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Artificial intelligence (AI) applications have introduced transformative possibilities within geohazard analysis, particularly concerning the assessment of rock slope instabilities. This study delves into the amalgamation of AI and empirical techniques to attain highly precise outcomes in the evaluation of slope stability. Specifically, our primary objective is to propose innovative and efficient methods by investigating the integration of AI within the well-regarded Qslope system, renowned for its efficacy in analyzing rock slope stability. Given the complexities inherent in rock characteristics, particularly in coastal regions, the Qslope system necessitates adjustments and harmonization with other geomechanical methodologies. Uncertainties prevalent in rock engineering, compounded by water-related factors, warrant meticulous consideration during all calculations. To address these complexities, we present a novel approach through the infusion of fuzzy set theory into the Qslope classification, leveraging fuzziness to effectively quantify and accommodate uncertainties. Our approach employs a sophisticated fuzzy algorithm encompassing six inputs, three outputs, and 756 fuzzy rules, thereby enabling a robust assessment of rock slope stability in coastal regions. The implementation of this method capitalizes on the high-level programming language Python, enhancing computational efficiency. To validate the potency of our AI-based approach, we conducted preliminary tests on slope instabilities within coastal zones, indicating a promising initial direction. The results underwent thorough evaluation, affirming the precision and dependability of the proposed method. However, it is crucial to emphasize that this work represents a first attempt to apply AI to the evaluation of rock slope stability. Our findings underscore a high degree of concurrence and expeditious stability assessment, vital for timely and effective hazard mitigation. Nonetheless, we acknowledge that the r

Riverside landslides present a significant geohazard globally, posing threats to infrastructure and human lives. In line with the United Nations’ Sustainable Development Goals (SDGs), which aim to address global challenges, professionals in the field have developed diverse methodologies to analyze, assess, and predict the occurrence of landslides, including quantitative, qualitative, and semi-quantitative approaches. With the advent of computer programs, quantitative techniques have gained prominence, with computational intelligence and knowledge-based methods like artificial neural networks (ANNs) achieving remarkable success in landslide susceptibility assessments. This article offers a comprehensive review of the literature concerning the utilization of ANNs for landslide susceptibility assessment, focusing specifically on riverside areas, in alignment with the SDGs. Through a systematic search and analysis of various references, it has become evident that ANNs have emerged as the preferred method for these assessments, surpassing traditional approaches. The application of ANNs aligns with the SDGs, particularly Goal 11: Sustainable Cities and Communities, which emphasizes the importance of inclusive, safe, resilient, and sustainable urban environments. By effectively assessing riverside landslide susceptibility using ANNs, communities can better manage risks and enhance the resilience of cities and communities to geohazards. While the number of ANN-based studies in landslide susceptibility modeling has grown in recent years, the overarching objective remains consistent: researchers strive to develop more accurate and detailed procedures. By leveraging the power of ANNs and incorporating relevant SDGs, this survey focuses on the most commonly employed neural network methods for riverside landslide susceptibility mapping, contributing to the overall SDG agenda of promoting sustainable development, res

Pandemic plastics (e.g., masks, gloves, aprons, and sanitizer bottles) are global consequences of COVID-19 pandemic-infected waste, which has increased significantly throughout the world. These hazardous wastes play an important role in environmental pollution and indirectly spread COVID-19. Predicting the environmental impacts of these wastes can be used to provide situational management, conduct control procedures, and reduce the COVID-19 effects. In this regard, the presented study attempted to provide a deep learning-based predictive model for forecasting the expansion of the pandemic plastic in the megacities of Iran. As a methodology, a database was gathered from February 27, 2020, to October 10, 2021, for COVID-19 spread and personal protective equipment usage in this period. The dataset was trained and validated using training (80%) and testing (20%) datasets by a deep neural network (DNN) procedure to forecast pandemic plastic pollution. Performance of the DNN-based model is controlled by the confusion matrix, receiver operating characteristic (ROC) curve, and justified by the k-nearest neighbours, decision tree, random forests, support vector machines, Gaussian naïve Bayes, logistic regression, and multilayer perceptron methods. According to the comparative modelling results, the DNN-based model was found to predict more accurately than other methods and have a significant predominance over others with a lower errors rate (MSE = 0.024, RMSE = 0.027, MAPE = 0.025). The ROC curve analysis results (overall accuracy) indicate the DNN model (AUC = 0.929) had the highest score among others.

