Assessing of causes of accidents based on a novel integrated interval-valued Fermatean fuzzy methodology: towards a sustainable construction site

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Springer London Ltd

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The statistics pertaining to accidents occurring at construction sites underscore the pressing requirement for a substantial and timely reevaluation of safety measures within the construction sector. Accidents do not occur randomly; rather, they arise from the presence of unsafe actions, hazardous conditions, or a combination of both. The majority of accidents stem from a combination of contributing causes, including unsafe acts and conditions. To enhance safety performance on a broader scale, this study undertakes an extensive analysis to identify these causes, evaluate their importance, and determine the countries that are most and least impacted by them. Ten African countries were selected as potential alternatives based on the frequency of infrastructure construction projects. A thorough review of existing literature was conducted to establish a three-level criteria framework. The framework was further refined through the Modified Delphi method to gather expert opinions. The weights assigned to the criteria were determined using the interval-valued Fermatean fuzzy analytical hierarchy process methodology. The Technique for Order Preference by Similarity to Ideal Solution method under the same fuzzy environment was then employed to rank the alternative countries. A sensitivity analysis was carried out to assess the robustness of the proposed methodology. The analysis revealed that the main cause of accidents was attributed to poor management, as it included ineffective project supervision, inadequate safety policies, poor organizational structure, and inappropriate scheduling/planning as the main underlying sub-factors. Additionally, it was observed that the sixth alternative country exhibited the highest susceptibility to accidents occurring at construction sites.


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Interval-valued Fermatean fuzzy sets, Causal accidents, Construction site, AHP, TOPSIS


Neural Computing & Applications

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