Liquefaction behavior of Indo-Gangetic region using novel metaheuristic optimization algorithms coupled with artificial neural network

Ghani, Sufyan; Kumari, Sudesh

doi:10.1007/s11069-021-05165-y

Cited by 29 publications

(3 citation statements)

References 74 publications

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“…In addition, plasticity index (PI) has considerable influence on measuring the liquefaction behavior of fine-grained soils on the liquefaction sensitivity of highly plastic soils. Ghani [137,138] and others compared Culture Algorithm (CA), Firefly Algorithm (FA), Genetic Algorithm (GA), Gray Wolf Optimizer (GWO), Particle Swarm Optimization (PSO) and Gradient-based optimizer (GBO) combined with artificial neural network for soil liquefaction assessment and concluded that PI and GBO based ANN models are a promising new tool and can help geotechnical engineers to be able to estimate the occurrence of liquefaction in the early stages of engineering projects.…”

Section: Prediction Of Seismic Liquefactionmentioning

confidence: 99%

Recent Advances of Deep Learning in Geological Hazard Forecasting

Wang¹,

Sun²,

Chen³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Geological hazard is an adverse geological condition that can cause loss of life and property. Accurate prediction and analysis of geological hazards is an important and challenging task. In the past decade, there has been a great expansion of geohazard detection data and advancement in data-driven simulation techniques. In particular, great efforts have been made in applying deep learning to predict geohazards. To understand the recent progress in this field, this paper provides an overview of the commonly used data sources and deep neural networks in the prediction of a variety of geological hazards. KEYWORDSGeological hazard; deep learning; neural networks; geohazard data sources; earthquake; volcanic 1 Introduction Geological hazards are caused by endogenous and exogenous geological processes or anthropogenic factors on earth [1,2], which encompass a broad range of abnormal stratigraphic activity or extreme changes in geological environments, such as landslides, avalanches, debris flows, earthquakes, etc. Geological hazards pose a serious threat to human life and property [3]. Only by accurately forecasting the occurrence of geohazards can emergency measures be devised to mitigate the damage caused by the impending disaster. Geological hazard forecasting refers to the use of logical reasoning, numerical simulation, and comprehensive analysis based on historical geological hazard activity patterns, formation conditions, occurrence mechanisms, and other factors to speculate and assess the development and changes of geological hazards and the possible degree of danger and damage in a certain period in the future. Geohazard forecasting has attracted tremendous attention nowadays, particularly considering natural resource scarcity, environmental degradation, population expansion, sustainable development, and world economic integration. Many efforts have been made in forecasting geological hazards in the past decades. The "3S technology", including Global Position System (GPS), Geographic Information System (GIS) [4], and Remote Sensing (RS), has been widely applied in the field of geological hazard forecasting [5]. The

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Section: Prediction Of Seismic Liquefactionmentioning

confidence: 99%

Recent Advances of Deep Learning in Geological Hazard Forecasting

Wang¹,

Sun²,

Chen³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

show abstract

“…Geotechnical engineering has seen significant advancements in modeling techniquesfrom simple analytical methods to complex numerical modeling techniques. Initially, mathematical equations were used to predict soil behavior, but recent research has shown that machine learning is a reliable method for predicting geotechnical parameters, [28][29][30], particularly liquefaction [31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Estimating Liquefaction Susceptibility Using Machine Learning Algorithms with a Case of Metro Manila, Philippines

Galupino

Dungca

2023

Applied Sciences

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Soil liquefaction is a phenomenon that can occur when soil loses strength and behaves like a liquid during an earthquake. A site investigation is essential for determining a site’s susceptibility to liquefaction, and these investigations frequently generate project-specific geotechnical reports. However, many of these reports are frequently stored unused after construction projects are completed. This study suggests that when these unused reports are consolidated and integrated, they can provide valuable information for identifying potential challenges, such as liquefaction. The study evaluates the susceptibility of liquefaction by considering several geotechnical factors modeled by machine learning algorithms. The study estimated site-specific characteristics, such as ground elevation, groundwater table elevation, SPT N-value, soil type, and fines content. Using a calibrated model represented by an equation, the investigation determined several soil properties, including the unit weight and peak ground acceleration (PGA). The study estimated PGA using a linear model, which revealed a significant positive correlation (R2 = 0.89) between PGA, earthquake magnitude, and distance from the seismic source. On the Marikina West Valley Fault, the study also assessed the liquefaction hazard for an anticipated 7.5 M and delineated a map that was validated by prior studies.

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“…Geotechnical researchers have demonstrated that machine learning (ML)-based methods can effectively solve complex geotechnical problems (Karthikeyan and Samui,2014;Fahim et al, 2022;Ghani and Kumari, 2022;Phoon and Zhang, 2022;Tehrani et al, 2022;Dehghanbanadaki, 2021). Arti cial intelligence (AI)-based solutions have also gained popularity recently for solving geotechnical problems (Uncuoglu et al, 2022;Baghbani et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Enhanced Soil Liquefaction Potential Estimation using Machine Learning and Web-Based Platform

Vasegh

Dehghanbanadaki

Motamedi

2023

Preprint

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In this study, a new web-based platform was developed for fast estimation of soil liquefaction potential (SLP). The geotechnical results from 47 boreholes in the north of Iran were collected over three years to create an estimator model. The dataset included information on SPT, soil type, strength parameters, and water content. Python libraries Pycaret and Gradio were used to develop the model for SLP. A set of pipeline codes were applied to base classifiers, including 13 different machine learning models such as the Ada boost classifier (ad), decision tree classifier (dt), gradient boosting classifier (gb), the k-neighbors classifier (knn), light gradient boosting machine (lightgbm) and random forest classifier (rf). The results show that the lightgbm model outperformed the other applied machine learning classifiers with accuracy = 0.946, AUC = 0.982, and F1-score = 0.9. The proposed model was then used as the primary element of the web-based application, providing a helpful tool for geotechnical engineers to determine SLP.

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Liquefaction behavior of Indo-Gangetic region using novel metaheuristic optimization algorithms coupled with artificial neural network

Cited by 29 publications

References 74 publications

Recent Advances of Deep Learning in Geological Hazard Forecasting

Recent Advances of Deep Learning in Geological Hazard Forecasting

Estimating Liquefaction Susceptibility Using Machine Learning Algorithms with a Case of Metro Manila, Philippines

Enhanced Soil Liquefaction Potential Estimation using Machine Learning and Web-Based Platform

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