Discontinuous rock slope stability analysis under blocky structural sliding by fuzzy key-block analysis method

This study presents a fuzzy logical decision-making algorithm based on block theory to effectively determine discontinuous rock slope reliability under various wedge and planar slip scenarios. The algorithm was developed to provide rapid response operations without the need for extensive quantitative stability evaluations based on the rock slope sustainability ratio. The fuzzy key-block analysis method utilises a weighted rational decision (multi-criteria decision-making) function to prepare the 'degree of reliability (degree of stability-instability contingency)' for slopes as implemented through the Mathematica software package. The central and analyst core of the proposed algorithm is provided as based on discontinuity network geometrical uncertainties and hierarchical decision-making. This algorithm uses block theory principles to proceed to rock block classification, movable blocks and key-block identifications under ambiguous terms which investigates the sustainability ratio with accurate, quick and appropriate decisions especially for novice engineers in the context of discontinuous rock slope stability analysis. The method with very high precision and speed has particular matches with the existing procedures and has the potential to be utilised as a continuous decision-making system for discrete parameters and to minimise the need to apply common practises. In order to justify the algorithm, a number of discontinuous rock mass slopes were considered as examples. In addition, the SWedge, RocPlane softwares and expert assignments (25-member specialist team) were utilised for verification of the applied algorithm which led to a conclusion that the algorithm was successful in providing rational decision-making

A correlation based on pressuremeter, SPT and CPT tests for characterizing of coastal alluvium: A study for phase 14 South Pars, Iran

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

A Deep Learning Method for the Prediction of the Index Mechanical Properties and Strength Parameters of Marlstone

The index mechanical properties, strength, and stiffness parameters of rock materials (i.e., uniaxial compressive strength, c, ϕ, E, and G) are critical factors in the proper geotechnical design of rock structures. Direct procedures such as field surveys, sampling, and testing are used to estimate these properties, and are time-consuming and costly. Indirect methods have gained popularity in recent years due to their time-saving and highly accurate results, which are comparable to those obtained through direct approaches. This study presents a procedure for establishing a deep learning-based predictive model (DNN) for obtaining the geomechanical characteristics of marlstone samples that have been recovered from the South Pars region of southwest Iran. The model was implemented on a dataset resulting from the execution of numerous geotechnical tests and the evaluation of the geotechnical parameters of a total of 120 samples. The applied model was verified by using benchmark learning classifiers (e.g., Support Vector Machine, Logistic Regression, Gaussian Naïve Bayes, Multilayer Perceptron, Bernoulli Naïve Bayes, and Decision Tree), Loss Function, MAE, MSE, RMSE, and R-square. According to the results, the proposed DNN-based model led to the highest accuracy (0.95), precision (0.97), and the lowest error rate (MAE = 0.13, MSE = 0.11, and RMSE = 0.17). Moreover, in terms of R2, the model was able to accurately predict the geotechnical indices (0.933 for UCS, 0.925 for E, 0.941 for G, 0.954 for c, and 0.921 for φ)

A novel empirical classification method for weak rock slope stability analysis

This study presents a novel empirical classification system for stability analysis of rock slopes in weak rock based on their geotechnical and geological properties. For this purpose, consideration is given to the marly rock slopes, which include three main groups of weak rock (lime marlstone, marlstone, and marly limestone). The 40 different slopes located in the South Pars special zone (Assalouyeh), southwest of Iran, are targeted in classification. To prepare comprehensive graphical stability charts for weak rocks, extensive field surveys, sampling, geotechnical laboratory tests, and ground measurements are conducted in slope sites. Using the findings of the study, empirical stability charts for slopes composed of weak materials were developed. The charts are associated with geotechnical indexes, geo-units’ weathering impact, and in-situ stress conditions. Using these graphical charts assists in investigating the stability condition of rock slopes and estimating the geotechnical characteristics of clay-based weak rocks such as marlstones

Discontinuous rock slope stability analysis by limit equilibrium approaches - a review

Slope stability is one of the most important topics of engineering geology with a background of more than 300 years. So far, various stability assessment techniques have been developed which include a range of simple evaluations, planar failure, limit state criteria, limit equilibrium analysis, numerical methods, hybrid and high-order approaches which are implemented in two-dimensional (2D) and three-dimensional (3D) space. In the meantime, limit equilibrium methods due to their simplicity, short analysis time, coupled with probabilistic and statistics functions to estimate the safety factor (F.S), probable slip surface, application on different failure mechanisms, and varied geological conditions has been received special attention from researchers. The presented paper provides a review to limit equilibrium methods used for discontinuous rock slope stability analyses with different failure mechanisms of natural and cut slopes. The article attempted to provide a systematic review for rock slope stability analysis outlook based on limit equilibrium approaches

Fuzzy-Based Intelligent Model for Rapid Rock Slope Stability Analysis Using Qslope

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 reliability of this innovative method must be established through broader applications across diverse scenarios. The proposed AI-based approach’s effectiveness is validated through a preliminary survey on a slope instability case within a coastal region, and its potential merits must be substantiated through broader validation efforts

Riverside Landslide Susceptibility Overview: Leveraging Artificial Neural Networks and Machine Learning in Accordance with the United Nations (UN) Sustainable Development Goals

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, resilience, and disaster risk reduction. Through the integration of ANNs in riverside landslide susceptibility assessments, in line with the SDGs, this review aims to advance our knowledge and understanding of this field. By providing insights into the effectiveness of ANNs and their alignment with the SDGs, this research contributes to the development of improved risk management strategies, sustainable urban planning, and resilient communities in the face of riverside landslides

The predictive model for COVID‑19 pandemic plastic pollution by using deep learning method

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

An Experimental Study for Swelling Effect on Repairing of Cracks in Fine-Grained Clayey Soils

Earth-dam failure starts with cracking in the clay core, and this cracking is not easy to detect and prevent. Therefore, swellable clay is a feasible solution, which helps to close the cracks automatically based on the self-healing process. The presented study utilizes experimental procedures to analyze the swelling behavior of fine-grained clayey soils to prevent structural failure regarding crack generations. In this regard, the clayey materials were modified using Kaolin and Bentonite mixed with various weight percentages (2.5, 5.0, 7.5, 10.0, and 12.5%) and extracted the geotechnical characteristics of the studied soils, which included 90 specimens and 85 tests, such as physical properties, consolidation, particle-size analysis, hydrometry, Atterberg limits, compaction, odometer, and pinhole. The experimental results revealed that the swelling of the Bentonite is more than Kaolin satisfied for self-healing features in clayey soils. Regarding the numerous swelling tests, Bentonite provides optimum results (attained 10%) compared to Kaolin. As a verification procedure, the pinhole test was performed on samples, which revealed that Bentonite was dominant in controlling the water flow through the samples