Cost-effective risk reduction in scaffold assembly using fuzzy logic-based assessment and optimization

Accidents during scaffold assembly and disassembly remain a persistent safety challenge in the construction industry. Traditional risk assessment methods are often insufficient in capturing the uncertainty and interdependence of contributing factors. This paper proposes a fuzzy logic-based methodology to assess and reduce occupational risks related to scaffold operations, while optimizing the associated costs of prevention. The method combines qualitative linguistic assessment (e.g., “low anchoring strength”, “poor distance from façade”, “missing diagonals”) with expert-defined membership functions and rule matrices. Risk levels are inferred using a Mamdani-type fuzzy inference system. To improve workplace safety, we introduce a reverse optimization model that identifies the most cost-efficient prevention actions required to reduce the fuzzy-evaluated risk below a critical threshold. A simulation developed in MATLAB demonstrates the effectiveness of the approach on a real-life scaffolding scenario. The model inputs include worker experience level, scaffold anchoring, diagonal installation, and tool condition. For each variable, cost vectors are assigned to possible safety improvements. The optimization process identifies the minimal-cost combination of input changes needed to reduce the overall fuzzy risk level from “Very High” to “Acceptable”.The results highlight the potential of integrating fuzzy systems with cost modeling to improve safety planning in high-risk construction tasks. This approach supports informed decision-making, tailored preventive actions, and efficient resource allocation, contributing to a proactive safety culture.