Artificial Intelligence in Geosciences最新文献

{"title":"AI-based approaches for wetland mapping and classification: A review of current practices and future perspectives","authors":"Kai Cheng ,&nbsp;Cong Zhang ,&nbsp;Yaocheng Fan ,&nbsp;Hongli Diao ,&nbsp;Shibin Xia","doi":"10.1016/j.aiig.2025.100165","DOIUrl":"10.1016/j.aiig.2025.100165","url":null,"abstract":"<div><div>Wetlands are critical ecosystems that provide essential ecological, hydrological, and socio-economic services, such as water purification, climate regulation, and biodiversity conservation. However, effective wetland management faces significant challenges, particularly in the analysis and classification of complex wetland environments. Traditional methods of wetland monitoring often suffer from limitations in spatial coverage, temporal resolution, and data processing efficiency. Recent advancements in artificial intelligence (AI), particularly machine learning and deep learning techniques, have been increasingly integrated with remote sensing technologies, offering a powerful solution to these challenges. AI has demonstrated significant potential in automating large-scale remote sensing data analysis, enabling the extraction of detailed spatial information, and enhancing the accuracy and efficiency of wetland mapping and classification. Bibliometric analysis indicates a growing body of research, with notable contributions from China and the United States, though regional disparities and a lack of diverse datasets remain key issues. Despite the success of AI in wetland monitoring, challenges persist in addressing environmental heterogeneity, mixed pixels, and data quality. This review synthesizes the current state of AI-based approaches in wetland mapping and classification, identifies trends and gaps, and outlines future research directions, emphasizing the need for interdisciplinary collaboration and integration of multi-source data to advance AI applications in wetland conservation.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100165"},"PeriodicalIF":4.2,"publicationDate":"2025-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantifying uncertainty of mineral prediction using a novel Bayesian deep learning framework","authors":"Yue Liu","doi":"10.1016/j.aiig.2025.100164","DOIUrl":"10.1016/j.aiig.2025.100164","url":null,"abstract":"<div><div>Mineral resource exploration increasingly demands not only accurate prospectivity maps but also reliable measures of confidence to guide high-stakes decisions. In this study, a novel Bayesian deep learning (BDL) framework was introduced, which embeds probabilistic inference within a deep neural network to jointly predict mineralization potential and quantify uncertainty. Two posterior approximation strategies, Metropolis–Hastings (MH) sampling and variational inference (VI), are implemented to estimate model weights as distributions rather than as fixed values, enabling decomposition of predictive uncertainty into aleatoric and epistemic components. When applied to eleven ore-controlling features in the Nanling tungsten polymetallic region (China), both MH-based and VI-based BDL models demonstrate strong classification performance while revealing contrasting spatial patterns and uncertainty patterns. Correlation studies across probability bands confirm that MH sampling captures a broader spread of uncertainty at the cost of greater computational demand, while VI delivers greater efficiency but risks underestimating uncertainty. The results highlight trade-offs between accuracy, interpretability, and computational load, demonstrating that MH-based BDL offers more robust uncertainty assessments, whereas VI-based BDL places greater emphasis on efficiency. By providing spatially explicit probability and uncertainty maps, this framework advances risk-aware mineral exploration, enabling practitioners to target areas of high potential with low uncertainty and to identify regions warranting additional data acquisition.