This paper addresses challenges and solutions in urban development and infrastructure resilience, particularly in the context of Japan’s rapidly urbanizing landscape. It explores the integration of smart city concepts to combat land subsidence and liquefaction, phenomena highlighted by the 2011 Great East Japan Earthquake. Additionally, it examines the current situation and lack of geoinformation and communication technology in the concept of smart cities in Japan. Consequently, this study employs advanced technologies, including smart sensing and predictive analytics through kriging and ensemble learning, with the objective of enhancing the precision of geotechnical investigations and urban planning. By analyzing data in Setagaya, Tokyo, it develops predictive models to accurately determine the depth of bearing layers that are critical to urban infrastructure. The results demonstrate the superiority of ensemble learning in predicting the depth of bearing layers. Two methods have been developed to predict undetected geographic data and prepare ground reality and digital smart maps for the construction industry to build smart cities. This study is useful for real-time analysis of existing data, for the government to make new urban plans, for construction companies to conduct risk assessments before doing their jobs, and for individuals to obtain real-time geographic data and hazard warnings through mobile phones and other means in the future. To the best of our knowledge, this is the first instance of predictive analysis of geographic information being conducted through geographic information, big data technology, machine learning, integrated learning, and artificial intelligence.

This review presents a comprehensive synthesis of existing studies on the correlations between soil electrical resistivity and fundamental geotechnical properties, with the aim of improving the applicability of resistivity testing in geotechnical engineering. Based on both laboratory and field research, the analysis highlights moisture content as the most influential factor inversely affecting resistivity, followed by plasticity index, clay content, and dry unit weight. Statistical approaches—including regression analysis and Spearman’s rank correlation—and artificial neural networks (ANNs) were evaluated for their effectiveness in modeling these nonlinear relationships. ANNs demonstrated superior predictive performance, particularly in heterogeneous and fine-grained soils. The review also examines the influence of temperature and salinity, emphasizing the need for multivariable models for robust interpretation. While resistivity testing shows great potential for non-destructive soil classification and compaction assessment, limitations related to soil type specificity, environmental variability, and model generalizability are noted. This work provides an important foundation for the development of data-driven, non-invasive techniques for subsurface characterization and outlines directions for future research, including field validation and hybrid modeling.

This study focused on the middle-pressure jet grouting method, which has a complicated development mechanism for the columnar soil-improved body, with the aim of establishing a computer-aided engineering (CAE) system that can simulate the performance on a computer. Furthermore, in order to confirm the effect of middle-pressure jet grouting with mechanical agitation and mixing, a comparative analysis was performed with different jet pressures, the development situation was visualized, and the performance of this method was evaluated. The results of MPS-CAE as one of the CAE systems showed that the cement slurry jet ratio in the planned improvement range, including the periphery of the mixing blade, by the middle-pressure jet grouting together with the mechanical agitation and mixing was increased and a high quality columnar soil-improved body was obtained. It is expected that the introduction of CAE will contribute to the visualization of the ground, and that CAE will be an effective tool for the visual management of construction for ground improvement and the maintenance of improved grounds during the life cycle of the ground-improvement method.