# An IVTIFN-TOPSIS Based Computational Approach for Pipe Materials Selection
## 1 Introduction  
Pipe materials are prone to chemical corrosion or scaling during waste-water collection and transportation, due to complex and harmful compounds contained. If the corrosion intensifies, it may cause failure of piping system, resulting in leakage and possible environmental damage [1]. Selection of appropriate materials is a premise to help with the careful design of a piping system, ultimately to ensure its operation in a safe and reliable way. Material selection is crucial for engineering design [2]. It is a complex issue, in which a decision maker may encounter a number of conflicting or competing attributes, e.g., that the economic and functional performance of materials to some extent may not match each other [3]. Basically, the selection of materials is to meet the design requirements [4]. [PERSON] [5] presented that reliability and longevity was critical to selection of pipe materials, in which internal pressure and external loads were the primary criteria to assess their performances. [PERSON] et al. [6] further incorporated the mechanical properties with corrosion resistance into the selection criteria of pipe materials. [PERSON] et al. [7] took the compatibility of materials with the working fluid into the selection criteria of heating pipes. In addition to materials' functional performances, economic attributes are also important factors in influencing material selection to decrease the manufacturing cost [8; 9]. [PERSON] [10] made a selection from five insulation materials based on their market price. [PERSON] et al. [11] evaluated the performance ofpipe materials for a borehole heat exchanger (BHE) in terms of their installation costs. [PERSON] et al. [12] designed economic evaluation criteria for the selection of plastic pipes, including procurement cost, processing cost and market share. With the concept of environmentally conscious design emerging in lean manufacturing, material selection pays more attention to mitigation of the product's lifecycle environmental impact [13]. [PERSON] et al. [14] investigated the possible lifecycle impacts on different pipe materials to lead optimal selection. [PERSON] et al. [15] selected the optimal pipe among four common sewage pipes based on their lifecycle environmental impacts. The above-mentioned studies inform us of the key criteria that should be considered in pipe-materials selection, including functional, economic, and environmental aspects. However, they seldom surveyed the impact of correlation among different criteria that may give rise to information overlapping. Our study filled such a gap by using the correlation test to eliminate the most interrelated criteria, through which Mahalanobis distance is further coupled with the multicriteria decision-making (MCDM) method to reduce the uncertainty of the selection results. Multicriteria decision making (MCDM) is classified into multiple-objective decision making (MODM) and multiple-attribute decision making (MADM) [16]. The former mainly focuses on applying optimization to satisfy predefined objective functions, whilst the latter ranks a finite number of alternatives according to their performances on a set of predetermined attributes [12; 17]. Since multiple factors are being taken into decision analysis, MADM methods, including the Analytic Hierarchy Process (AHP), Elimination Choice Translating Reality (ELECTRE), the Preference-ranking Organization Method for Enrichment Evaluations (PROMETHEE), the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR), Complex Proportional Assessment (COPRAS), etc., are widely employed to help decision makers carry out trade-offs among various alternatives. AHP has evolved into weighting the assignment of different criteria in a hierarchical indicator system [18], dealing with the inconsistency of group decision making [19], and examining the performance of Pareto-optimality on a set of feasible solutions [20].