Rapid Regional Liquefaction Probability Assessment Based on Transfer Learning

Earthquake-induced liquefaction poses significant risks to urban infrastructure, yet traditional regional assessment methods are hindered by sparse geotechnical data and high-cost exploration. Based on transfer learning, this study develops a rapid assessment procedure for regional probabilistic liquefaction, enabling efficient probabilistic liquefaction assessment. This study demonstrates the feasibility of utilizing transfer learning to integrate abundant source domain data with readily available seismic information and post-earthquake observation data. A novel regional liquefaction probability index is also introduced. Both the proposed procedure and index are validated through their application to the 1999 Chi-Chi earthquake case, illustrating practical utility. Case results for Yuanlin City show that the assessment, which identifies the southeastern area as most liquefaction-prone and is consistent with both the index (highest values in this area) and post-earthquake field observations, validates the procedure’s effectiveness. A simplified calculation method for the index is also provided, ensuring strong practical applicability.