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Fuzzy-logic-based decision system for optimising infrastructure maintenance
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AI
DOI：10.1080/15732479.2025.2608056.png)
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Abstract
En 中文
The accelerated ageing of reinforced concrete (RC) due to environmental exposure creates significant economic risks, demanding timely, objective condition assessment. The paper presents a methodology that screens and classifies degradation indicators (DI), like carbonation depth, chloride content, corrosion potential, and electrical resistivity, into four damage classes, then uses transition functions to map classes to continuous values. These are combined through the Analytic Hierarchy Process (AHP) to compute a Corrosion Damage Index (CDI), which enters a Fuzzy-Logic (FL) decision system for selecting repair principles. The fuzzy inference comprises fuzzification, expert rule-based inference, and defuzzification to evaluate the optimal rehabilitation options. Practicality was demonstrated on a RC retaining wall in Vienna using real inspection data. The wall was discretised into a grid; DI were measured at grid nodes to represent the survey input. Measurements were converted to CDI via AHP and fed to the FL system to produce a spatial map of potential interventions at each grid point, providing location-specific guidance for maintenance. The approach scales from component to network level, supporting service-life forecasting and budget optimisation through aggregated CDI trends and intervention performance. Integrating expert knowledge with quantitative analysis, the system offers a robust, sustainable, and data-driven framework for planning and prioritising rehabilitation.
Keywords:
Analytical hierarchy process
condition assessment
corrosion monitoring
fuzzy logic
infrastructure maintenance
reinforced concrete structures
service life prediction
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2.6
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452
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5.3K
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