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100164"},"PeriodicalIF":4.2,"publicationDate":"2025-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145519710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Undrained uplift capacity prediction of open-caisson anchors in anisotropic clays using XGBoost integrated with mutation-based genetic algorithms","authors":"Rungroad Suppakul ,&nbsp;Wittaya Jitchaijaroen ,&nbsp;Suraparb Keawsawasvong ,&nbsp;Sutasinee Intui ,&nbsp;Shinya Inazumi","doi":"10.1016/j.aiig.2025.100162","DOIUrl":"10.1016/j.aiig.2025.100162","url":null,"abstract":"<div><div>This study evaluates the undrained uplift capacity of open-caisson anchors embedded in anisotropic clay using Finite Element Limit Analysis (FELA) and a hybrid machine learning framework. The FELA simulations investigate the influence of the radius ratio (<em>R/B</em>), anisotropic ratio (<em>r</em>_<em>e</em>), interface roughness factor (<em>α</em>), and inclination angle (<em>β</em>). Specifically, the results reveal that increasing <em>β</em> significantly enhances <em>N</em>_<em>c</em>, especially as soil behavior approaches isotropy. Higher <em>α</em> improves resistance at steeper inclinations by mobilizing greater interface shear. <em>N</em>_<em>c</em> increases with <em>r</em>_<em>e</em>, reflecting enhanced strength under isotropic conditions. To enhance predictive accuracy and generalization, a hybrid machine learning model was developed by integrating Extreme Gradient Boosting (XGBoost) with Genetic Algorithm (GA) and Mutation-Based Genetic Algorithm (MGA) for hyperparameter tuning. Among the models, MGA-XGBoost outperformed GA-XGBoost, achieving higher predictive accuracy (R² = 0.996 training, 0.993 testing). Furthermore, SHAP analysis consistently identified anisotropic ratio (<em>r</em>_<em>e</em>) as the most influential factor in predicting uplift capacity, followed by interface roughness factor (<em>α</em>), inclination angle (<em>β</em>), and radius ratio (<em>R/B</em>). The proposed framework serves as a scalable decision-support tool adaptable to various soil types and foundation geometries, offering a more efficient and data-driven approach to uplift-resistant design in anisotropic cohesive soils.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100162"},"PeriodicalIF":4.2,"publicationDate":"2025-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145415648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deciphering influential features in the seismic catalog for large earthquake occurrence from a machine learning perspective","authors":"Jinsu Jang ,&nbsp;Byung-Dal So ,&nbsp;David A. Yuen ,&nbsp;Sung-Joon Chang","doi":"10.1016/j.aiig.2025.100161","DOIUrl":"10.1016/j.aiig.2025.100161","url":null,"abstract":"<div><div>The spatiotemporal distribution and magnitude of seismicity collected over decades are crucial for understanding the stress interactions underlying large earthquakes. In this study, machine learning (ML) explainers identify and rank the features that distinguish Large Earthquake Occurrence (LEO) from non-LEO spatiotemporal windows. Seventy-eight statistics related to time, latitude, longitude, depth, and magnitude were extracted from the earthquake catalog (Global Centroid Moment Tensor) to produce 202,706 spatiotemporally discretized windows. ML explainers trained on these windows revealed the maximum magnitude (<em>M</em>_<em>max</em>) as the most influential feature. Classification performance improved when the maximum inter-event time, the average inter-event time, and the minimum ratio of focal depth to magnitude were jointly trained with <em>M</em>_<em>max</em>. The top five features showed weak-to-moderate correlations, providing complementary information to the ML explainers. Our explainable ML framework can be extended to different earthquake catalogs, including those with focal mechanisms and small-magnitude events.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100161"},"PeriodicalIF":4.2,"publicationDate":"2025-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unsupervised hierarchical sequence stratigraphy framework of carbonate successions","authors":"Márcio Vinicius Santana Dantas ,&nbsp;Kaio Henrique Masse Vieira ,&nbsp;Thomás Jung Spier ,&nbsp;José Arthur Oliveira Santos ,&nbsp;Alan Cabral Trindade Prado ,&nbsp;Danilo Vomlel ,&nbsp;Mariana Leite ,&nbsp;Felipe Alves Farias ,&nbsp;Daniel Galvão Carnier Fragoso ,&nbsp;Humberto Reis ,&nbsp;Gabriel Coutinho ,&nbsp;Douglas G. Macharet","doi":"10.1016/j.aiig.2025.100160","DOIUrl":"10.1016/j.aiig.2025.100160","url":null,"abstract":"<div><div>Performing the high-resolution stratigraphic analysis may be challenging and time-consuming if one has to work with large datasets. Moreover, sedimentary records have signals of different frequencies and intrinsic noise, resulting in a complex signature that is difficult to identify only through eyes-based analysis. This work proposes identifying transgressive-regressive (T-R) sequences from carbonate facies successions of three South American basins: (i) São Francisco Basin - Brazil, (ii) Santos Basin - Brazil, and (iii) Salta Basin - Argentina. We