Liquefaction Resistance Evaluation of Soils using Artificial Neural Network for Dhaka City, Bangladesh

Abstract Soil liquefaction resistance evaluation is an important site investigation for seismically active areas. To minimize the loss of life and property, liquefaction hazard analysis is a prerequisite for seismic risk management and development of an area. Liquefaction potential index (LPI) is widely used to determine the severity of liquefaction quantitatively and spatially. LPI is estimated from the factor of safety (FS) of liquefaction that is the ratio of cyclic resistance ratio (CRR) to cyclic stress ratio (CSR) calculated applying simplified procedure. Artificial neural network (ANN) algorithm has been used in the present study to predict CRR directly from the normalized standard penetration test blow count (SPT-N) and near-surface shear wave velocity (Vs) data of Dhaka City. It is observed that ANN model have generated accurate CRR data. Three liquefaction hazard zones are identified in Dhaka City on the basis of the cumulative frequency (CF) distribution of the LPI of each geological unit. The liquefaction hazard maps have been prepared for the city using the liquefaction potential index (LPI) and its cumulative frequency (CF) distribution of each liquefaction hazard zone. The CF distribution of the SPT-N based LPI indicates that 15%, 53%, and 69% of areas, whereas the CF distribution of the Vs based LPI indicates that 11%, 48%, and 62% of areas of Zone 1, 2, and 3, respectively, show surface manifestation of liquefaction for a scenario earthquake of moment magnitude, Mw 7.5 with a peak horizontal ground acceleration (PGA) of 0.15 g.

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Predicting the liquefaction potential of soil layers in Tabriz city via artificial neural network analysis

SN Applied Sciences ◽

10.1007/s42452-021-04704-3 ◽

2021 ◽

Vol 3 (7) ◽

Author(s):

Mohammad Alizadeh Mansouri ◽

Rouzbeh Dabiri

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Soil Liquefaction ◽

Data Sets ◽

Liquefaction Potential ◽

Advantages And Disadvantages ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Prediction Approach ◽

Empirical Approaches

AbstractSoil liquefaction is a phenomenon through which saturated soil completely loses its strength and hardness and behaves the same as a liquid due to the severe stress it entails. This stress can be caused by earthquakes or sudden changes in soil stress conditions. Many empirical approaches have been proposed for predicting the potential of liquefaction, each of which includes advantages and disadvantages. In this paper, a novel prediction approach is proposed based on an artificial neural network (ANN) to adequately predict the potential of liquefaction in a specific range of soil properties. To this end, a whole set of 100 soil data is collected to calculate the potential of liquefaction via empirical approaches in Tabriz, Iran. Then, the results of the empirical approaches are utilized for data training in an ANN, which is considered as an option to predict liquefaction for the first time in Tabriz. The achieved configuration of the ANN is utilized to predict the liquefaction of 10 other data sets for validation purposes. According to the obtained results, a well-trained ANN is capable of predicting the liquefaction potential through error values of less than 5%, which represents the reliability of the proposed approach.

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Risk-based liquefaction potential evaluation using standard penetration tests

Canadian Geotechnical Journal ◽

10.1139/t00-064 ◽

2000 ◽

Vol 37 (6) ◽

pp. 1195-1208 ◽

Cited By ~ 44

Author(s):

C Hsein Juang ◽

Caroline J Chen ◽

Tao Jiang ◽

Ronald D Andrus

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Limit State ◽

State Function ◽

Limit State Function ◽

Liquefaction Potential ◽

New Approach ◽

Mapping Functions ◽

Artificial Neural ◽

Penetration Tests

In this paper, a new approach is presented for developing a liquefaction limit state function, which defines a boundary that separates liquefaction from no-liquefaction occurrence. The new approach is developed using a database consisting of 243 field liquefaction performance cases at sites where standard penetration tests (SPT) had been conducted. This database is first used to train and test an artificial neural network for predicting the occurrence of liquefaction or no liquefaction. The successfully trained neural network is then used to establish a liquefaction limit state function. Based on the developed limit state function, mapping functions that relate calculated factors of safety to probability of liquefaction are established. The established mapping functions form a basis for the development of a risk-based chart for liquefaction potential evaluation.Key words: probability, risk-based design, liquefaction potential, SPT, artificial neural network.

