This study demonstrates the feasibility of utilizing machine learning (ML) for routine identification of sand particles. Identifying different types of sand is necessary for various geotechnical exploration projects because understanding the specific sand type plays an important role in estimating the physical and mechanical properties of the soil. To accomplish this, dynamic image analysis was employed to generate a substantial volume of sand particle images. Individual size and shape descriptors were automatically extracted from each particle image. The analysis involved use of 40,000 binary particle images representing 20 different sand types, and a corresponding six size and four shape descriptors for each particle (400,000 parameters). Six ML models were trained and tested. The work demonstrates that using size and shape features the models efficiently identified up to 49% of individual sand particles. However, when clusters of particles were considered in conjunction with a voting algorithm, classification accuracy significantly improved to 90%. Among the ML models studied, neural networks performed the best, while decision tree exhibited the lowest accuracy. Finally, the use of size consistently outperformed shape as a classification parameter but combining size and shape parameters yielded superior results across all sands and classifiers. These findings suggest that ML holds much promise for automating sand classification using ordinary images.

Significant uncertainties can be found in the modelling of geotechnical materials. This can be attributed to the complex behaviour of soils and rocks amidst construction processes. Over the past decades, the field has increasingly embraced the application of artificial intelligence methodologies, thus recognising their suitability in forecasting non-linear relationships intrinsic to materials. This review offers a critical evaluation AI methodologies incorporated in computational mechanics for geotechnical engineering. The analysis categorises four pivotal areas: physical properties, mechanical properties, constitutive models, and other characteristics relevant to geotechnical materials. Among the various methodologies analysed, ANNs stand out as the most commonly used strategy, while other methods such as SVMs, LSTMs, and CNNs also see a significant level of application. The most widely used AI algorithms are Artificial Neural Networks (ANN), Random Forest (RF), and Support Vector Machines (SVM), representing 35%, 19%, and 17% respectively. The most extensive AI application is in the domain of mechanical properties, accounting for 59%, followed by other applications at 16%. The efficacy of AI applications is intrinsically linked to the type of datasets employed, the selected model input. This study also outlines future research directions emphasising the need to integrate physically guided and adaptive learning mechanisms to enhance the reliability and adaptability in addressing multi-scale and multi-physics coupled mechanics problems in geotechnics.

The Tibetan Plateau, known as the world's Third Pole due to its high altitude, is experiencing rapid, intense climate change, similar to and even far more than that occurring in the Arctic and Antarctic. Scientific data sharing is very important to address the challenges of better understanding the unprecedented changes in the Third Pole and their impacts on the global environment and humans. The National Tibetan Plateau Data Center (TPDC, http://data.tpdc.ac.cn) is one of the first 20 national data centers endorsed by the Ministry of Science and Technology of China in 2019 and features the most complete scientific data for the Tibetan Plateau and surrounding regions, hosting more than 3,500 datasets in diverse disciplines. Fifty datasets featuring high-mountain observations, land surface parameters, near-surface atmospheric forcing, cryospheric variables, and high-profile article-associated data over the Tibetan Plateau, frequently being used to quantify the hydrological cycle and water security, early warning assessments of glacier avalanche disasters, and other geoscience studies on the Tibetan Plateau, are highlighted in this manuscript. The TPDC provides a cloud-based platform with integrated online data acquisition, quality control, analysis, and visualization capability to maximize the efficiency of data sharing. The TPDC shifts from the traditional centralized architecture to a decentralized deployment to effectively connect Third Pole-related data from other domestic and international data sources. As an embryo of data sharing and management over extreme environment in the upcoming big data era, the TPDC is dedicated to filling the gaps in data collection, discovery, and consumption in the Third Pole, facilitating scientific activities, particularly those featuring extensive interdisciplinary data use.