applied a hidden Markov model in an unsupervised approach followed by a Score-Based Recommender System that automatically finds medium or low-frequency sedimentary cycles from high-frequency ones. Our method is applied to facies identified using Fullbore Formation Microimager (FMI) logs, outcrop description, and composite logs from carbonate intervals. The automatic recommendation results showed better long-distance correlations between medium- to low-frequency sedimentary cycles, whereas the hidden Markov model method successfully identified high-resolution (high-frequency) transgressive and regressive systems tracts from the given facies successions. Our workflow offers advances in the automated analyses and construction of lower- to higher-rank stratigraphic framework and short to long-distance stratigraphic correlation, allowing for large-scale automated processing of the basin dataset. Our approach in this work fits the unsupervised learning framework, as we require no previous input of stratigraphical analysis in the basin. The results provide solutions for prospecting any sediment-hosted mineral resource, especially for the oil and gas industry, offering support for subsurface geological characterization, whether at the exploration scale or for reservoir zoning during production development.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100160"},"PeriodicalIF":4.2,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145264973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancements in Sinkhole Remediation: Field data-driven Sinkhole grout volume prediction model via machine learning-based regression Analysis","authors":"Bubryur Kim ,&nbsp;Yuvaraj Natarajan ,&nbsp;K.R. Sri Preethaa ,&nbsp;V. Danushkumar ,&nbsp;Ryan Shamet ,&nbsp;Jiannan Chen ,&nbsp;Rui Xie ,&nbsp;Timothy Copeland ,&nbsp;Boo Hyun Nam ,&nbsp;Jinwoo An","doi":"10.1016/j.aiig.2025.100159","DOIUrl":"10.1016/j.aiig.2025.100159","url":null,"abstract":"<div><div>Sinkhole formation poses a significant geohazard in karst regions, where unpredictable subsurface erosion often necessitates costly grouting for stabilization. Accurate estimation of grout volume remains a persistent challenge due to spatial variability, site-specific conditions, and the limitations of traditional empirical methods. This study introduces a novel machine learning-based regression model for grout volume prediction that integrates cone penetration test (CPT)-derived Sinkhole Resistance Ratio (SRR) values, spatial correlations between CPT and grouting points (GPs), and field-recorded grout volumes from six sinkhole sites in Florida. Three data transformation methods, the Proximal Allocation Method (PAM), the Equitable Distribution Method (EDM), and the Threshold-based Equitable Distribution Method (TEDM), were applied to distribute grout influence across CPTs, with TEDM demonstrating superior predictive performance. Synthetic data augmentation using spline methodology further improved model robustness. A high-degree polynomial regression model, optimized with ridge regularization, achieved high accuracy (R² = 0.95; PEV = 0.94) and significantly outperformed existing linear and logarithmic models. Results confirm that lower SRR values correlate with higher grout demand, and the proposed model reliably captures these nonlinear relationships. This research advances sinkhole remediation practice by providing a data-driven, accurate, and generalizable framework for grout volume estimation, enabling more efficient resource allocation and improved project outcomes.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100159"},"PeriodicalIF":4.2,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145320144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a reliable rock slope stability model utilizing field and analytical data – An integration of FE-ML approaches","authors":"Virat Singh Chauhan ,&nbsp;Md. Rehan Sadique ,&nbsp;Mohd. Masroor Alam ,&nbsp;Mohd. Ahmadullah Farooqi","doi":"10.1016/j.aiig.2025.100158","DOIUrl":"10.1016/j.aiig.2025.100158","url":null,"abstract":"<div><div>Slope instability in hilly regions is a highly complex phenomenon, with triggering factors ranging from natural events to anthropogenic activities. Such failures hit disastrous losses both in terms of material as well as life. It is necessary to comprehend the mechanism of these failures to mitigate such events and also to predict their vulnerability for better preparedness. Significant advancements have already been done in the area of slope stability analysis, and scores of valued