Editorial for Internet of Things (IoT) and Artificial Intelligence (AI) in geotechnical engineering

the editorial staff of the Journal of Rock Mechanics and Geotechnical Engineering (JRMGE), to serve as Guest Editors for this Special Issue (SI).Over the last decade, the application of the Internet of Things (IoT) and Artificial Intelligence (AI) has increased rapidly to enhance automation in various industries.For efficient construction and maintenance of geotechnical infrastructures (slopes, tunnels, pipelines, and other ground infrastructures), there is a need to access and examine measured data in real-time.Variations in data type due to the usage of unmanned aerial vehicle (UAV) photogrammetric sensors, LiDAR, and fiber optic sensing techniques make data management and analysis more complicated.Advanced artificial intelligence, metaheuristic optimization, and data science can be reliable methods in geotechnical engineering for site investigation, risk assessment, design, construction, and maintenance at a higher level.The purpose of this SI is to review the latest developments in IoT and AI techniques as well as their key applications in solving geotechnical engineering problems.The SI contains 25 papers covering various monitoring methods, machine learning (ML) techniques, and their performance and challenges faced in real world applications.The following is a brief summary of each published paper in this SI.Liu et al. (2022) adopted seven ML methods for predicting tunneling-induced ground settlement using 14 contributing factors measured before or during tunnel excavation.ML methods were able to successfully predict settlements even though the dataset size was relatively small.Zhong and Tao (2022) developed a vibrational wireless underground system inspired by subterranean animals that rely on vibrations or seismic waves.The performance of the developed system shows that seismic waves produced by vibration can be used as an information carrier and can potentially be implemented in the Internet of underground things (IoUTs).Kannangara et al. (2022) compared the application of four different methods, such as Pearson correlation method, sequential forward selection (SFS), sequential backward selection (SBS) and Boruta algorithm, for predicting the tunneling-induced maximum