As ground subsidence accidents in urban areas that occur due to damage to underground utilities can cause great damage, it is necessary to predict and prepare for such accidents in order to minimize such damage. It has been reported that the main cause of ground subsidence in urban areas is cavities in the ground formed by damage to underground utilities. Thus, in this study, attribute information and historical ground subsidence information of six types of underground utility lines (water supply, sewage, power, gas, heating, and communication) were collected to develop a ground subsidence risk prediction model based on machine learning. To predict the risk of ground subsidence in the target area, it was divided into a grid with a square size of 500 m × 500 m, and attribute information of underground utility lines and historical information of ground subsidence included in the grid were extracted. Six types of underground utility lines were merged into single-type attribute information, and the risk of ground subsidence was categorized into three levels using the number of ground subsidence occurrences to develop a dataset. In addition, 12 datasets, which were developed based on the conditions of certain divided ranges of attribute information and risk levels, and 12 additional datasets, which were developed using the Synthetic Minority Oversampling Technique to resolve the imbalance of data, were built. Then, factors that represented significant correlations between input and output data were singled out and were then applied to the RandomForest, XGBoost, and LightGBM algorithms to select a model that produced the best performance. By classifying the ground subsidence risk levels through the selected model, it was found that density was the most important influencing factor used in the model. A risk map of ground subsidence in the target area was made through the model; the map showed the trend of well-predicted risk levels in the area where ground subsidence was concentrated.

In this study, a machine learning-based prediction model was developed using the attribute information of underground pipelines and the history information of ground subsidence in order to predict the risk level of ground subsidence in urban areas. The target area was divided into a grid with sizes of 100m×100m, 300m×300m, and 500m×500m, and the attribute information of underground pipelines in the grid and ground subsidence data were utilized to build a dataset. For input data, the pipeline’s diameter, the number of years used, and density were selected based on the pipeline’s length as the basic unit. And the risk level of ground subsidence using history information was set as the output data. A total of 36 datasets were built according to the conditions, and factors with significant correlation were selected through a correlation analysis of the datasets. The developed datasets were divided into training data and evaluation data. The synthetic minority oversampling technique was used to resolve the data imbalance. The model performance evaluation indexes used in this study were F1-score and AUC(Area Under the Curve). The performance of each model was compared, and the comparison results showed that a model that applied a preprocessed dataset with 500m×500m grid size, 10 years in use, 100mm pipeline diameter, and 1-2 ground sinks in Level 1 risk range to the LGBM(Light Gradient Boosting Model) classifier derived the best evaluation indexes(F1-Score:0.750, AUC:0.840). The map was found to be effective for predicting the risk level of ground subsidence in urban areas.

As large cities are continually being developed around coastal areas, structural damage due to the consolidation settlement of soft ground is becoming more of a problem. Estimating consolidation settlement requires calculating an accurate compressive index through consolidation tests. However, these tests are time-consuming, and there is a risk of the test results becoming compromised while preparing and testing the specimens. Therefore, predicting the compression index based on the results of relatively simple physical property tests enables more reliable and accurate predictions of consolidation settlement by calculating the compression index at multiple points. In this context, this study collected geotechnical data from the soft ground of Korea’s south coast. The collected data were used to construct a dataset for developing a compression index prediction model, and significant influencing factors were identified through Pearson correlation analysis. Simple and multiple linear regression analysis was performed using these factors to derive regression equations, and compression index prediction models were developed by applying machine learning algorithms. The results of deriving the significance of the influencing factors from the developed compression index prediction model showed that natural water content was the most significant factor in predicting the compression index. By collecting a significant amount of high-quality data and using the compression index prediction model and the model construction process proposed in this study, more accurate predictions of the compressive index will be possible in the future.

In recent years, we have witnessed an increase in road subsidence accidents in urban areas, threatening the safety of citizens. Various road facilities, such as water and sewage pipes, and telecommunication facilities are buried under roads, and the aging of these facilities is one of the factors causing road subsidence. In particular, old sewer pipes are a primary cause of road subsidence. However, most maintenance work on such facilities is carried out based on how long ago they were buried underground, without considering the risk of road subsidence caused by them. Therefore, this study aims to present a reliable method to assess road subsidence risk that considers various sewer pipe specifications and the environment surrounding them. To derive the factors influencing subsidence, sewer pipes near the target region, where road subsidence occurs the most, were extracted to analyze the correlation between road subsidence, pipe integrity, and the surrounding environment. An effective analysis method was selected by comparing logistic regression analysis and AHP (Analytic Hierarchy Process) analysis, and a weighted road subsidence risk assessment method was proposed by evaluating the importance of factors affecting ground subsidence. Its applicability was examined by comparing actual road subsidence data and analyzing risk in a pilot study area to validate the reliability of the proposed methodology. The results showed that it was possible to make reliable predictions of road subsidence risk areas.

The main cause of ground subsidence accidents in urban areas is cavities formed by damage to underground utilities. For this reason, the attribute information of underground utilities should be used to prepare against ground subsidence accidents. In this study, attribute information (pipe age, diameter, burial depth, and density) of six types of underground utilities (water, sewer, gas, power, heating, and communication) and history information of ground subsidence were collected. A correlation analysis was conducted using the collected data, and a prediction model of vulnerability to ground subsidence was developed through the ordinary least squares (OLS) method and spatial regression analysis (spatial lag model (SLM) and spatial error model (SEM)). To do this, the target area was divided into a grid of 100 m × 100 m. Datasets were constructed using the attribute information of underground utilities included in the divided grid and the number of ground subsidence occurrences. To analyze the OLS of the constructed data, the variance inflation factor (VIF) of the attribute information of underground utilities was studied. An OLS analysis was conducted using the appropriate factors, and the results show that the spatial data were autocorrelated. Subsequently, SEM and SLM analyses, which were spatial regression analyses, were conducted. As a result, the model using SLM was selected as suitable for analyzing the vulnerability of ground subsidence, and the density of six types of underground utilities was found to be the highest influencing factor. In addition, a vulnerability map of ground subsidence in the target area was prepared using the model. The vulnerability map demonstrates that regions with frequent ground subsidence can be predicted to be highly vulnerable.