Underground winter bamboo shoots, prized for their high nutritional value and economic significance, face harvesting challenges owing to inefficient manual methods and the lack of specialized detection technologies. This review systematically evaluates current detection approaches, including manual harvesting, microwave detection, resistivity methods, and biomimetic techniques. While manual methods remain dominant, they suffer from labor shortages, low efficiency, and high damage rates. Microwave-based technologies demonstrate high accuracy and good depths but are hindered by high costs and soil moisture interference. Resistivity methods show feasibility in controlled environments but struggle with field complexity and low resolution. Biomimetic approaches, though innovative, face limitations in odor sensitivity and real-time data processing. Key challenges include heterogeneous soil conditions, performance loss, and a lack of standardized protocols. To address these, an integrated intelligent framework is proposed: (1) three-dimensional modeling via multi-sensor fusion for subsurface mapping; (2) artificial intelligence (AI)-driven harvesting robots with adaptive excavation arms and obstacle avoidance; (3) standardized cultivation systems to optimize soil conditions; (4) convolution neural network-transformer hybrid models for visual-aided radar image analysis; and (5) aeroponic AI systems for controlled growth monitoring. These advancements aim to enhance detection accuracy, reduce labor dependency, and increase yields. Future research should prioritize edge-computing solutions, cost-effective sensor networks, and cross-disciplinary collaborations to bridge technical and practical gaps. The integration of intelligent technologies is poised to transform traditional bamboo forestry into automated, sustainable smart forest farms, addressing global supply demands while preserving ecological integrity.

This study investigated the mechanical properties of rammed earth (RE) stabilized with cement or lime and reinforced with straw. Specifically, the compressive and tensile strengths of 15 different mix designs were analyzed, including unstabilized RE, RE stabilized with lime or cement (at 4 % and 8 % by weight of soil), and RE reinforced with straw (at 0.5 % and 1.0 % by weight of soil), along with various combinations of stabilized and unstabilized RE reinforced with straw. Mechanical properties were further assessed through ultrasonic testing and scanning electron microscopy (SEM). Additionally, a data-driven fuzzy logic model was developed to estimate the mechanical properties of RE, addressing a key gap in the application of fuzzy logic to RE construction. The results showed that stabilizing RE with cement and lime increased its 28-day dry compressive strength by 365% to 640% and 109% to 237%, respectively. The addition of straw generally reduced compressive strength. The stress-strain curves indicated that the elastic modulus of RE stabilized with cement and lime increased by up to 350% and 11 %, respectively. The 28-day dry tensile strength of the samples ranged from 0.17 to 0.56 MPa. Furthermore, the addition of stabilizers improved tensile strength by approximately 88 % to 224 %, while straw enhanced the tensile strength of unstabilized RE by about 35 %. Ultrasonic and SEM analyses provided valuable insights into the mechanical properties of RE. Additionally, the fuzzy logic model proved useful, yielding satisfactory results in predicting the properties of RE, particularly when using the centroid defuzzification method. The study concluded that RE materials when properly cured and effectively stabilized with cement, lime, and straw, can achieve acceptable mechanical properties and offer sustainable benefits.

Powdery Mildew Blumeria graminis (PMBG) is one of the most dangerous diseases for winter wheat plants, causing damage to all above-ground plant organs. The main aim of this study is to develop and validate machine learning (ML) models with explainable AI (XAI) capabilities for accurate risk prediction of PMBG in winter wheat crops at the pre-symptomatic stage. Multiple heterogeneous ML classifiers with XAI for PMBG risk prediction have been developed in this study. The weather data used in this study were collected from two regions in Ukraine and included hourly air temperature, solar radiation, leaf wetness duration and other measurements of soil and climatic parameters. Several different feature selection approaches were leveraged to retrieve the most salient features. The multistack of ML models has been used to find the best-performing pipeline, which achieved an accuracy of 82 %. Further, diverse XAI methods such as Shapley Additive Values (SHAP), ELI5, Anchor and Local Interpretable Model-agnostic Explanations (LIME) have been applied to understand the model predictions. The precision, recall, f1-score and AUC obtained were 85%, 82%, 82% and 72 % respectively. As a result a decision support system has been developed to predict the risk of wheat powdery mildew using soil and climatic parameters monitoring, ML, and XAI. This study provides the holistic risk prediction of PMBG for the enhancement of wheat stress resistance during the full cycle of its cultivation in open-field conditions.

