Assessment of soil liquefaction potential using genetic programming using a probability-based approach

Abstract

Abstract Soil liquefaction is a substantial seismic hazard that endangers both human life and infrastructure. This research specifically examines the occurrence of soil liquefaction events in past earthquakes, with a special emphasis on the 1964 Niigata, Japan and 1964 Alaska, USA earthquakes. These occurrences were important achievements in the comprehension of harm caused by liquefaction. Geotechnical engineers often use in-situ experiments, such as the standard penetration test (SPT) to evaluate the likelihood of liquefaction. The attraction for this option arises from the difficulties connected in acquiring undisturbed samples of superior quality, as well as the related expenses. Geotechnical engineering specialists choose the deterministic framework for liquefaction assessment because of its clear mathematical approach and low needs for data, time, and effort. This work emphasises the need of integrating probabilistic and reliability methodologies into the design process of crucial life line structures to enable well-informed risk-based decision-making. The objective of this project is to create models that use deterministic, probabilistic, and reliability-based methods to evaluate the likelihood of soil liquefaction. The work presents a new equation that combines Bayes conditional probability with Genetic Programming (GP). and also in study is to identify the most suitable method for liquefaction analysis based on factor of safety and Performance Fitness Error Metrics (PFEMs), Rank analysis, Gini index, etc. The information provided in study data include soil and seismic characteristics, including the corrected blow count (N1)60cs, fines content (FC), mean grain size (D50), peak horizontal ground surface acceleration (amax), earthquake magnitude (M), and CSR7.5. The parameters are derived from the SPT measurements conducted at many global locations, together with field performance observations (LI) and probability of liquefaction has been assessed through the use of Gini Index (GI). A comparison was made between the novel methodology and the techniques proposed by Juang et al. (2002), Toprak et al. (1999), and Idriss and Boulanger (2006) status of case history data using Performance Fitness Error Metrices. The comparison included employing a confusion matrix for binary classification and doing a score analysis based on factor ranking. The proposed model exhibited superior performance, as the outputs of the constructed model increased for all positive factors and decreased for negative indicators.