Liquefaction potential analysis using hybrid multi-objective intelligence model

Shahri, Abbas Abbaszadeh; Moud, Fardad Maghsoudi

doi:10.1007/s12665-020-09173-2

Cited by 10 publications

(2 citation statements)

References 65 publications

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“…Referring to available literatures, the applicability of ML and hybridized models successfully in various field of engineering and natural disasters (Abbaszadeh Shahri, Asheghi et al, 2021;Abbaszadeh Shahri, Kheiri et al, 2021;Abbaszadeh Shahri & Maghsoudi Moud, 2020, psychometric analysis (Orrù et al, 2020;Rosenbusch et al, 2021), medical and pharmaceutics (Kan, 2017;Réda et al, 2020;Vamathevan et al, 2019), incorporating with graph theory (Abderrahim et al, 2014) as well as social sciences (N. C. Chen et al, 2018;Grimmer et al, 2021;Hindman, 2015) have been approved.…”

Section: Introductionmentioning

confidence: 99%

Battling COVID-19 using machine learning: A review

Chadaga

Prabhu

et al. 2021

Cogent Engineering

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Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2) known as Coronavirus surfaced in late 2019. It turned out to be a life-threatening disease and is causing chaos all around the world. The World Health Organisation (WHO) declared it a pandemic in March 2020. To handle COVID-19 related problems, research in many areas of science was introduced. Machine learning (ML), being one of the most successful stories in recent times is widely used to solve a variety of problems in our everyday life. Here, an overview of machine learning that tackles the pandemic is discussed in the beginning. Various datasets related to COVID-19 are also explored. Diagnosis of this viral disease using CT-Scans, X-ray images, sound analysis and blood tests using machine learning are presented in-depth. Drug and vaccine development using machine learning for COVID-19 are also discussed. Pandemic management and control were also examined. The main objective of this paper is to conduct a systematic review of machine learning applications that fight the deadly virus. This paper helps the researchers to understand and analyse the data trends related to COVID-19 and also

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Section: Introductionmentioning

confidence: 99%

Battling COVID-19 using machine learning: A review

Chadaga

Prabhu

et al. 2021

Cogent Engineering

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“…In another study, authors Das and Muduli [ 21 ] used Genetic Programming (GP) in an attempt to predict soil liquefaction potential based on CPT data obtained after the Chi-Chi earthquake, Taiwan. In addition, Abbaszadeh Shahri and Maghsoudi Moud [ 22 ] developed two Feedforward Neural Network (FNN) models, namely, ICA-MOGFFN and MOGFNN, to determine soil liquefaction potential and showed good accuracy. In general, studies using the machine learning model in general and neural network models, in particular, have achieved certain success in assessing the liquefaction capacity of soils.…”

Section: Introductionmentioning

confidence: 99%

Application of Feedforward Neural Network and SPT Results in the Estimation of Seismic Soil Liquefaction Triggering

Pham

2021

Computational Intelligence and Neuroscience

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Soil liquefaction is a dangerous phenomenon for structures that lose their shear strength and soil resistance, occurring during seismic shocks such as earthquakes or sudden stress conditions. Determining the liquefaction and nonliquefaction capacity of soil is a difficult but necessary job when constructing structures in earthquake zones. Usually, the possibility of soil liquefaction is determined by laboratory tests on soil samples subjected to dynamic loads, and this is time-consuming and costly. Therefore, this study focuses on the development of a machine learning model called a Forward Neural Network (FNN) to estimate the activation of soil liquefaction under seismic condition. The database is collected from the published literature, including 270 liquefaction cases and 216 nonliquefaction case histories under different geological conditions and earthquakes used for construction and confirming the model. The model is built and optimized for hyperparameters based on a technique known as random search (RS). Then, the L2 regularization technique is used to solve the overfitting problem of the model. The analysis results are compared with a series of empirical formulas as well as some popular machine learning (ML) models. The results show that the RS-L2-FNN model successfully predicts soil liquefaction with an accuracy of 90.33% on the entire dataset and an average accuracy of 88.4% after 300 simulations which takes into account the random split of the datasets. Compared with the empirical formulas as well as other machine learning models, the RS-L2-FNN model shows superior performance and solves the overfitting problem of the model. In addition, the global sensitivity analysis technique is used to detect the most important input characteristics affecting the activation prediction of liquefied soils. The results show that the corrected SPT resistance (N1)60 is the most important input variable, affecting the determination of the liquefaction capacity of the soil. This study provides a powerful tool that allows rapid and accurate prediction of liquefaction based on several basic soil properties.

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On the evaluation of internal stability of gap-graded soil: a status quo review

Yan

2022

Nat Hazards

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Liquefaction potential analysis using hybrid multi-objective intelligence model

Cited by 10 publications

References 65 publications

Battling COVID-19 using machine learning: A review

Battling COVID-19 using machine learning: A review

Application of Feedforward Neural Network and SPT Results in the Estimation of Seismic Soil Liquefaction Triggering

On the evaluation of internal stability of gap-graded soil: a status quo review

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