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Samiei, Saeed, Aalianvari, Ali. (1404). Hydrogeological Risk Assessment of Tunnel Excavation Using a Hybrid AHP-AI Model: A Case Study. , (), -. doi: 10.22077/jgm.2025.9786.1056
Saeed Samiei; Ali Aalianvari. "Hydrogeological Risk Assessment of Tunnel Excavation Using a Hybrid AHP-AI Model: A Case Study". , , , 1404, -. doi: 10.22077/jgm.2025.9786.1056
Samiei, Saeed, Aalianvari, Ali. (1404). 'Hydrogeological Risk Assessment of Tunnel Excavation Using a Hybrid AHP-AI Model: A Case Study', , (), pp. -. doi: 10.22077/jgm.2025.9786.1056
Samiei, Saeed, Aalianvari, Ali. Hydrogeological Risk Assessment of Tunnel Excavation Using a Hybrid AHP-AI Model: A Case Study. , 1404; (): -. doi: 10.22077/jgm.2025.9786.1056

Hydrogeological Risk Assessment of Tunnel Excavation Using a Hybrid AHP-AI Model: A Case Study

Journal of Geomine
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 30 شهریور 1404
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22077/jgm.2025.9786.1056
نویسندگان
Saeed Samiei1؛ Ali Aalianvari* 2
1university of kashan
22 Associate Professor, Faculty of Engineering, Mining Engineering Department, university of kashan, ali_alianvari@kashanu.ac.ir
چکیده
This study investigates the hydrogeological impacts of the Bazideraz water transfer tunnel on the spring discharge of the Sarab Garm Spring in western Iran, a critical local water resource. A hybrid methodology is developed, integrating the Analytic Hierarchy Process (AHP) with Artificial Intelligence (AI) techniques, which are employed to refine the weights assigned to critical parameters. Fourteen key factors—including karst development, rock permeability, and fault influences—are assessed to quantify the risk of reduced spring discharge due to tunnel excavation. The AHP facilitates structured pairwise comparisons, while AI methods enhance the validation of these weights. The calculated risk index ® for Sarab Garm Spring is found to be 257, categorizing it within the moderate-to-high risk range and indicating a significant likelihood of flow reduction if no mitigation measures are implemented. Sensitivity analysis reveals that karst potential and rock mass permeability are the most influential factors affecting risk. These findings underscore the importance of geological structures, karst features, and aquifer-tunnel interactions in assessing spring vulnerability. This research provides a robust, data-driven tool for proactive pre-construction risk assessment and supports sustainable tunnel design by informing targeted mitigation strategies to ensure water security and environmental protection in complex hydrogeological settings.
کلیدواژه‌ها
Tunnel Excavation؛ Spring Discharge؛ Hydrogeological Risk؛ AHP؛ Artificial Intelligence؛ Bazideraz؛ Karst؛ Zagros
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