Risk-based liquefaction potential evaluation using standard penetration tests

2000 ◽  
Vol 37 (6) ◽  
pp. 1195-1208 ◽  
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.

Loader Boom Reliability Analysis with Artificial Neural Network Method

2013 ◽  
Vol 838-841 ◽  
pp. 250-253
Author(s):  
Li Rong Sha ◽  
Yue Yang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Reliability Analysis ◽  
Limit State ◽  
Time History ◽  
State Function ◽  
Neural Network Method ◽  
Network Method ◽  
Artificial Neural Network Method ◽  
Artificial Neural

The artificial neural network is used to solve the reliability analysis of the engineering structure. When the limit state function of structure is highly complex or with nonlinearity, it is time-consuming or cumbersome to carry out reliability analysis with traditional methods. The artificial neural network response surface method is adopted to analyze the fatigue reliability of loader boom, the working process of loader machine is analyzed with FEM software and analytical method, the stress-time history and strain-time history of loader boom are schematized with rain-flow algorithm, consequently the fatigue life analysis on the structure can be carried out with local stress-strain method. The artificial neural network method is used to fit the performance function as well as its derivatives, so as to calculate the reliability of the structure. The numerical example results show that the proposed method has capability of solving industrial-scale reliability problems.

New Approach for 3D Mesh Retrieval Using Artificial Neural Network and Histogram of Features

2018 ◽  
Vol 10 (2) ◽  
pp. 84-94 ◽  
Author(s):  
M. Pershina ◽  
V.S. Bouksim ◽  
K. Arhid ◽  
F.R. Zakani ◽  
M. Aboulfatah ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
New Approach ◽  
3D Mesh ◽  
Artificial Neural

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

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.

A New Reliability Evaluation Algorithm Using Response Surface Methodology Combined With Space Mapping Technique

2006 ◽  
Author(s):  
Lixin Zhang ◽  
Zhijun Jian ◽  
Zhaohui Xu
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Limit State ◽  
Space Mapping ◽  
Mapping Technique ◽  
State Function ◽  
Limit State Function ◽  
New Approach ◽  
Successive Response ◽  
Evaluation Algorithm

A new method is proposed to tackle the huge computation cost involved in Successive Response Surface Methodology applied to the reliability analysis, in which Space Mapping technique is combined with Response Surface Methodology. While the new approach is performed, the limit state function is only fitted at the first iteration; at other iterations Space Mapping technique is employed to map the original limit state function into the new ones. Experimental design, corresponding model evaluations and response surface fitting of the limit state function are not done repetitively as what we do while SRSM is used, which leads to the great cutting down of computational efforts.

On the usage of acoustic properties combined with an artificial neural network – A new approach of determining presence of dairy fouling

2011 ◽  
Vol 103 (4) ◽  
pp. 449-456 ◽  
Author(s):  
Eva Wallhäußer ◽  
Walid B. Hussein ◽  
Mohamed A. Hussein ◽  
Jörg Hinrichs ◽  
Thomas M. Becker
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Acoustic Properties ◽  
New Approach ◽  
Artificial Neural
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