Multi-Objective Optimization of the Depth and Cementation of Liquefiable Soil Surrounding Tunnels
محورهای موضوعی : Structural Engineering
Mohammad Shabani Soltan Moradi
1
(PhD Candidate, Department of Civil Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran.)
Mohammad Azadi
2
(Associate Professor, Department of Civil Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran. )
Homayoun Jahanian
3
(Department of Civil Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran)
کلید واژه: Multi-Objective Optimization, Liquefaction, Pore Water Pressure, Neural Network, Uplift,
چکیده مقاله :
Designing tunnels in liquefiable sandy soils presents a significant challenge in determining the optimal depth and extent of soil cementation around them. Reducing the depth of the tunnel decreases both the bending anchor force and the axial load on the tunnel's shell, yet it leads to an increase in ground surface settlement, and the opposite is true when depth is increased. Enhancing the cementation level at the tunnel's optimal depth reduces both structural uplift and shear forces exerted on the tunnel lining, but it also leads to an increase in axial loads and vice versa. Given the contradictory nature of these outcomes, the FLAC software was employed to simulate tunnels in liquefiable soils to address this dilemma. Subsequently, a neural network was utilized to identify correlations between the inputs and outputs of the simulation. This network was the objective function for identifying optimal values by applying a genetic algorithm. Optimal design parameters were derived using the NSGA-II modified algorithm, a multi-objective optimization technique based on the objective functions. Ultimately, Pareto charts generated from the multi-objective optimization process enabled designers to select the most suitable tunnel location according to their specific requirements concerning depth and soil cementation in liquefied soils.
Designing tunnels in liquefiable sandy soils presents a significant challenge in determining the optimal depth and extent of soil cementation around them. Reducing the depth of the tunnel decreases both the bending anchor force and the axial load on the tunnel's shell, yet it leads to an increase in ground surface settlement, and the opposite is true when depth is increased. Enhancing the cementation level at the tunnel's optimal depth reduces both structural uplift and shear forces exerted on the tunnel lining, but it also leads to an increase in axial loads and vice versa. Given the contradictory nature of these outcomes, the FLAC software was employed to simulate tunnels in liquefiable soils to address this dilemma. Subsequently, a neural network was utilized to identify correlations between the inputs and outputs of the simulation. This network was the objective function for identifying optimal values by applying a genetic algorithm. Optimal design parameters were derived using the NSGA-II modified algorithm, a multi-objective optimization technique based on the objective functions. Ultimately, Pareto charts generated from the multi-objective optimization process enabled designers to select the most suitable tunnel location according to their specific requirements concerning depth and soil cementation in liquefied soils.
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