpol.2018.12.029 [2] Reddy, A. S., Kumar, P. R., & Raj, P. A. (2020). 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Railway reinforced concrete infrastructure life management and sustainability index

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