Soil Liquefaction and Its Evaluation Based on SPT by Soft-Computing Techniques

Kumar, Vijay; Rawat, Anupam

doi:10.20319/mijst.2017.32.316327

Cited by 4 publications

(3 citation statements)

References 4 publications

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“…An ANFIS has the ability to learn from data, such as that possessed by an artificial neural network. Kumar and Rawat (2017) proposed soil liquefaction and its evaluation based on SPT by soft-computing techniques. ANN &ANFIS techniques have been adapted to the development of soft computing models.…”

Section: Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

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A Liquefaction Study Using ENN, CA, and Biogeography Optimized-Based ANFIS Technique

Umar

Kumari

Samui

et al. 2021

International Journal of Applied Metaheuristic Computing

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In any construction projects,assessment of liquefaction potential induced due to seismic excitation during earthquake is a critical concern.The objective of present model development is to classify and assess liquefaction potential of soil.This paper addresses Emotional Neural Network(ENN), Cultural Algorithm(CA) and biogeography optimized(BBO) based adaptive neuro-fuzzy inference system (ANFIS) for liquefaction study.The performance of neural emotional network and cultural algorithm has been also discussed. BBO-ANFIS combines the biogeography features to optimize the ANFIS parameters to achieve higher prediction accuracy.The model is trained with case history of liquefaction databases.Two parameters are used as input such as the cyclic stress ratio and standard penetration test (SPT) value.The performance of these models was assessed using different indexes i.e. sensitivity, specificity, FNR, FPR and accuracy rate.The performance of all models is compared.Among the models, the BBO-ANFIS model has been outperformed and can be adopted as new reliable technique for liquefaction study.

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Section: Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

“…Samui & Sitharam (2011), A. Shahri (2016), Kumar & Rawat (2017) assessed and predicted the liquefaction potential using different artificial neural network models. Umar et al(2018) have been employed the deterministic and probabilistic study of liquefaction for many areas in Bihar.…”

Section: Introductionmentioning

confidence: 99%

A Liquefaction Study Using ENN, CA, and Biogeography Optimized-Based ANFIS Technique

Umar

Kumari

Samui

et al. 2021

International Journal of Applied Metaheuristic Computing

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“…Clayey content <15-20% of the particles smaller than 0.005mm by weight and ratio of water content to liquid limit >0.9 [4,10]. The method which was proposed by Wang (1979), is slightly revised by the Seed (1982), by considering plasticity characteristics of clayey soils, which was not considered by the Wang studies earlier [4,11]. Many researchers all over the world, stated that clayey soil could be susceptible to liquefaction if the soil deposit meets the following criteria [4,11,12].…”

Section: Introductionmentioning

confidence: 99%

Quantification of Liquefaction Triggering Susceptibility: Using Geological, Geomorphological and Geotechnical Characteristics

M¹

2020

GEOMATE

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The damages patterns from the Bhuj earthquake (2001,7.6 M w ) observed within the 250km radial distance from the epicenter due to regional surface and sub-surfaces settings. In order to estimate the damage level from future earthquakes in the current study, an attempted to evaluate the various compositional aspects in the fields of geological, geomorphological and geotechnical characterization of the Amaravati capital region, Andhra Pradesh (AP) state, India. The selected study area lies between latitudes of 16°01" N to 16°09" N and longitudes of 80°00" E to 80°08" E. The results from all the aspects presented in the form of thematic maps, developed by using Geological Information System (GIS). The various types of data like spatial data (satellite image), attributes data and field data collected during the study. To characterize the soils within the study area, soil samples collected from 250 boreholes by conducting the Standard Penetration Test (SPT) at different locations within the study area. In addition to this, basic laboratory tests like index properties and grain size analysis were conducted to identify the nature of soils and to classify the soils according to the size of the grain. The obtained laboratory results compared with existing methodologies like Chinese Criteria by Wang (1979), Seed (1982 and modified Chinese Criteria by Andrews & Martin (2000). Based on the results, liquefaction prone areas identified in the study area and depending upon the susceptibility to liquefaction entire study area has been divided into three groups namely i) susceptible ii) partially susceptible iii) not susceptible.

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Why “AI” models for predicting soil liquefaction have been ignored, plus some that shouldn’t be

Maurer

Sanger

2023

Earthquake Spectra

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Soil liquefaction remains an important and interesting problem that has attracted the development of enumerable prediction models. Increasingly, these models are utilizing algorithmic learning or “artificial intelligence” (AI). The rapid growth of AI in the liquefaction literature is unsurprising, given its ease of implementation and potential advantages over traditional statistical methods. However, AI liquefaction models have been widely ignored by practitioners and researchers alike; the objective of this article is to investigate “why?” Through a sample review of 75 publications, we identify several good reasons. Namely, these models frequently (1) are not compared to state-of-practice models, making it unclear why they should be adopted; (2) depart from best practices in model development; (3) use AI in ways that may not be useful; (4) are presented in ways that overstate their complexity and make them unapproachable; and (5) are discussed but not actually provided, meaning that no one can use the models even if they wanted to. These prevailing problems must be understood, identified, and remedied, but this does not mean that AI itself is problematic or that all prior efforts have been without merit or utility. Instead, understanding these recurrent shortcomings can help improve the direction and perceptions of this growing body of work. Toward this end, we highlight papers that are generally free from these shortcomings, and which demonstrate applications where AI is more likely to provide value in the near-term: permitting new modeling approaches and potentially improving predictions of liquefaction phenomena.

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Soil Liquefaction and Its Evaluation Based on SPT by Soft-Computing Techniques

Abstract: Abstract

Cited by 4 publications

References 4 publications

A Liquefaction Study Using ENN, CA, and Biogeography Optimized-Based ANFIS Technique

A Liquefaction Study Using ENN, CA, and Biogeography Optimized-Based ANFIS Technique

Quantification of Liquefaction Triggering Susceptibility: Using Geological, Geomorphological and Geotechnical Characteristics

Why “AI” models for predicting soil liquefaction have been ignored, plus some that shouldn’t be

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