Featured Application: AI application in UCS prediction for limestones of Maragheh. The geomechanical properties of rock materials, such as uniaxial compression strength (UCS), are the main requirements for geo-engineering design and construction. A proper understanding of UCS has a significant impression on the safe design of different foundations on rocks. So, applying fast and reliable approaches to predict UCS based on limited data can be an efficient alternative to regular traditional fitting curves. In order to improve the prediction accuracy of UCS, the presented study attempted to utilize the support vector machine (SVM) algorithm. Multiple training and testing datasets were prepared for the UCS predictions based on a total of 120 samples recorded on limestone from the Maragheh region, northwest Iran, which were used to achieve a high precision rate for UCS prediction. The models were validated using a confusion matrix, loss functions, and error tables (MAE, MSE, and RMSE). In addition, 24 samples were tested (20% of the primary dataset) and used for the model justifications. Referring to the results of the study, the SVM (accuracy = 0.91/precision = 0.86) showed good agreement with the actual data, and the estimated coefficient of determination (R2) reached 0.967, showing that the model’s performance was impressively better than that of traditional fitting curves.

Featured Application: The presented paper conducted a comparative analysis based on well-known MLP, SVM, DT, and RF learning methods to assess/predict the safety factor (F.S) of earthslopes. Earth slopes’ stability analysis is a key task in geotechnical engineering that provides a detailed view of the slope conditions used to implement appropriate stabilizations. In the stability analysis process, calculating the safety factor (F.S) plays an essential part in the stability assessment, which guarantees operations’ success. Providing accurate and reliable F.S can be used to improve the stability analysis procedure as well as stabilizations. In this regard, researchers used computational intelligent methodologies to reach highly accurate F.S calculations. The presented study focused on the F.S estimation process and attempted to provide a comparative analysis based on computational intelligence and machine learning methods. In this regard, the well-known multilayer perceptron (MLP), decision tree (DT), support vector machines (SVM), and random forest (RF) learning algorithms were used to predict/calculate F.S for the earth slopes. These machine learning classifiers have a strong capability predict the F.S under certain conditions for slope failures and uncertainties. These models were implemented on a dataset containing 100 earth slopes’ stabilities, recorded based on F.S from various locations in the provinces of Fars, Isfahan, and Tehran in Iran, which were randomly divided into the training and testing datasets. These predictive models were validated by Janbu’s limit equilibrium analysis method (LEM) and GeoStudio commercial software. Regarding the study’s results, MLP (accuracy = 0.901/precision = 0.90) provides more accurate results to predict the F.S than other classifiers, with good agreement with LEM results. The SVM algorithm follows MLP (accuracy = 0.873/precision = 0.85). Regarding the estimated loss function, MLP obtained a 0.29 average loss in the F.S p

Pressuremeter Test (PMT), Cone Penetration Test (CPT), and Standard Penetration Test (SPT) are the key in-situ experiments to directly estimate the in-situ modulus of deformation and strength parameters of soils, which are highly used in coastal alluvium. In addition, CPT and SPT are unique tests for estimating engineering properties that are ideal for onshore regions. These tests are adaptable for coastal alluvium with different saturation levels, which facilitates the determination of the field deformation modulus. Regression analysis, on the other hand, is primarily employed to estimate the empirical relationship between measured parameters and to predict geo-engineering properties. This technique is typically used to estimate the in-situ modulus of deformation and strength parameters from CPT, SPT, and PMT results. The proposed formulas in this paper used regression to correlate and characterize coastal alluvium located in phase 14 South Pars (Assalouyeh) and were compared with previously published equations. As a result of the evaluations, the correlations provided for phase 14 South Pars can be expressed as Em = 0.442 qc + 2.221 (R2 = 0.999) and PL = 0.06 Em0.778 (R2 = 0.515). • This empirical method can be useful for ground assessment and estimating the in-situ modulus of deformation. • This relationship can use as a modification for the original formula used based on CPT-PMT-SPT for alluvium. • This empirical correlation provides fast and reliable data for Southwest Iran nearby the Persian Gulf.

