## 7 Conclusions  
This study considers the time, cost, and access constraints of a single geotechnical site investigation. A data-driven adaptive fusion sampling strategy is proposed that automatically develops an assessment model of the spatial distribution of soil liquefaction potential from spatially sparse geotechnical data, performs monitoring of liquefaction-induced settlement, and integrates spatiotemporally unconstrained geophysical data to update the model systematically and quantitatively. It contains three major components: (i) data-driven site modeling by 2D Bayesian compressive sampling using sparse geotechnical data as input, (ii) CPT-based liquefaction procedure incorporating domain knowledge for consolidation settlement modeling, and (iii) multi-source fusion Bayesian compressive sampling coupled with information entropy to automatically fuse sampling samples of geotechnical and geophysical data in a self-adaptive manner and determine the optimal sampling locations. This strategy yields data combinations with different spatial scales and varying levels of uncertainty, not limited by soil types, to better characterize the subsurface space. Moreover, considering that most current geotechnical engineering models are highly non-linear and present large data set forms, it is difficult to perform integral solutions in high-dimensional parameter spaces. This study chooses MCMC simulation to obtain the target posterior distribution in MSF-BCS instead of the maximum likelihood estimation in the existing methods. The proposed strategy further obtains information on soil spatial variability while reducing interpolation uncertainty during the performing modeling, illustrated by a real data case from New Zealand. The results indicate that the developed self-adaptive fusion sampling strategy for evaluating the spatial distribution of soil liquefaction potential and performing monitoring of consolidation settlement, automatically and systematically fusing geophysical data (MASW), effectively overcomes the difficulty of reconstructing the high-resolution spatial distribution of liquefaction potential from sparse geotechnical data, enabling more accurate judgment of settlement variations in local areas, and is an effective tool for site liquefaction hazard analysis. Although the proposed strategy accurately evaluates the spatial distribution of soil liquefaction potential and performs settlement monitoring, there are still some limitations worth noting. Complex models still require more computational resources and time, thus requiring geotechnical engineers to trade-off between the accuracy of estimation results and computational efficiency. Moreover, MCMC inferring posterior distributions may introduce a reliance on prior distributions, which still require the engineering experience and expertise of geotechnical engineers to determine. Conceptualization, H.Y. and Z.L. ; methodology, H.Y. ; writing--original draft preparation, H.Y. ; writing--review and editing, Y.L., Y.Y. and G.T. ; funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript. This study was supported by the National Natural Science Foundation of China (41867039), the Guangxi Key Laboratory of Geomechanics and Geotechnical Engineering (20-Y-XT-03), and the Foundation Project of South China Mine Geological Environment Technology Innovation Center (CXZX2020002). Not applicable. Figure 19: Comparison of liquefaction-induced \(S_{v}\) monitoring results. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request. **Acknowledgments:** The authors acknowledge the members of the New Zealand Geotechnical Database for developing the database. **Conflicts of Interest:** The authors declare no conflict of interest.