Multi-segment trenchless technology method selection algorithm for buried pipelines. (March 2018)

• Record Type: Journal Article
• Authors: Matthews, John C.; Allouche, Erez; Vladeanu, Greta; Alam, Shaurav
• Is Part Of: Tunnelling and underground space technology. Volume 73(2018)
• Page Start: 295
• Page End: 301
• Publication Date: 2018-03
• Subjects: Trenchless technology -- Decision support -- Pipeline construction; Tunneling -- Periodicals; Underground construction -- Periodicals; Tunnels -- Periodicals; Underground areas -- Periodicals; 624.193
• DOI: 10.1016/j.tust.2018.01.001
• Languages: English
• ISSNs: 0886-7798

Abstract

Abstract: There are many tools, models, and algorithms to aid in the selection of appropriate trenchless methods for pipeline installation or rehabilitation. However, one key concern is which method or technique provides optimum solution to the rehabilitation of multiple pipe segments rather than just a single pipe segment. Therefore, the searching criterion for an optimum decision support system is divided into two parts. First, the selection of the optimum method capable of solving the problem properly. Second, a simultaneous analysis of other parameters such as cost, time, and quality to improve the overall benefits of the project. It is observed that most of the real-world cases involves multiple segments in a single project. Therefore, an optimization of the solution must be made for those multiple pipe segments. Although use of different methods for each segment is a preferable solution, it may not be feasible in a wider consideration of project cost, quality, and time. Hence, one way to determine the optimal solution for multiple line segments is to minimize the number of methods and their anticipated total costs which include direct costs and social costs. This paper presents a mathematical approach to expedite the optimum solution by evaluating all of the trenchless technology methods capable of installing, replacing, or rehabilitating each pipe segment (a solution set) and minimizing the number of methods and their total costs.