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Department of civil engineering, Faculty of engineering , namavaremad@gmail.com
Abstract: (114 Views)
Accurate geotechnical classification is essential for excavation design in urban environments, where soil behavior is highly influenced by excavation-induced stresses. This study refines the geotechnical characterization of fine-grained alluvial deposits belonging to the youngest sedimentary unit (Unit D) in Rieben’s classification. A comprehensive investigation was conducted through borehole drilling, Standard Penetration Tests (SPT), pressuremeter testing, and laboratory experiments including triaxial, uniaxial, and direct shear tests. Excavation stability was assessed using the Morgenstern–Price method under both short-term and long-term conditions. Based on the geotechnical parameters and slope stability simulations, Unit D was subdivided into three distinct zones (D1, D2, and D3) with different excavation behaviors. Zone D1, characterized by lower sand content, allows deeper vertical cuts, whereas the presence of sandy lenses in Zone D3 restricts excavation depth and requires gentler slopes. The findings provide an updated geotechnical classification framework for fine-grained alluvia, offering practical guidelines for safe excavation design and contributing to the broader understanding of alluvial systems in urban geotechnical engineering.
The developed framework offers substantial practical advantages including cost reduction through minimized laboratory testing, rapid prediction capabilities for quality control, and enhanced risk assessment through uncertainty quantification. The integration of petrographic analysis with machine learning provides engineers and practitioners with a scientifically robust and economically viable approach to rock strength assessment, supporting more reliable engineering design and reducing the risk of costly project failures.
The developed framework offers substantial practical advantages including cost reduction through minimized laboratory testing, rapid prediction capabilities for quality control, and enhanced risk assessment through uncertainty quantification. The integration of petrographic analysis with machine learning provides engineers and practitioners with a scientifically robust and economically viable approach to rock strength assessment, supporting more reliable engineering design and reducing the risk of costly project failures.
Keywords: Geotechnical classification, excavation stability, Morgenstern–Price method, alluvial deposits
Type of Study: Original Research |
Subject:
Engineering Geology
Received: 2025/09/18 | Accepted: 2025/10/11
Received: 2025/09/18 | Accepted: 2025/10/11
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