scholarly journals A genetic-algorithm approach for assessing the liquefaction potential of sandy soils

2010 ◽  
Vol 10 (4) ◽  
pp. 685-698 ◽  
Author(s):  
G. Sen ◽  
E. Akyol
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Sandy Soils ◽  
Cone Penetration ◽  
Liquefaction Potential ◽  
Empirical Relations ◽  
Liquefaction Index ◽  
Genetic Algorithm Approach ◽  
Good Relation ◽  
Index Estimation

Abstract. The determination of liquefaction potential is required to take into account a large number of parameters, which creates a complex nonlinear structure of the liquefaction phenomenon. The conventional methods rely on simple statistical and empirical relations or charts. However, they cannot characterise these complexities. Genetic algorithms are suited to solve these types of problems. A genetic algorithm-based model has been developed to determine the liquefaction potential by confirming Cone Penetration Test datasets derived from case studies of sandy soils. Software has been developed that uses genetic algorithms for the parameter selection and assessment of liquefaction potential. Then several estimation functions for the assessment of a Liquefaction Index have been generated from the dataset. The generated Liquefaction Index estimation functions were evaluated by assessing the training and test data. The suggested formulation estimates the liquefaction occurrence with significant accuracy. Besides, the parametric study on the liquefaction index curves shows a good relation with the physical behaviour. The total number of misestimated cases was only 7.8% for the proposed method, which is quite low when compared to another commonly used method.

Evaluating cyclic liquefaction potential using the cone penetration test

1998 ◽  
Vol 35 (3) ◽  
pp. 442-459 ◽  
Author(s):  
P K Robertson ◽  
CE (Fear) Wride
Keyword(s):  
Earthquake Engineering ◽  
Sandy Soils ◽  
Cone Penetration Test ◽  
Soil Liquefaction ◽  
Penetration Test ◽  
Cone Penetration ◽  
Liquefaction Resistance ◽  
Liquefaction Potential ◽  
Engineering Research ◽  
Research Workshop

Soil liquefaction is a major concern for structures constructed with or on sandy soils. This paper describes the phenomena of soil liquefaction, reviews suitable definitions, and provides an update on methods to evaluate cyclic liquefaction using the cone penetration test (CPT). A method is described to estimate grain characteristics directly from the CPT and to incorporate this into one of the methods for evaluating resistance to cyclic loading. A worked example is also provided, illustrating how the continuous nature of the CPT can provide a good evaluation of cyclic liquefaction potential, on an overall profile basis. This paper forms part of the final submission by the authors to the proceedings of the 1996 National Center for Earthquake Engineering Research workshop on evaluation of liquefaction resistance of soils.Key words: cyclic liquefaction, sandy soils, cone penetration test

scholarly journals Hybrid Modeling KMeans – Genetic Algorithms in the Health Care Data

2013 ◽  
Vol 2 (1) ◽  
Author(s):  
Tessy Badriyah
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Health Care ◽  
Genetic Algorithms ◽  
Hybrid Method ◽  
Health Care Data ◽  
Computational Performance ◽  
Genetic Algorithm Approach ◽  
A New Technique ◽  
Published Research

K-Means is one of the major algorithms widely used in clustering due to its good computational performance. However, K-Means is very sensitive to the initially selected points which randomly selected, and therefore it does not always generate optimum solutions. Genetic algorithm approach can be applied to solve this problem. In this research we examine the potential of applying hybrid GA- KMeans with focus on the area of health care data. We proposed a new technique using hybrid method combining KMeans Clustering and Genetic Algorithms, called the “Hybrid K-Means Genetic Algorithms” (HKGA). HKGA combines the power of Genetic Algorithms and the efficiency of K-Means Clustering. We compare our results with other conventional algorithms and also with other published research as well. Our results demonstrate that the HKGA achieves very good results and in some cases superior to other methods.Keywords: Machine Learning, K-Means, Genetic Algorithms, Hybrid KMeans Genetic Algorithm (HGKA).

Predicting liquefaction of tangier soils by using semiempirical approaches and continuum based modelling

2012 ◽  
Vol 3 (2) ◽  
pp. 119-126
Author(s):  
N. Touil ◽  
A. Khamlichi ◽  
P. Dubujet ◽  
A. Jabbouri
Keyword(s):  
Reliability Analysis ◽  
Field Tests ◽  
Sandy Soils ◽  
Historical Earthquakes ◽  
Cone Penetration ◽  
Liquefaction Potential ◽  
Penetration Testing ◽  
One Dimensional ◽  
Cone Penetration Testing ◽  
Semiempirical Approach

Abstract Liquefaction potential of soils under the risk of seism is usually assessed by using correlation formulas that are based on field tests and historical earthquakes databases. These correlations depend on the site where they were derived. To use them for other sites where seismic history is not available, further investigation is needed. In this work, a one-dimensional modelling of liquefaction phenomenon is performed by using DeepSoil software. The soil data required for simulations were obtained from field tests consisting of core sampling and cone penetration testing. Using reliability analysis, the probability of liquefaction was estimated for sandy soils located in the Moroccan city of Tangier. The obtained results were found to be close to predictions due to Juang semiempirical approach.

