Artificial intelligence (AI) and the Internet of Things (IoT) are transforming the way geotechnical engineers monitor, analyze, and manage soil‑structure systems. Applications of AI and IoT in Geotechnical Engineering for Sustainable Infrastructure explores how these emerging digital technologies can enhance decision‑making, improve infrastructure resilience, and support sustainable development in civil engineering projects. It examines the integration of AI and IoT in geotechnical engineering, including slope stability assessments, data‑driven techniques for soil classification and characterization, foundation and liquefaction analysis, optimizing earthwork practices and operations, tunnels, and earth‑retaining structures using real‑time sensor networks and data‑driven models. This book also discusses machine learning techniques for predicting soil behavior, real‑time monitoring of pile foundations, automated site investigation methods, and digital tools for risk assessment and infrastructure health monitoring/assessment.
This book is intended for researchers, graduate students, and practicing engineers in geotechnical and civil engineering. It will also benefit professionals working in smart infrastructure, disaster risk reduction, and infrastructure asset management who are interested in integrating digital technologies into sustainable engineering practice.
Table of Contents:
1. A Review on Integration of AI and IoT in Geotechnical Engineering 2. Enhancing Resilience of Geotechnical Structures with AI and IoT 3. AI‑Based Soil Classification and Characterization: Techniques and Case Studies 4. Harnessing Artificial Intelligence Techniques for Prediction of Compaction Parameters of Soil 5. Application of Data‑Driven Techniques for Prediction of Soil Water Characteristic Curve: A State‑of‑the‑Art Review 6. AI‑Driven Prediction Models for Geotechnical Failures: Models and Real‑World Case Studies 7. AI and Machine Learning Approaches for Predicting Geotechnical Failures in Civil Infrastructure 8. Surrogate Modelling and Reliability Analysis for Complex Geotechnical Systems Using AI 9. Development and Application of an MR Model for Slope Stability Assessment: A Case Study on the Jorabat–Shillong Expressway 10. Identification of Landslide Susceptibility Areas in Pettimudi Hills Using Frequency Ratio and Logistic Regression Models 11. AI and IoT‑Driven Prediction Models for Stone Column Performance under Liquefaction 12. Development of Mobile Application for Calculating the Bearing Capacity of a Pile 13. Sustainable Earthwork Practices through AI Optimization 14. Optimizing Earthwork Operations for Sustainable Infrastructure Using AI Techniques 15. Earthquakes in the Age of AI: Redefining Risk Resilience and Responsibility 16. Seismic Data Analysis for Tsunamigenic Earthquake Detection Using Soft‑Computing Techniques 17. Application of Non‑Parametric Machine Learning Models in Estimating Penetration Rate of Tunnel Boring Machines 18. Real‑Time Monitoring and Health Assessment of Pile Foundations Using IoT and AI Technologies
About the Author :
Vedprakash C. Maralapalle is an Associate Professor at the L. S. Raheja School of Architecture, Mumbai, India. He holds a Ph.D. in Civil Engineering from NMIMS University, Mumbai, with research focused on the physical modelling and numerical analysis of vertical piles.His research interests include geotechnical engineering, pile foundations, physical and centrifuge modelling, numerical analysis using advanced software tools, artificial intelligence applications in civil engineering, smart infrastructure systems, and sustainable urban development.
Jayatheja Muktinutalapati is a researcher and academician in the domain of civil engineering. He holds a Ph.D. in civil engineering from Birla Institute of Technology and Science (BITS) - Pilani, and master’s degree in geotechnical engineering from Jawaharlal Nehru Technological University Hyderabad (JNTUH). He is currently working as an Assistant Professor in the RICS School of Built Environment, Amity University Maharashtra, Mumbai. His areas of research include recyclable materials, retaining structures, life cycle assessments, and AI applications in civil engineering.
Bogireddy Chandra is currently a Foreign Scientist at the ALT University in Almaty, Kazakhstan, and a Visiting Scholar in the Department of Civil and Intelligent Construction Engineering at Shantou University in China. Dr. Bogireddy's areas of research interest includes basic geomechanics, numerical modeling, image analysis, site characterization, earthquake engineering, biogeotechnology, sustainable and carbon reduction ground engineering (MICP, Biochar), consolidation, pipeline geotechnical engineering, and the study of atmospheric aerosols in geo-environmental systems.
Gangadhara Reddy Narala is an academician and researcher in Civil Engineering with a specialization in Geotechnical Engineering. He holds a Ph.D. in Civil Engineering from the IIT Bhubaneswar and a Master’s degree in Geotechnical Engineering from the NIT Bhopal. He is currently serving as an Assistant Professor at the Fiji National University, Suva, Fiji. His research interests lie in geotechnical and geoenvironmental engineering, particularly the beneficial utilization of industrial waste materials, pavement geotechnics, climate-resilient infrastructure, sustainable soil stabilization, the application of biopolymers and biochar in landfill engineering, slope stabilization, and climate change adaptation.
Abdullah Ansari currently serves as a Research Professor at the Earthquake Monitoring Center (EMC), Sultan Qaboos University, Muscat, Oman. His research focuses on seismic risk assessment, geotechnical earthquake engineering, tunnel and underground structure performance, and infrastructure resilience under multi-hazard conditions. He has extensive experience in developing analytical, numerical, and data-driven models for evaluating the seismic vulnerability of critical infrastructure systems. A key aspect of his research involves integrating artificial intelligence and machine learning techniques with traditional geotechnical engineering approaches to improve the prediction of seismic damage and infrastructure performance.
