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Binary composite crossover genetic algorithm for locating critical slip surface.

Wei Qin¹, Jiancheng Zhao²

¹Chongqing Vocational Institute of Engineering, Chongqing, 402260, China. qinw09@163.com.

Scientific Reports

|December 3, 2024

Summary

This summary is machine-generated.

This study introduces a novel genetic algorithm (RGA-BCC) to efficiently find the critical slip surface and minimum factor of safety for slope stability analysis. The RGA-BCC demonstrates superior performance compared to other algorithms.

Keywords:

Critical slip surface Factor of safety Genetic algorithm Slope stability analysis

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Area of Science:

Geotechnical Engineering
Computational Mechanics
Optimization Algorithms

Background:

Slope stability analysis is crucial for infrastructure safety.
Identifying the critical slip surface (CSS) and minimum factor of safety (Min. F) are complex optimization challenges.
Existing methods may lack efficiency in determining the CSS.

Purpose of the Study:

To propose and validate a real-coded binary composite crossover genetic algorithm (RGA-BCC) for locating slope CSSs.
To assess the efficiency and performance of the RGA-BCC in slope stability analysis.
To compare the RGA-BCC with other optimization algorithms like differential evolution (DE) and sparrow search algorithm (SSA).

Main Methods:

Developed a real-coded binary composite crossover genetic algorithm (RGA-BCC).
Optimized RGA-BCC parameters using six benchmark functions.
Applied RGA-BCC in conjunction with the Morgenstern and Price method to analyze five soil slopes from existing literature.
Conducted sensitivity analyses on control variables and population size.

Main Results:

The RGA-BCC efficiently determined the CSSs and minimum factors of safety for analyzed soil slopes.
Parameter settings, particularly the number of control variables, significantly impact CSS determination accuracy.
Slope structural characteristics influence optimal parameter settings for the RGA-BCC.
RGA-BCC outperformed DE and SSA in solving the slope CSS problem.

Conclusions:

The proposed RGA-BCC is an effective and efficient tool for slope stability analysis.
Careful consideration of control variables and population size, informed by slope characteristics, is essential for accurate results.
RGA-BCC offers a promising advancement over existing optimization methods for geotechnical engineering problems.