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.

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.

Despite the widespread application of data-centric techniques in Geotechnical Engineering, there is a rising need for building trust in the artificial intelligence (AI)-driven safety assessment of road embankments due to its so-called black-boxnature. In addition, from the lens of limit equilibrium approaches, e.g., Bishop, Fellenius, Janbu and Morgenstern-Price, and finite element method, it is essential to carefully examine the interplay of both topological and physical/mechanical properties during the safety factor (FoS) predictions. First, aside from having conventional geotechnical inputs for soil in core and foundation and the height of embankments, this paper codifies geometric features innovatively. The number of slope types with different ratios including 1:1, 1.5:1 and 2:1 as well as the number of berms is introduced. Second, a pool of 19 machine learning (ML) techniques is effortlessly trained on the dataset using an automated ML (AutoML) pipeline to identify the most optimized ML algorithm. Finally, to achieve post-hoc interpretability for the internal mechanism of the input- output relationship unbiasedly, a game-theory-based explainable AI (XAI) method called Shapley additive explanations (SHAP) values is applied. SHAP-aided importance analysis provides human-interpretable insights and indicates height, California bearing ratio, slope type 2:1 and cohesion as the most influential parameters. Exclusively, analyzing hazardous embankments by classifying main and joint contributors exhibits a complex and highly variable influence on the FoS. This paper harnesses the power of XAI tools to enhance reliability and transparency for the rapid FoS prediction of slopes. It targets geotechnical researchers, practitioners, decision-makers, and the general public for the first time.