Seismic Liquefaction Resistance Based on Strain Energy Concept Considering Fine Content Value Effect and Performance Parametric Sensitivity Analysis

Wan, Xusheng; Lü, Jian; Hu, Jilei; Ahmad, Mahmood; Tahmoorian, Farzaneh

doi:10.32604/cmes.2022.022207

Cited by 4 publications

(3 citation statements)

References 78 publications

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“…Seismic liquefaction [129,130] is the process by which an originally stable, predominantly solidlike sandy soil layer is transformed into an unstable mixed liquid under seismic action, resulting in a reduction in the support of the sandy soil layer. Before the earthquake, the saturated sandy soil body below the water table carried the weight of the soil and buildings above.…”

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

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“…In this research, we introduce a deep learning framework that leverages a cascaded Convolutional Neural Network (CNN) for tackling regression-based challenges [33][34][35][36]. Our CNN design incorporates six bi-dimensional convolutional strata, interspersed with three max-pooling segments and terminates in three densely interconnected layers, as detailed in Le Cun et al, 1998 [37][38][39][40].…”

Section: A Cnn Architecturementioning

confidence: 99%

Deep Convolutional Neural Network for Accurate Prediction of Seismic Events

Turarbek,

Bektemesov,

Ongarbayeva

et al. 2023

IJACSA

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In recent years, the realm of seismology has witnessed an increased integration of advanced computational techniques, seeking to enhance the precision and timeliness of earthquake predictions. The paper titled "Deep Convolutional Neural Network and Machine Learning Enabled Framework for Analysis and Prediction of Seismic Events" embarks on an ambitious exploration of this interstice, marrying the formidable prowess of Deep Convolutional Neural Networks (CNNs) with an array of machine learning algorithms. At the forefront of our investigation is the Deep CNN, known for its unparalleled capability to process spatial hierarchies and multi-dimensional seismic data. Accompanying this neural behemoth is LightGBM, a gradient boosting framework that offers superior speed and performance, especially with voluminous datasets. Additionally, conventional neural networks, noted for their adeptness in pattern recognition, offer a robust method to gauge the intricacies of seismic data. Our exploration doesn't halt here; the research delves deeper with Random Forest and Support Vector Machines (SVM), both renowned for their resilient performance in classification tasks. By amalgamating these diverse methodologies, this research crafts a multifaceted and synergistic framework. The culmination is a sophisticated tool poised to not only discern the minutiae of seismic activities with heightened accuracy but to predict forthcoming events with a degree of certainty previously deemed elusive. In this era of escalating seismic activities, our research offers a timely beacon, heralding a future where communities are better equipped to respond to the Earth's capricious tremors.

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“…Hu et al (2022) developed a hybrid Bayesian network (BN) model to predict liquefaction caused by earthquakes. The model is based on shear wave velocity (𝑉𝑠) and builds upon previous studies by Hu and Liu (2019); Hu (2021b); Hu et al (2022); Pirhadi et al (2023). In their study, used Cetin et al (2004) database of case histories to establish a new collection of probabilistic and deterministic connections.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Soil Liquefaction Potential Using Genetic Programming Using a Probability-Based Approach

Reddy,

Gupta,

Sahu

2024

Iran J Sci Technol Trans Civ Eng

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Soil liquefaction is a substantial seismic hazard that endangers both human life and infrastructure. This research specifically examines the occurrence of soil liquefaction events in past earthquakes, with a special emphasis on the 1964 Niigata, Japan and 1964 Alaska, USA earthquakes. These occurrences were important achievements in the comprehension of harm caused by liquefaction. Geotechnical engineers often use in-situ experiments, such as the standard penetration test (SPT) to evaluate the likelihood of liquefaction. The attraction for this option arises from the difficulties connected in acquiring undisturbed samples of superior quality, as well as the related expenses. Geotechnical engineering specialists choose the deterministic framework for liquefaction assessment because of its clear mathematical approach and low needs for data, time, and effort. This work emphasises the need of integrating probabilistic and reliability methodologies into the design process of crucial life line structures to enable well-informed risk-based decision-making. The objective of this project is to create models that use deterministic, probabilistic, and reliability-based methods to evaluate the likelihood of soil liquefaction. The work presents a new equation that combines Bayes conditional probability with Genetic Programming (GP). and also in study is to identify the most suitable method for liquefaction analysis based on factor of safety and Performance Fitness Error Metrics (PFEMs), Rank analysis, Gini index, etc. The information provided in study data include soil and seismic characteristics, including the corrected blow count (𝑁1)60𝑐𝑠, fines content (FC), mean grain size (𝐷50), peak horizontal ground surface acceleration (𝑎𝑚𝑎𝑥), earthquake magnitude (M), and 𝐶𝑆𝑅7.5. The parameters are derived from the SPT measurements conducted at many global locations, together with field performance observations (LI) and probability of liquefaction has been assessed through the use of Gini Index (GI). A comparison was made between the novel methodology and the techniques proposed by Juang et al. (2002), Toprak et al. (1999), and Idriss and Boulanger (2006 status of case history data using Performance Fitness Error Metrices. The comparison included employing a confusion matrix for binary classification and doing a score analysis based on factor ranking. The proposed model exhibited superior performance, as the outputs of the constructed model increased for all positive factors and decreased for negative indicators.

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Seismic Liquefaction Resistance Based on Strain Energy Concept Considering Fine Content Value Effect and Performance Parametric Sensitivity Analysis

Cited by 4 publications

References 78 publications

Recent Advances of Deep Learning in Geological Hazard Forecasting

Recent Advances of Deep Learning in Geological Hazard Forecasting

Deep Convolutional Neural Network for Accurate Prediction of Seismic Events

Assessment of Soil Liquefaction Potential Using Genetic Programming Using a Probability-Based Approach

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