Application of New Variable Selection Techniques to near Infrared Spectroscopy

1996 ◽  
Vol 4 (1) ◽  
pp. 163-174 ◽  
Author(s):  
Lois G. Weyer ◽  
Steven D. Brown
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Infrared Spectroscopy ◽  
Linear Regression ◽  
Variable Selection ◽  
Near Infrared ◽  
Partial Least Square ◽  
Least Square ◽  
Genetic Algorithm Approach ◽  
Classification And Regression

Two non-traditional variable selection techniques, classification and regression trees (CART) and genetic algorithms (GA), were explored for their application to near infrared spectroscopic calibrations. The results were compared to those of multiple linear regression (MLR) and partial least square (PLS) calibrations. Both numerical comparisons and interpretation of the reasons for the wavelength choices of the different techniques were made. A challenging set of mixtures, containing a low level of an alcohol with a spectrum very similar to one of the major components, was used as a test for the various techniques. The genetic algorithm approach succeeded in locating three wavelengths, which together were capable of generating a model which predicted unknown mixtures very well.

Backcalculation Analysis of Pavement-Layer Moduli Using Genetic Algorithms

1997 ◽  
Vol 1570 (1) ◽  
pp. 134-142 ◽  
Author(s):  
T. F. Fwa ◽  
C. Y. Tan ◽  
W. T. Chan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Performance Evaluation ◽  
Objective Function ◽  
The Other ◽  
Local Minima ◽  
Structural Composition ◽  
Genetic Algorithm Approach ◽  
Solution Surface ◽  
Surface Deflections

Most existing iterative backcalculation programs for pavement layer moduli arrive at their solutions by minimizing an objective function related to the differences between computed and measured surface deflections. Unfortunately, the solution surface of the backcalculation problem of pavement-layer moduli is known to contain many local minima. A potentially good backcalculation procedure would be one that has a strong global search ability to overcome the problem of local minima. The genetic algorithm (GA) is a technique that satisfies this requirement. The development of a backcalculation program known as NUS-GABACK using the genetic-algorithm approach is presented, along with the formulation and operations of the program. A detailed performance evaluation of the GA-based method is made against four other programs by solving five backcalculation problems with different structural composition. It was found that NUS-GABACK performed comparably well against the other programs and demonstrated consistency in the accuracies of backcalculated moduli.

scholarly journals Design Of Fuzzy Rule-Based System Using Genetic Algorithms

2021 ◽  
Vol 23 (11) ◽  
pp. 683-692
Author(s):  
Sanjay Charaya ◽  
◽  
Kapil Mehta ◽  
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Fuzzy Rule ◽  
Fixed Number ◽  
Fuzzy Rules ◽  
Observation Data ◽  
Dimensional Solution ◽  
Air Conditioning System ◽  
Rule Based ◽  
Genetic Algorithm Approach

The aim of this paper is to obtain a compact and optimal fuzzy rule-based model from observation data by utilizing the Genetic algorithm technique. The approach is optimized by applying Genetic Algorithms, owing to its capability of searching irregular and high dimensional solution spaces. Genetic Algorithms has been applied to learn consequent part of fuzzy rules and models with fixed number of rules. In the work we propose a Genetic algorithm approach to a non-linear air conditioning system for the construction of optimal fuzzy rules in two steps. First, fuzzy clustering is applied to obtain an initial rule based model having pre-calculated number of rules with antecedents only. In the second step, the regions of rule-consequents are obtained by a binary coded Genetic Algorithm which leads to the extraction of an optimal rule based model.

scholarly journals SIMULASI JADWAL UJIAN NASIONAL MENGGUNAKAN ALGORITMA GENETIK

2018 ◽  
Vol 1 (2) ◽  
pp. 185
Author(s):  
Hambali Hambali ◽  
Feri Hari Utami
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Secondary School ◽  
Execution Time ◽  
Large Problem ◽  
Junior Secondary School ◽  
National Examination ◽  
School Based ◽  
Junior Secondary ◽  
Genetic Algorithm Approach

Abstrack - Scheduling of national examination supervisors is done by crossing, the crossing is done by applying a genetic algorithm approach. Genetic algorithm is one way to solve a fairly large problem with a fairly good solution even though the problem requires a long execution time if done manually. Genetic algorithms are good enough to be used in scheduling supervisors in a junior secondary school. Based on the results of the discussion and testing it can be concluded as follows: National Examination Schedule Simulation Program Using Genetic Algorithms using PHP-Mysql can find out information systems and reports for each National Examination Monitoring Schedule, National Exam Schedule Simulation Using Genetic Algorithms Using PHP-MySql, can be used by Education and cultural services with evaluation data.   Keywords - Scheduling, PHP-Mysql, Genetic Algorithms

Formulation of Colors Using a Genetic Algorithm

2012 ◽  
Vol 17 (4) ◽  
pp. 241-244
Author(s):  
Cezary Draus ◽  
Grzegorz Nowak ◽  
Maciej Nowak ◽  
Marcin Tokarski
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Production Process ◽  
Computer Technology ◽  
Manual Method ◽  
Apply Genetic ◽  
Computer Aided ◽  
Multiple Samples ◽  
Plastics Industry

Abstract The possibility to obtain a desired color of the product and to ensure its repeatability in the production process is highly desired in many industries such as printing, automobile, dyeing, textile, cosmetics or plastics industry. So far, most companies have traditionally used the "manual" method, relying on intuition and experience of a colorist. However, the manual preparation of multiple samples and their correction can be very time consuming and expensive. The computer technology has allowed the development of software to support the process of matching colors. Nowadays, formulation of colors is done with appropriate equipment (colorimeters, spectrophotometers, computers) and dedicated software. Computer-aided formulation is much faster and cheaper than manual formulation, because fewer corrective iterations have to be carried out, to achieve the desired result. Moreover, the colors are analyzed with regard to the metamerism, and the best recipe can be chosen, according to the specific criteria (price, quantity, availability). Optimaization problem of color formulation can be solved in many diferent ways. Authors decided to apply genetic algorithms in this domain.

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