The use of basalt fibers, which are employed in various fields, such as construction, automotive, chemical, and petrochemical industries, the sports industry, and energy engineering, is also increasingly common in soil reinforcement studies, another application area of geotechnical engineering, alongside their use in concrete. With this growing application, scientific studies on soil reinforcement with basalt fiber have also gained momentum. This study establishes the effects of basalt fiber on the liquid limit, plastic limit, and strength properties of soils, and the relationships among the liquid limit, plastic limit, and unconfined compressive strength of the soil. For this purpose, 12 mm basalt fiber was used as a reinforcement material in kaolin clay at ratios of 1.0%, 1.5%, 2.0%, 2.5%, and 3.0%. The prepared samples were subjected to liquid limit, plastic limit, and unconfined compressive strength tests. As a result of the experimental studies, the fiber ratio that provided the best improvement in the soil properties was determined, and the relationships among the liquid limit, plastic limit, and unconfined compressive strength were established. The experimental results were then used as input data for an artificial intelligence model. The used neural network (NN) was trained to obtain basalt fiber-to-kaolin ratios based on the liquid limit, plastic limit, and unconfined compressive strength. This model enabled the prediction of the fiber ratio that provides the maximum improvement in the liquid limit, plastic limit, and compressive strength without the need for experiments. The NN results were in great agreement with the experimental results, demonstrating that the fiber ratio providing the maximum improvement in the soil properties can be identified using the NN model without requiring experimental studies. Moreover, the performance and reliability of the NN model were evaluated using 5-fold cross-validation and compared with other AI methods. The ANN model demonstrated superior predictive accuracy, achieving the highest correlation coefficient (R = 0.82), outperforming the other models in terms of both accuracy and reliability.

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.

Shear wave velocity (Vs) is an important soil parameter to be known for earthquake-resistant structural design and an important parameter for determining the dynamic properties of soils such as modulus of elasticity and shear modulus. Different Vs measurement methods are available. However, these methods, which are costly and labor intensive, have led to the search for new methods for determining the Vs. This study aims to predict shear wave velocity (Vs (m/s)) using depth (m), cone resistance (qc) (MPa), sleeve friction (fs) (kPa), pore water pressure (u2) (kPa), N, and unit weight (kN/m3). Since shear wave velocity varies with depth, regression studies were performed at depths up to 30 m in this study. The dataset used in this study is an open-source dataset, and the soil data are from the Taipei Basin. This dataset was extracted, and a 494-line dataset was created. In this study, using HyperNetExplorer 2024V1, Vs prediction based on depth (m), cone resistance (qc) (MPa), shell friction (fs), pore water pressure (u2) (kPa), N, and unit weight (kN/m3) values could be performed with satisfactory results (R2 = 0.78, MSE = 596.43). Satisfactory results were obtained in this study, in which Explainable Artificial Intelligence (XAI) models were also used.

Soilcrete is an innovative construction material made by combining naturally occurring earth materials with cement. It can be effectively used in areas where other construction materials are not readily available due to financial or environmental reasons since soilcrete is made from readily available natural clay. It can also help to cut down the greenhouse gas emissions from the construction industry by encouraging the use of resources that are locally available. Thus, it is imperative to reliably predict different properties of soilcrete since the accurate determination of these properties is crucial for the widespread use of soilcrete materials. However, the laboratory determination of these properties is subjected to significant time and resource constraints. As a result, this research was undertaken to provide empirical prediction models for the density, shrinkage, and strain of soilcrete mixes using two machine learning algorithms: Gene Expression Programming (GEP) and Extreme Gradient Boosting (XGB). The analysis revealed that XGB-based predictions correlated more with real-life values than GEP having training R2=0.999\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{R}}{2}=0.999$$\end{document} for both density and shrinkage prediction and R2=0.944\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{R}}{2}=0.944$$\end{document} for strain prediction. Moreover, several explanatory analyses including individual conditional expectation (ICE) analysis and shapely analysis were done on the XGB model which showed that water-to-binder ratio, metakaolin content, and modulus of elasticity are some of the most important variables for forecasting soilcrete materials properties. Furthermore, an interactive graphical user interface (GUI) has been developed for effective utilization in civil engineering industry to forecast these properties of soilcrete materials.

