https://doi.org/10.14311/APP.2022.33.0316 Acta Polytechnica CTU Proceedings 33:316–321, 2022 © 2022 The Author(s). Licensed under a CC-BY 4.0 licence Published by the Czech Technical University in Prague RAILWAY REINFORCED CONCRETE INFRASTRUCTURE LIFE MANAGEMENT AND SUSTAINABILITY INDEX Seyed Mohammad Sadegh Lajevardi∗, Paulo B. Lourenço, Hélder S. Sousa, José C. Matos University of Minho, ISISE, Department of Civil Engineering, Campus of Azurém, 4800-058, Guimarães, Portugal ∗ corresponding author: 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. Keywords: Life management, maintenance planing, sustainability index. 1. Introduction Most of the transportation modes are made through railway networks and road infrastructures. Passen- gers’ expectation is directly affected by the quality of structures 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 maintenance approach. The maintenance approach would be re- newed or retrofitted during the operation. Quality will explain by performance indicator which has been affected by several attitudes, especially peripheral cli- mate 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. Therefore, Multi-Criteria Decision Making (MCDM) has been applied to esti- mate the infrastructure’s element performance dur- ing the operation [2]. Recent research, for finding the sustainability index has been focused on several subsets 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 no significant weights for attributes. In other research, In order to mine the effective parameters for the in- dexing model based on Pareto concept and making pair-wise comparison questionnaire serving Analyti- cal 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 calcu- lated by AHP [5]. The theoretical framework devel- oped for extracting the Sustainability Index (SI) is a function that comprises Wellbeing, Resource, Com- pliance, 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 Sustainabil- ity Index model (SIM) useful to assess manufacturing performances or maintenance procedures during the operation 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 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. There- fore, sustainable development is a comprehensive so- lution for the present and future of humans [11]. Based on recent research, the sustainable index is an important index for the bridge management sys- tem and green infrastructures and green cities. 2. Aim of the research This paper presents an approach to developing a prac- tical indexing model in terms of resource management 316 https://doi.org/10.14311/APP.2022.33.0316 https://creativecommons.org/licenses/by/4.0/ https://www.cvut.cz/en vol. 33/2022 Railway Reinforced Concrete Infrastructure Subject Extracting indexmethod 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)) . 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] Featureselection principal component analysis- Sensitivity analysis Table 1. Research method and index extract. and optimization. It will prepare a decision support system for managers that leads to wider uptake wel- fare 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 opti- mized decision for the material and shape of their structures’ element during the design. This frame- work would also be useful for maintenance managers to decide about their maintenance plan with a com- parison between the elements and components of the railway network. 3. Research contribution This paper is structured as follows: • Data collection from overall monitoring, such as visual inspection. • Variance measurement and extracting the data value • Defining the reliability index • Define the effective parameters in terms of sustain- ability with a questionnaire form • Correlation analysis between the reliability index and sustainable parameters • Prepare a sustainable model for maintenance man- agers 317 S. 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. Methodology This research would establish a comprehensive math- ematical method for extracting the sustainability in- dex for infrastructure as follows. • Defect detection and extracting the probability of failure • Principal Component Analysis (PCA) and the Value 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 these weights, a new construction plan and materials re- garding their maintenance method will be updated. The framework of extracting sustainable index has been shown as follows diagram. 5. Data collection This research would be establishing a comprehensive mathematical method for extracting the sustainabil- ity index to assess infrastructure in terms of sustain- ability. This research has been focused on the Tehran Subway Bridge for monitoring and quality assessment during the operation. Based on the inspection check- list, five bridges are determined and inspected as a sample by expert inspectors. These bridges have been assessed by the items of the national code [14]. Parameters with subjective at- titudes have been ranked based on their environmen- tal 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 − µ σ (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. 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. 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 man- age assets along their lifetime in a more sustainable and efficient way [15]. Therefore, PCA has been used 318 vol. 33/2022 Railway Reinforced Concrete Infrastructure 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 Table 2. Component Matrix. 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 [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 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 Table 4. Number of registered defects based on bridge’s elements. Bridge number Number of defect on element [Table 4] Defect type Approximate volume (m3) Probability of defect [Table 4] (%) Impact [Table 2 & 3] Sustainability index 4 3 Instabilityof deck 709.8 0.42 0.931Rf+.031Th 0.42(0.931 Rf+.031Th) Table 5. Sustainability index for a bridge in case study. 319 S. M. S. Lajevardi, P. B. Lourenço, H. S. Sousa, J. C. Matos Acta Polytechnica CTU Proceedings Figure 2. Scree plot for component’s eigenvalue based on PCA analysis. 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 prob- ability 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 [16]. It is necessary for monitoring the mean value and variance changes for each feature, which would be im- portant for sustainable structures. For example, in- creasing 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 in- creasing the crack growth of concrete elements, these features would be related to these types of defects. These relations are shown as follows. 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 fol- lowing formula [17]. For each bridge (see Table 4), V is an approximate measure representative of their vol- ume regarding length (L) and width (W). The num- ber 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 de- fect in its components when compared with a smaller one. 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 sustainability index has been presented as follows. Bridge number 4 in this step is a sample for calculating the sustain- ability index for the instability of the deck. With this result, it is necessary to consider the risk of re- inforced 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 environ- ment features and structures. 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 ap- proach after putting up the probability of environ- ment 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 sus- tainability. 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 rail- way 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. 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