Fill dam structures are generally considered safe, but frequent heavy rainfall in recent years due to climate change has increased their risk of collapse. Technologies for the monitoring and safety management of these structures have attracted considerable attention, and methods to utilize technologies such as artificial intelligence (AI) and Internet of Things (IoT) for maintenance have been investigated. However, the measurement and communication processes of sensors used in the IoT technology are prone to measurement errors. Moreover, the communication environment and frequency range used by unlicensed operators vary within a country. Technologies for accurate interpretation of measurement results and optimal communication are required to address these issues. In this study, a technology for dam safety management and communication environment optimization was developed using AIoT (AI+IoT), and its field applicability was verified.

For tunneling in urban areas, understanding the interaction and behavior of tunnels and the foundation of adjacent structures is very important, and various studies have been conducted. Superstructures in urban areas are designed and constructed with piled rafts, which are more effective than the conventional piled foundation. However, the settlement of a piled raft induced by tunneling mostly focuses on raft settlement. In this study, therefore, raft and pile settlements were obtained through 3D numerical analysis, and the change rate of settlement along the pile length was calculated by linear assumption. Machine learning was utilized to develop prediction models for raft and pile settlement and change rate of settlement along the pile length due to tunneling. In addition, raft settlement in the laboratory model test was used for the verification of the prediction model of raft settlement, derived through machine learning. As a result, the change rate of settlement along the pile length was between 0.64 and −0.71. In addition, among features, horizontal offset pile tunnel had the greatest influence, and pile diameter and number had relatively little influence.

Tunneling, especially in urban areas, affects many structures on the ground, which directly influences the usability and stability of the structures. The settlement of and axial stress on the pile foundation are important factors that determine the behavioral characteristics of the pile foundation. Therefore, this study uses numerical analysis and machine learning to derive a prediction model of pile axial stress due to tunnel excavation adjacent to the piled raft. Numerical analysis data were utilized for machine learning purposes, and the effects of the input data on the prediction model were scrutinized. The numerical analysis revealed that the change in the pile axial stress resulting from tunnel excavation differed depending on the pile’s location, with the greatest axial stress reduction occurring in the center of the piled raft. Furthermore, the rate of reduction was higher in soils with lower relative densities. Several algorithms were employed to derive the prediction model, with tree-based algorithms displaying notable performance in predicting pile axial stress. Additionally, preprocessing the data with appropriate feature engineering techniques exhibited superior predictive power, and incorporating settlement data aided in enhancing the prediction model’s performance.

The new Austrian tunnelling method (NATM) and tunnel boring machine (TBM) are methods for excavating underground spaces. The NATM construction method has noise and vibration problems, and excavation using TBM has the disadvantage of high initial cost and is limited by ground conditions. A parallel TBM and NATM construction method has been developed to overcome these limitations. However, research on the TBM and NATM parallel construction method is lacking, and it is crucial to identify the applicability of the TBM and NATM parallel construction method in advance by applying it to various ground and blasting conditions. In this study, based on the vibration measurement data of the excavation site using the NATM construction method, the vibration measurement values generated when excavating the top surface using the existing NATM construction method and the TBM and NATM parallel construction methods were compared. Lastly, the vibration reduction effect of the two construction methods was analysed according to the tunnel excavation depth and tunnel size through 3D numerical analysis. As the depth of tunnel excavation increases, the vibration at the ground surface is reduced by up to 83%. In addition, as the ratio of the size of the blasting surface with NATM and the excavation surface with TBM is closer to 1.0, the vibration decreases.

Vital underground structures such as sewers, power transmission lines, subways, and underpasses are potentially vulnerable to adverse effects from aboveground construction. In this study, the influence of pile installation on nearby existing tunnels was investigated. Both a laboratory model test and finite-element numerical analysis were conducted. Twelve different combinations of horizontal and vertical offsets between the pile and the tunnel were investigated. Different surcharge loads (allowable and ultimate) were also considered. In this way, the appropriate separation distance between the existing tunnel and the piles was established for sandy, medium-compaction soil. Although this study considers simple ground conditions, it facilitates safe construction by confirming the appropriate separation distance and comparing the areas that cannot be penetrated by the structures of each standard.

Submerged tunnels are an innovative infrastructure solution for connecting roads and railways, especially in areas where conventional bridge or overland tunnel construction is limited by deep waterways, narrow straits, or dense urban development. In such regions, submerged tunnels offer an efficient and less intrusive alternative that overcomes geographical constraints. However, unlike conventional ground-level or subsea tunnels, submerged tunnels have unique structural and environmental characteristics, which necessitate the development of a dedicated evaluation system for responding to fire and other disasters. In this study, a quantitative fire risk assessment program (SFT_QRA) was developed by reflecting the specific characteristics of submerged tunnels. The program was applied to both road and railway tunnels to obtain evaluation results. First, to more realistically reflect the fire risk within submerged tunnels, the latest statistical data were used to update fire occurrence probabilities and the proportion of vulnerable users. In addition, the optimal smoke control mode for structural stop zones in ultra-long tunnels was analyzed to derive strategies for establishing a safe evacuation environment. Second, an Excel VBA-based assessment program was developed to improve user convenience and was structured to enable fire analysis and evacuation simulations. Third, in order to verify the accuracy and reliability of the developed program, a comparative analysis was conducted against commercial quantitative risk assessment programs. As a result, the total risk error rate was 0.4% for road tunnels and within 5.0% for railway tunnels, showing similar levels of results. This study advances quantitative risk assessment methods by incorporating the unique features of submerged tunnels and implementing them in a validated program. Through this approach, it presents a practical solution that can contribute to the advancement of tunnel fire safety technologies and the overall enhancement of tunnel safety.