A Data-Driven Visualization Approach for Life-Cycle Cost Analysis of Open-Cut and Trenchless CIPP Methods for Sanitary Sewers: A PRISMA Systematic Review
Source abstract
Abstract
The wastewater conveyance systems in the United States are facing severe structural challenges, with the nation’s overall wastewater infrastructure receiving a critically low grade of D- from the American Society of Civil Engineers (ASCE). Innovative trenchless technologies, such as Cured-in-Place Pipe Renewal Technology (CIPPRT), offer a cost-efficient substitute for traditional open-cut construction methods (OCCM). However, the possibility of a comprehensive life-cycle cost analysis (LCCA) comparing these methods remains unexplored. LCCA examines the comprehensive financial impact, encompassing installation, operation, maintenance, rehabilitation, and replacement expenses, using net present value (NPV) over a set duration. The objective of this study is to systematically review the existing literature to explore advancements in calculating the LCCA for CIPPRT and compare the latter approach to OCCM. A rigorous PRISMA-guided methodology applied to academic databases identified 845 publications (1995–2024), with 83 documents being selected after stringent screening. The findings reveal limited use of artificial intelligence (AI) or machine learning (ML) in predicting CIPPRT costs. A bibliometric analysis using VOSviewer visualizes the results. The study underscores the potential of intelligent, data-driven approaches, such as spreadsheet models and AI, to enhance decision-making in selecting rehabilitation methods tailored to project conditions. These advancements promise more sustainable and cost-effective management of sanitary sewer systems, offering vital insights for decision-makers in addressing critical infrastructure challenges.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Infrastructure Maintenance and Monitoring · BIM and Construction Integration · Asphalt Pavement Performance Evaluation
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Civil Engineering Department, The University of Texas at Arlington, Box 19308, Arlington, TX 76019, USA
References (100+)
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