營造業是職災死亡人數最多的產業，此現象來自於其產業特性本身的 高風險特徵導致風險評估項目具有高度的不確定性，因此難以被管理者量 化進行有效管理和評估。風險矩陣為現行實務上最常使用之風險評估方法， 然而其因未考慮資料不確定性、評估因子相對權重以及客觀性不足等問題 而無法有效評估危害事件的風險，因此專家學者提出模糊理論以解決上述 問題。此研究旨在探討模糊理論中風險評估因子包含隸屬函數的選擇、危害 事件類型等，以提升模糊理論為基礎的風險評估模型之完整性。 本研究藉由將模糊理論與理想解相似度順序偏好法結合作為研究方法 以探討風險矩陣的缺點，並於後續章節模擬並探討模糊數及相關評估因子 如何對評估結果產生影響，最後藉專家提供之案例驗證先前評估因子特性 探討結果。研究結果發現模糊數數值、專家及準則的權重等評估因子對於結 果具有影響力。透過此一研究將提供管理者考量不確定因素之完整風險評 估模型及原則，使管理者得以提升施工安全並降低風險及成本。;The construction industry consistently records the highest number of occupational fatalities, a situation largely attributed to its inherently high-risk nature, which introduces significant uncertainty into risk assessment processes. This uncertainty complicates the ability of managers to quantify and effectively manage and evaluate risks. Although the risk matrix remains the most widely adopted method in practice, it has been criticized for its inability to address data uncertainty, incorporate the relative weighting of evaluation criteria, and maintain sufficient objectivity. In response to these limitations, fuzzy set theory (FST) has been proposed by researchers as a viable solution to deal with this problem. This study aims to enhance the robustness of fuzzy logic-based risk assessment models by investigating critical factors, including the selection of membership functions and the classification of hazard event types. Fuzzy logic is integrated with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to systematically address the shortcomings of traditional risk matrix approaches. Simulations are conducted to examine how variations in fuzzy numbers and associated evaluation factors impact assessment results. Furthermore, the model is validated using expert-provided case data from the National Archives project to corroborate the earlier analysis of factor characteristics. The findings reveal that variables such as fuzzy number values, expert input, and criteria weightings significantly influence risk evaluation outcomes. This research offers a comprehensive risk assessment framework that accounts for uncertainty, providing construction managers with practical tools to enhance safety performance, mitigate risks, and reduce project costs.