tools and techniques have been developed, such as limit equilibrium methods, finite element and finite difference methods, stochastic methods, and several of their combinations. In this study, an attempt has been made to capitalize on machine learning tools to predict the factor of safety of rock slope stability in hilly regions. Three road-cut slopes have been considered and their stability is determined using both finite element (FE) and machine learning (ML) techniques. The idea to intertwine these approaches is to supplement each other and enhance the reliability of the results. The geotechnical data was acquired through field investigation trips to the adopted mountainous sites. Since the slopes at the site are rocky, in the FE model, the Generalized Hoek Brown (GHB) material model with shear strength reduction technique have been used. In the implementation of ML models, Random Forest (RF) and Gradient Boosting Machine (GBM) models have been used. For the training of the ML model, ample published data has been utilized, while for testing the ML model, the data from the current slope site is used. The analysis in ML model is carried out in three stages: a) without Hyperparameter tuning, b) with Hyperparameter tuning using GridSearchCV, and c) Pipeline incorporating Recursive Feature Elimination (RFE). Performance metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and R² score, were evaluated to assess the accuracy of the model. A slight discrepancy within a range of 10 percent has been found, which is rather expected due to factors such as grid refinement and, data volume and variability. Overall, the proposed ML model demonstrates excellent compatibility with the FE model results. This study is an attempt to pick relevant ML techniques to develop a purpose-built framework that has the potential to validate the rock slope stability obtained using the traditional methods.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100158"},"PeriodicalIF":4.2,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of major minerals in igneous rock microscopic images from thin sections through deep neural network analysis","authors":"Kouadio Krah ,&nbsp;Sié Ouattara ,&nbsp;Gbele Ouattara ,&nbsp;Marc Euphrem Allialy ,&nbsp;Alain Clement","doi":"10.1016/j.aiig.2025.100157","DOIUrl":"10.1016/j.aiig.2025.100157","url":null,"abstract":"<div><div>Several socio-environmental needs (medicine, industry, engineering, orogenesis, genesis, etc.) require minerals to be more precisly defined and characterised. The identification of minerals plays a crucial role for researchers and is becoming an essential aspect of geological analysis. However, traditional methods relied heavily on expert knowledge and specialised equipment, making them labour-intensive, costly and time-consuming. This dependence is often labour-intensive, not to mention costly and time-consuming. To address this issue, some researchers have opted for machine learning algorithms to quickly identify a single mineral in a microscopic image of rocks. However this approch does not correspond to patterns of mineral distribution, where minerals are typically found in associations. These associations make it difficult to accurately identify minerals using conventional machine learning algorithms. This paper introduces a deep neural learning model based on multi-label classification, utilizing the problem adaptation method to analyse microscopic images of rock thin sections. The model is based on the ResNet50 architecture, which is designed to analyse minerals and generates the probability of a mineral presence in an image. This method provides a solution to the dependence between associated minerals. Experiments on many test images showed a model confidence, achieving average precision, recall and F1_score 97.15 %, 96.25 % and 96.69 %, respectively. Visualisation of the class activation mapping using the Grad-CAM algorithm indicates that our model is likely to locate the identified minerals effectively. In this way, the importance of each pixel with the class of interest can be assessed using heat maps. The <strong>recorded</strong> results, in terms of both performance and pixel_level evaluation, demonstrate the promising potential of the model used. It can therefore be considered for multi-labels image classification, particulary for images representing rock minerals. This approach serves as a valuable support tool for geological studies.