This article explores the role of artificial intelligence (AI) in predicting nanomaterial properties, particularly its significance within geotechnical engineering. By analyzing multiple AI-based studies, the review concentrates on the forecasting of nanomaterial-altered soil characteristics and behaviors. Encouraging findings from these studies underscore AI's ability to accurately predict the geotechnical properties of nanomaterials, though challenges remain, particularly in quantifying nanomaterial percentages and their implications across various applications. Future research should address these challenges to enhance the accuracy of AI-based prediction models in geotechnical engineering. Nonetheless, the growing adoption of AI for predicting nanomaterial properties demonstrates its potential to revolutionize geotechnical engineering. AI's capacity to uncover intricate patterns and relationships beyond human capabilities enables more precise soil behavior predictions, fostering innovative solutions to geotechnical challenges. Its ability to process vast datasets, adapt to various scenarios, and continuously learn from new information makes AI an indispensable tool for understanding nanomaterial properties and their impact on soil behavior. In summary, the integration of AI and geotechnical engineering represents a pivotal advancement in comprehending nanomaterial properties and their practical applications. As research advances and AI technologies evolve, transformative progress in geotechnical engineering is expected. By harnessing AI's capabilities, researchers can unlock groundbreaking insights, drive innovation, and shape a more resilient and sustainable future for the geotechnical engineering industry.

Landslides are one of the geological disasters with wide distribution, high impact and serious damage around the world. Landslide risk assessment can help us know the risk of landslides occurring, which is an effective way to prevent landslide disasters in advance. In recent decades, artificial intelligence (AI) has developed rapidly and has been used in a wide range of applications, especially for natural hazards. Based on the published literatures, this paper presents a detailed review of AI applications in landslide risk assessment. Three key areas where the application of AI is prominent are identified, including landslide detection, landslide susceptibility assessment, and prediction of landslide displacement. Machine learning (ML) containing deep learning (DL) has emerged as the primary technology which has been considered successfully due to its ability to quantify complex nonlinear relationships of soil structures and landslide predisposing factors. Among the algorithms, convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are two models that are most widely used with satisfactory results in landslide risk assessment. The generalization ability, sampling training strategies, and hyperparameters optimization of these models are crucial and should be carefully considered. The challenges and opportunities of AI applications are also fully discussed to provide suggestions for future research in landslide risk assessment.

In geotechnical engineering, the precise identification of essential soil parameters from sensing and experimental data is vital for the accuracy of constitutive and finite element models. However, the complexity of sophisticated soil models often makes this task challenging. Traditional optimization methods that rely on gradient information often fall short in this class of problems, due to their struggle with black box models lacking clear gradient pathways. Gradient-free methods, though circumventing the need for direct gradient data, can still miss out on integrating previous insights when faced with new information. To tackle these issues, our study presents a cutting-edge method inspired by the mechanisms underlying AlphaZero, DeepMind's acclaimed algorithm that excels in mastering complex strategic games through autonomous learning. By adopting a comparable selflearning technique, our approach reinvents the task of parameter identification of advanced geotechnical models as a strategic game. It draws a parallel between optimizing model parameters and the complex task of developing victorious chess tactics. This method utilizes a blend of deep learning for initial estimations and Monte Carlo Tree Search (MCTS) for finer adjustments, promoting a self-enhancing calibration process. Such an approach paves the way for a more self-reliant and intelligent parameter identification methodology from sensing and experimental data. The outcomes of our study demonstrate the robustness and versatility of this approach across various geotechnical models, ranging from the parameter identification of sophisticated constitutive models to more complex applications involving inverse analyses using finite element models that include interactions between mechanical sensing devices and unsaturated soils.