Picture-Fuzzy Decision-Making Tool for Enhanced Risk Prioritization in Construction and Demolition Waste Management: A Hybrid FMEA-Fine-Kinney-SWARA-TOPSIS Approach

Abstract

Effectively managing Construction and Demolition Waste (CDW) requires prioritizing multi-dimensional risks, a task complicated by the inherent uncertainty and subjectivity of expert judgments. While classical methods like Failure Modes and Effects Analysis (FMEA) and Fine-Kinney (FK) provide a diagnostic structure, they struggle to capture the vagueness in subjective assessments. This study addresses this gap by developing an integrated framework that couples the classical FMEA/FK criteria (Severity, Exposure, Probability, Detectability, Frequency) with Picture-Fuzzy (PiF) multi-criteria decision making. The methodology first elicits criterion importances from 15 experts using PiF Stepwise Weight Assessment Ratio Analysis (PiF-SWARA), which retains approval, indeterminacy, rejection, and refusal degrees to reduce information loss. Subsequently, it ranks 40 risk factors using the PiF Technique for Order Preference by Similarity to Ideal Solution (PiF-TOPSIS). Results show severity is the most influential criterion, followed by exposure and probability. The framework identifies the highest-priority risks as cumulative pollution with rising complaints, groundwater leakage, and insufficient investment/operating budgets. A sensitivity analysis confirms that environmental and financial risks remain consistently prominent across various weighting scenarios. This harmonized FMEA/FK-PiF-SWARA-TOPSIS approach yields a transparent and defensible prioritization, offering a practical tool for managers to allocate resources effectively, focusing on critical environmental controls and addressing core financial deficiencies in CDW systems.