Environmental monitoring of mining regions using satellite imagery is crucial for sustainable exploitation and preventing geohazards. Movements due to the failure of the roof in underground coal mining, by migrating upwards and outwards from the seam being mined, could eventually appear as ground deformation. To investigate the matter further, the surface deformation that occurred over the Pabdana mining area was monitored in three time periods, between October 2, 2014, and July 27, 2019. Persistent scatterer interferometry (PSI) was used based on 150 ascending and descending Sentinel-1A images. The maximum mining subsidence rate during the studied periods was about 30 to 35 mm/yr. The PSI analysis shows that the subsidence rate varied both temporally and spatially during the three studied periods. The time series and the displacement rate for various cross-sections highlight a clear quantitative relationship between coal extraction progress and subsidence, which proceeded southward throughout the three study periods. So, considering coal mining subsidence as a geohazard, land developments and structures over the mining area may be safeguarded. The approach used in this investigation can be implemented in other similar coal mining zones.

This article is dedicated to the pursuit of establishing a robust empirical relationship that allows for the estimation of in-situ modulus of deformations (Em and Gm) within sedimentary rock slope masses through the utilization of Qslope values. To achieve this significant objective, an expansive and thorough methodology is employed, encompassing a comprehensive field survey, meticulous sample collection, and rigorous laboratory testing. The study sources a total of 26 specimens from five distinct locations within the South Pars (known as Assalouyeh) region, ensuring a representative dataset for robust correlations. The results of this extensive analysis reveal compelling empirical connections between Em, geomechanical characteristics of the rock mass, and the calculated Qslope values. Specifically, these relationships are expressed as follows: Em = 2.859 Qslope + 4.628 (R2 = 0.554), and Gm = 1.856 Qslope + 3.008 (R2 = 0.524). Moreover, the study unravels intriguing insights into the interplay between in-situ deformation moduli and the widely utilized Rock Mass Rating (RMR) computations, leading to the formulation of equations that facilitate predictions: RMR = 18.12 Em0.460 (R2 = 0.798) and RMR = 22.09 Gm0.460 (R2 = 0.766). Beyond these correlations, the study delves into the intricate relationship between RMR and Rock Quality Designation (RQD) with Qslope values. The findings elucidate the following relationships: RMR = 34.05e0.33Qslope (R2 = 0.712) and RQD = 31.42e0.549Qslope (R2 = 0.902). Furthermore, leveraging the insights garnered from this comprehensive analysis, the study offers an empirically derived support system tailored to the distinct characteristics of discontinuous rock slopes, grounded firmly within the framework of the Qslope methodology. This holistic approach contributes significantly to advancing the understanding of sedimentary rock slope stability and provides valuable tools for informed engineering decisions., Peer Reviewed, Objectius de Desenvolupament Sostenible::9 - Indústria, Innovació i Infraestructura, Postprint (published version)

This study performs a GIS-based landslide susceptibility assessment using a convolutional neural network, CNN, in a study area of the Gorzineh-khil region, northeastern Iran. For this assessment, a 15-layered CNN was programmed in the Python high-level language for susceptibility mapping. In this regard, as far as the landside triggering factors are concerned, it was concluded that the geomorphologic/topographic parameters (i.e., slope curvature, topographical elevation, slope aspect, and weathering) and water condition parameters (hydrological gradient, drainage pattern, and flow gradient) are the main triggering factors. These factors provided the landside dataset, which was input to the CNN. We used 80% of the dataset for training and the remaining 20% for testing to prepare the landslide susceptibility map of the study area. In order to cross-validate the resulting map, a loss function, and common classifiers were considered: support vector machines, SVM, k-nearest neighbor, k-NN, and decision tree, DT. An evaluation of the results of the susceptibility assessment revealed that the CNN led the other classes in terms of 79.0% accuracy, 73.0% precision, 75.0% recall, and 77.0% f1-score, and, hence, provided better accuracy and the least computational error when compared to the other models.