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LPI Based Earthquake Induced Soil Liquefaction Susceptibility Assessment at Probashi Palli Abasan Project Area, Tongi, Gazipur, Bangladesh

Journal of Scientific Research ◽

10.3329/jsr.v10i2.34225 ◽

2018 ◽

Vol 10 (2) ◽

pp. 105-116

Author(s):

A. H. Farazi ◽

N. Ferdous ◽

A. S. M. M. Kamal

Keyword(s):

Threshold Value ◽

Flood Plain ◽

Standard Penetration Test ◽

Soil Liquefaction ◽

Cumulative Frequency ◽

Liquefaction Potential ◽

Project Area ◽

Potential Index ◽

Liquefaction Hazard ◽

Simplified Procedure

This study aims at evaluation of seismic soil liquefaction hazard potential at Probashi Palli Abasan Project area of Tongi, Gazipur, exploiting standard penetration test (SPT) data of 15 boreholes, following Simplified Procedure. Liquefaction potential index (LPI) of each borehole was determined and then cumulative frequency distribution of clustered LPI values of each surface geology unit was determined assuming cumulative frequency at LPI = 5 as the threshold value for liquefaction initiation. By means of geotechnical investigation two surface geological units—Holocene flood plain deposits, and Pleistocene terrace deposits were identified in the study area. We predicted that 14% and 24% area of zones topped by Pleistocene terrace deposits and zones topped by Holocene flood plain deposits, respectively, would exhibit surface manifestation of liquefaction as a result of 7 magnitude earthquake. The engendered hazard map also depicts site specific liquefaction intensity through LPI values of respective boreholes, and color index, which was delineated by mapping with ArcGIS software. Very low to low, and low to high liquefaction potential, respectively, was found in the areas covered by Pleistocene terrace deposits and Holocene flood plain deposits. LPI values of both units are such that sand boils could be generated where LPI > 5.

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Assessment and Prediction of Liquefaction Potential Using Different Artificial Neural Network Models: A Case Study

Geotechnical and Geological Engineering ◽

10.1007/s10706-016-0004-z ◽

2016 ◽

Vol 34 (3) ◽

pp. 807-815 ◽

Cited By ~ 10

Author(s):

Abbas Abbaszadeh Shahri

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Network Models ◽

Neural Network Models ◽

Liquefaction Potential ◽

Artificial Neural ◽

Artificial Neural Network Models

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Appraising cone penetration test based liquefaction resistance evaluation methods: artificial neural network approach

Canadian Geotechnical Journal ◽

10.1139/t99-011 ◽

1999 ◽

Vol 36 (3) ◽

pp. 443-454 ◽

Cited By ~ 48

Author(s):

C Hsein Juang ◽

Caroline Jinxia Chen ◽

Yong-Ming Tien

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Success Rate ◽

Sandy Soils ◽

Cone Penetration Test ◽

Penetration Test ◽

Cone Penetration ◽

Liquefaction Resistance ◽

Ann Models ◽

Artificial Neural

This paper evaluates and compares two comprehensive cone penetration test (CPT) based methods for evaluating liquefaction resistance of sandy soils. The comparison is made based on the results obtained from artificial neural network (ANN) analyses. Two methods are compared, one by Olsen and his colleagues at the Waterways Experiment Station and one by Robertson and his colleagues at the University of Alberta. ANN models are created to approximate the two CPT-based methods so that they can easily be compared using a large database. The results show that ANN models can approximate both Robertson and Olsen methods well, and that both methods are fairly accurate in predicting liquefaction resistance. The Robertson method has a success rate of 89% in predicting liquefied cases, a success rate of 76% in predicting nonliquefied cases, and an overall success rate of 84%. The success rates for the Olsen method are 68%, 89%, and 77%, respectively, in predicting liquefied cases, nonliquefied cases, and all cases. Both methods are considered accurate in predicting liquefaction resistance of sandy soils. The Robertson method is slightly more accurate than the Olsen method. The issue of the propagation of potential uncertainties in the soil parameters and solution model is also discussed.

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