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100157"},"PeriodicalIF":4.2,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of the performance of gradient boost, linear regression, decision tree, and voting algorithms to separate geochemical anomalies areas in the fractal environment","authors":"Mirmahdi Seyedrahimi-Niaraq ,&nbsp;Hossein Mahdiyanfar ,&nbsp;Mohammad hossein Olyaee","doi":"10.1016/j.aiig.2025.100156","DOIUrl":"10.1016/j.aiig.2025.100156","url":null,"abstract":"<div><div>In this investigation, the Gradient Boosting (GB), Linear Regression (LR), Decision Tree (DT), and Voting algorithms were applied to predict the distribution pattern of Au geochemical data. Trace and indicator elements, including Mo, Cu, Pb, Zn, Ag, Ni, Co, Mn, Fe, and As, were used with these machine learning algorithms (MLAs) to predict Au concentration values in the Doostbigloo porphyry Cu-Au-Mo mineralization area. The performance of the models was evaluated using the Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) metrics. The proposed ensemble Voting algorithm outperformed the other models, yielding more accurate predictions according to both metrics. The predicted data from the GB, LR, DT, and Voting MLAs were modeled using the Concentration-Area fractal method, and Au geochemical anomalies were mapped. To compare and validate the results, factors such as the location of the mineral deposits, their surface extent, and mineralization trend were considered. The results indicate that integrating hybrid MLAs with fractal modeling significantly improves geochemical prospectivity mapping. Among the four models, three (DT, GB, Voting) accurately identified both mineral deposits. The LR model, however, only identified Deposit I (central), and its mineralization trend diverged from the field data. The GB and Voting models produced similar results, with their final maps derived from fractal modeling showing the same anomalous areas. The anomaly boundaries identified by these two models are consistent with the two known reserves in the region. The results and plots related to prediction indicators and error rates for these two models also show high similarity, with lower error rates than the other models. Notably, the Voting model demonstrated superior performance in accurately delineating mineral deposit locations and identifying realistic mineralization trends while minimizing false anomalies.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100156"},"PeriodicalIF":4.2,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine-learning seismic damage assessment model for building structures","authors":"Fatma Zohra Belhadj ,&nbsp;Ahmed Fouad Belhadj ,&nbsp;Mohamed Chabaat","doi":"10.1016/j.aiig.2025.100155","DOIUrl":"10.1016/j.aiig.2025.100155","url":null,"abstract":"<div><div>Buildings in seismic-prone regions are highly vulnerable to structural damage, necessitating meticulous Seismic Damage Assessment (SDA) for accurate design and mitigation strategies. The intricate nature of Seismic Damage Assessment (SDA) poses challenges, particularly when employing Finite Element Analysis (FE) for individual structures, as simulation techniques are time-intensive due to the inherent complexity of the models. Computational methods combining Soil-Structure Interaction (SSI) for earthquake damage assessment further compound the challenge, requiring substantial computational efforts to construct a comprehensive database for area-based prediction models. This study introduces such challenges via a novel Artificial Neural Network (ANN) approaches-based model as an alternative for prompt building Seismic Damage Assessment evaluation. The proposed ANN model leverages three key inputs—seismic, building, and soil parameters—incorporating a multi-step analysis process to generate seismic responses with soil-structure interaction. The findings underscore the remarkable accuracy of the SDA-Net model, positioning it as an effective predictive tool and rapid decision support system for structures affected by SSI impacts. This innovative approach not only serves as a proactive pre-disaster management tool for assessing potential damage but also emerges as a practical asset for ensuring the safety and durability of structures in the face of natural disasters. The study's contribution lies in its potential application as a valuable tool in structural engineering, aligning with the objectives and scope of the Research Journal of The Institution of Structural Engineers.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100155"},"PeriodicalIF":4.2,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145157171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}