Over the last two decades, there has been a paradigm shift in geotechnical engineering driven by advances in sensing, communication, and data-driven techniques. These advancements enhanced the safety and reliability of geotechnical infrastructure through real-time monitoring and automated decision-making. In recent times, Large Language Models (LLMs) have emerged as advanced data-driven techniques contributing to automated risk assessment of geotechnical infrastructure. LLMs are advanced deep learning models widely used to solve complex numerical problems, analyze large volumes of data, and generate human language. This paper presents a critical review of the application of LLM in geotechnical engineering. The integration of LLMs into geotechnical engineering has demonstrated significant advances in slope stability analysis, bearing capacity computation, numerical analysis, soil–structure interaction, and underground infrastructure. By summarizing the latest research findings and practical applications, this research paper underscores the potential of LLMs to advance and automate various processes in geotechnical engineering. The findings presented in this paper not only provide insights into the current LLM-based geotechnical practices but also emphasize the instrumental role that LLM can play in advancing geotechnical engineering, ultimately ensuring a safer and more sustainable future. Lastly, this paper highlights the different LLM capabilities which can be used to empower geotechnical engineers.

Soil suction is a crucial factor affecting the hydraulic and mechanical property of unsaturated soils, playing an important role in geotechnical, geoenvironmental, and hydrological engineering applications such as slope stability, foundation design and irrigation planning. Conventionally, measuring and modeling soil suction and its associated curves like Soil Water Characteristic Curve (SWCC) and Soil Water Retention Curve (SWRC) require extensive, time-consuming tests in the laboratory. Recent progress in Machine Learning (ML) offers powerful as well as data-driven and reliable alternatives ways that can enhance the efficiency and accuracy of suction-related predictions across a wide range of soil conditions. This study aims to cover the current state of the art research on the integration of ML techniques into the prediction and analysis of soil suction behavior. Studies utilized various algorithms including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Artificial Neural Networks (ANNs), Support Vector Machine (SVM), Multi-Expression Programming (MEP), K-Nearest Neighbors (KNN), and AdaBoost (AB) to predict soil suction. These models demonstrated high predictive performance (R² > 0.90 in majority cases) based on soil parameters which can be easily evaluated like soil texture, bulk density, climate parameters, and remotely sensed data. Overall, this study covers the understanding of the current research gap related to SWCC and SWRC using different data driven and ML techniques.

Over the last two decades, there has been a paradigm shift in geotechnical engineering driven by advances in sensing, communication, and data-driven techniques. These advancements enhanced the safety and reliability of geotechnical infrastructure through real-time monitoring and automated decision-making. In recent times, Large Language Models (LLMs) have emerged as advanced data-driven techniques contributing to automated risk assessment of geotechnical infrastructure. LLMs are advanced deep learning models widely used to solve complex numerical problems, analyze large volumes of data, and generate human language. This paper presents a comprehensive review of the application of LLM in geotechnical engineering. The integration of LLMs into geotechnical engineering has demonstrated significant advances in slope stability analysis, bearing capacity computation, numerical analysis, soil-structure interaction, and underground infrastructure. By summarizing the latest research findings and practical applications, this research paper underscores the potential of LLMs to advance and automate various processes in geotechnical engineering. The findings presented in this paper not only provide insights into the current LLM-based geotechnical practices but also emphasize the instrumental role LLM can play in advancing geotechnical engineering, ultimately ensuring a safer and more sustainable future. Lastly, this paper highlights the different LLM capabilities which can be used to empower geotechnical engineers.

Pavements are essential elements of transportation networks and are instrumental to the growth and development of a country. To harness the full potential of this infrastructure, it is necessary to maintain it in proper structural and functional conditions. Conventional techniques, such as manual inspection can be used to determine the functional conditions of the pavement. However, these techniques have different shortcomings, including high monitoring costs, time consumption and requirement of traffic control during pavement surveying. These shortcomings can be mitigated by utilizing various Artificial Intelligence (AI) techniques such as machine learning and deep learning, for pavement surface inspection. This study aims to systematically review state-of-the-art deep learning techniques such as vision transformer model for pavement distress detection. This research contributes to the knowledge body by summarizing the application of vision transformer model for pavement distress detection. Moreover, this research provides a brief description of the application of other deep learning architectures including You Only Look Once (YOLO), and Convolutional Neural Networks (CNNs) for pavement distress detection. Deep learning techniques can autonomously detect various types of pavement distress including longitudinal and transverse cracks, rutting, faulting, patching, shoving, raveling and potholes from the pavement surface. The research was performed in three different steps namely: keyword selection, identification of databases, literature acquisition and summarizing the literature based on different deep learning models. The findings from the review indicate that YOLO and CNN were extensively employed by researchers, however in recent times, vision transformers gained popularity among researchers and pavement engineers. Overall, this study highlights the critical role played by different deep learning techniques in transforming pavement monitoring, leading to safer, more resilient, and sustainable transportation infrastructure.

Pavements are essential elements of transportation networks and are instrumental to the growth and development of a country. To harness the full potential of this infrastructure, it is necessary to maintain it in proper structural and functional conditions. Conventional techniques, such as manual inspection can be used to determine the functional conditions of the pavement. However, these techniques have different shortcomings, including high monitoring costs, time consumption and requirement of traffic control during pavement surveying. These shortcomings can be mitigated by utilizing various Artificial Intelligence (AI) techniques such as machine learning and deep learning, for pavement surface inspection. This study aims to systematically review state-of-the-art deep learning techniques such as You Only Look Once (YOLO), Convolutional Neural Networks (CNNs), and vision transformer architectures for pavement distress detection. Deep learning techniques can autonomously detect various types of pavement distress including longitudinal and transverse cracks, rutting, faulting, patching, shoving, raveling and potholes from the pavement surface. The findings from the review indicate that YOLO and CNN were extensively employed by researchers, however in recent times, vision transformers gained popularity among researchers and pavement engineers. Overall, this study highlights the critical role played by different deep learning techniques in transforming pavement monitoring, leading to safer, more resilient, and sustainable transportation infrastructure.

Over the last two decades, climate change events have significantly affected the stability of geotechnical infrastructure, caused damage and resulting in injuries and loss of life. Climate-change events such as heavy rainfall, high temperatures, and permafrost thaw can affect the stability of slopes and embankments, causing problems with tunnel linings and foundation failures. The effects of climate change-induced extreme weather events on geotechnical infrastructure and people's lives can be minimized by using advanced data analysis techniques for monitoring and design. Recently, Large Language Models (LLMs) have become popular for efficient and accurate infrastructure design and monitoring because they are trained on large volumes of data. This review summarizes the contributions of different researchers on the effects of climate change-induced extreme weather events on geotechnical infrastructure, including slopes, tunnels, embankments, and foundations. Moreover, this research proposes avenues for using LLMs for efficient monitoring and design of geotechnical infrastructure. By summarizing the findings, this paper highlights the impact of extreme weather events on geotechnical infrastructure and helps engineers adopt LLMs for the design and monitoring of geotechnical infrastructure. Overall, integrating LLMs into the design and monitoring of geotechnical infrastructure can improve safety by enhancing early-warning systems for slopes, embankments, and tunnels and by enabling designs that account for the variability of environmental forces during extreme weather events.