SPT-based liquefaction potential assessment by relevance vector machine approach

Karthikeyan, J.; Kim, Dookie; Aiyer, Bhairevi G.; Samui, Pijush

doi:10.1080/19648189.2013.781546

Cited by 13 publications

(2 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the high cost and difficulty of collecting high-quality in-situ soil samples and testing granular soils, to evaluate soil liquefaction potential, geotechnical engineers normally adopt in situ testing or semi-empirical equations (liquefaction boundary curves) such as standard penetration tests (SPT) and cone penetration tests (CPT) based on machine learning methods [132]. For example, Karthikeyan et al [133] used a correlation vector machine (RVM) based on SPT to determine the liquefaction trend of soil and compared it with an artificial neural network (ANN). Xue et al [134] proposed a hybrid model based on a combination of support vector machines (SVM) and particle swarm optimization (PSO), and tested the robustness of the model based on CPT field data.…”

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

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

show abstract

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

“…Apart from estimating compressive strength, the SVM is used for several applications in engineering, such as traffic sign detection [33], modelling soil pollution [34], predicting daily flow of river [35] predicting elastic modulus of concrete [36], modelling landslide susceptibility [37], air balancing for ventilation systems [38] and predicting shear force for base isolation device [39,40]. Its other successful applications include for example, classifying building information modelling elements [41], system reliability analysis of slopes [42], estimation of concrete expansion caused by alkali-aggregate reaction [43], damage detection in a three-story frame structure [44], prediction of lateral load capacity of piles [45], crack inspection for aircraft skin [46] and assessing liquefaction potential [47].…”

Section: Introductionmentioning

confidence: 99%

Support vector machine for determining the compressive strength of brick-mortar masonry using NDT data fusion (case study: Kharagpur, India)

2019

View full text Add to dashboard Cite

The accurate prediction of compressive strength of brick-mortar masonry walls is crucial for the damage assessment of load-bearing masonry constructions. Direct tests conducted to estimate compressive strength involve core drilling and are expensive. To estimate compressive strength, several indirect test parameters can be used as empirical predictors. Nondestructive tests can be rapidly executed, can significantly reduce repair costs, and can increase the knowledge level of buildings by indirectly estimating compressive strength. This study aimed to determine the compressive strength of masonry construction by using support vector machines (SVMs). Input variables of the model are test data obtained from the nondestructive and destructive testing of 44 masonry wallettes cast in a laboratory for evaluating the compressive strength of brick (f b), rebound hammer number, and ultrasonic pulse velocity, while the compressive strength of the wall (f c) is output. The final results obtained using an SVM model are validated for a masonry building in Kharagpur, India through experimental testing, and these results are compared with other established empirical relationships. The results indicate that the SVM can be efficiently used to predict the compressive strength of brick-mortar masonry.

show abstract

Application of Soft Computing in Geotechnical Earthquake Engineering

Samui

2021

Latest Developments in Geotechnical Earthquake Engineering and Soil Dynamics

View full text Add to dashboard Cite

SPT-based liquefaction potential assessment by relevance vector machine approach

Cited by 13 publications

References 17 publications

Recent Advances of Deep Learning in Geological Hazard Forecasting

Recent Advances of Deep Learning in Geological Hazard Forecasting

Support vector machine for determining the compressive strength of brick-mortar masonry using NDT data fusion (case study: Kharagpur, India)

Application of Soft Computing in Geotechnical Earthquake Engineering

Contact Info

Product

Resources

About