In many construction projects,a proactive slope stability evaluation is a prerequisite.Although many deterministic or non-deterministic approaches have been commonly used,metaheuristic approaches have resulted in high...In many construction projects,a proactive slope stability evaluation is a prerequisite.Although many deterministic or non-deterministic approaches have been commonly used,metaheuristic approaches have resulted in high precision and significant outcomes for slope stability analysis problems.The current work focuses on the reliable assessment of critical failure surfaces associated with the least factor of safety value in both homogeneous and non-homogeneous slopes using a new simplified meta-heuristic approach called optics-inspired optimization(OIO).The algorithm utilizes six different LEM methods as a fitness function for deriving the factor of safety.Experimental analysis over three benchmark studies has been performed to demonstrate the algorithm's robustness and effectiveness.The implementation found more robust results as compared to previous studies.Meanwhile,the algorithm's statistical implication is conducted using the ANOVA test,which inferred better outcomes.With this interpretation,the approach claims to be suitable and efficient for slope stability analysis.展开更多
文摘In many construction projects,a proactive slope stability evaluation is a prerequisite.Although many deterministic or non-deterministic approaches have been commonly used,metaheuristic approaches have resulted in high precision and significant outcomes for slope stability analysis problems.The current work focuses on the reliable assessment of critical failure surfaces associated with the least factor of safety value in both homogeneous and non-homogeneous slopes using a new simplified meta-heuristic approach called optics-inspired optimization(OIO).The algorithm utilizes six different LEM methods as a fitness function for deriving the factor of safety.Experimental analysis over three benchmark studies has been performed to demonstrate the algorithm's robustness and effectiveness.The implementation found more robust results as compared to previous studies.Meanwhile,the algorithm's statistical implication is conducted using the ANOVA test,which inferred better outcomes.With this interpretation,the approach claims to be suitable and efficient for slope stability analysis.
