Site Characterization Model Using Artificial Neural Network and Kriging
01 Oct 2010-International Journal of Geomechanics (American Society of Civil Engineers)-Vol. 10, Iss: 5, pp 171-180
TL;DR: In this article, the problem of site characterization is treated as a task of function approximation of the large existing data from standard penetration tests (SPTs) in three-dimensional subsurface of Bangalore, India.
Abstract: In this paper, the problem of site characterization is treated as a task of function approximation of the large existing data from standard penetration tests (SPTs) in three-dimensional subsurface of Bangalore, India. More than 2,700 field SPT values (N) has been collected from 766 boreholes spread over an area of 220 -km2 area in Bangalore, India. To get N corrected value ( Nc ) , N values have been corrected for different parameters such as overburden stress, size of borehole, type of sampler, length of connected rod. In three-dimensional analysis, the function Nc = Nc ( X,Y,Z ) , where X , Y , and Z are the coordinates of a point corresponds to Nc value, is to be approximated with which Nc value at any half-space point in Bangalore, India can be determined. An attempt has been made to develop artificial neural network (ANN) model using multilayer perceptrons that are trained with Levenberg-Marquardt back-propagation algorithm. Also, a geostatistical model based on ordinary kriging technique has been ad...
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TL;DR: The methods discussed here help the geotechnical practitioner to be familiar with the limitations and strengths of ANN compared with alternative conventional mathematical modeling methods.
Abstract: Artificial neural network (ANN) aimed to simulate the behavior of the nervous system as well as the human brain. Neural network models are mathematical computing systems inspired by the biological neural network in which try to constitute animal brains. ANNs recently extended, presented, and applied by many research scholars in the area of geotechnical engineering. After a comprehensive review of the published studies, there is a shortage of classification of study and research regarding systematic literature review about these approaches. A review of the literature reveals that artificial neural networks is well established in modeling retaining walls deflection, excavation, soil behavior, earth retaining structures, site characterization, pile bearing capacity (both skin friction and end-bearing) prediction, settlement of structures, liquefaction assessment, slope stability, landslide susceptibility mapping, and classification of soils. Therefore, the present study aimed to provide a systematic review of methodologies and applications with recent ANN developments in the subject of geotechnical engineering. Regarding this, a major database of the web of science has been selected. Furthermore, meta-analysis and systematic method which called PRISMA has been used. In this regard, the selected papers were classified according to the technique and method used, the year of publication, the authors, journals and conference names, research objectives, results and findings, and lastly solution and modeling. The outcome of the presented review will contribute to the knowledge of civil and/or geotechnical designers/practitioners in managing information in order to solve most types of geotechnical engineering problems. The methods discussed here help the geotechnical practitioner to be familiar with the limitations and strengths of ANN compared with alternative conventional mathematical modeling methods.
97 citations
TL;DR: In this article, a new framework for the probabilistic analysis of tunnel longitudinal performance is presented, where the conditional random field theory is adopted to simulate the spatial variation of soil properties along the tunnel longitudinal direction, in which the soil properties at borehole locations can be explicitly considered.
84 citations
TL;DR: In this article, the results of 90 Unconfined Compressive Strength (UCS) and California Bearing Ratio (CBR) tests on sulfate silty sand stabilized with different lime and microsilica percentages as the two main stabilizers.
62 citations
TL;DR: The main conclusions is that the number of researches in this field increases almost exponentially, the most used (AI) technique is the Artificial Neural Networks and its enhancements where it is presents about half the researches and finally correlating soil and rock properties is the most addressed subject with about 30% of the researched.
Abstract: It was 35 years ago since the first usage of Artificial Intelligence (AI) technique in geotechnical engineering, during those years many (AI) techniques were developed based in mathematical, statistical and logical concepts, but the breakthrough occurs by mimicking the natural searching and optimization algorithms. This huge development in (AI) techniques reflected on the geotechnical engineering problems. In this research, 626 paper and thesis published in the period from 1984 to 2019 concerned in applying (AI) techniques in geotechnical engineering were collected, filtered, arranged and classified with respect to subject, (AI) technique, publisher and publishing date and stored in a database. The extracted information from the database were tabulated, presented graphically and commented. The main conclusions is that the number of researches in this field increases almost exponentially, the most used (AI) technique is the Artificial Neural Networks and its enhancements where it is presents about half the researches and finally correlating soil and rock properties is the most addressed subject with about 30% of the researches.
56 citations
Additional excerpts
...…et al. (1997) 365 Cheng et al. (2014) 405 Ornek (2014) 445 Samui et al. (2009) 326 Kiefa (1998) 366 Miranda et al. (2004) 406 Ornek et al. (2012) 446 Samui and Sitharam (2010) 327 Amini et al. (2005) 367 Shahin (2015a) 407 Zaman et al. (2010) 447 Samui et al. (2015) 328 Prasad and…...
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TL;DR: A new model is derived to predict the peak ground acceleration (PGA) utilizing a hybrid method coupling artificial neural network (ANN) and simulated annealing (SA), called SA-ANN, which is superior to the single ANN and other existing attenuation models.
Abstract: A new model is derived to predict the peak ground acceleration (PGA) utilizing a hybrid method coupling artificial neural network (ANN) and simulated annealing (SA), called SA-ANN. The proposed model relates PGA to earthquake source to site distance, earthquake magnitude, average shear-wave velocity, faulting mechanisms, and focal depth. A database of strong ground-motion recordings of 36 earthquakes, which happened in Iran’s tectonic regions, is used to establish the model. For more validity verification, the SA-ANN model is employed to predict the PGA of a part of the database beyond the training data domain. The proposed SA-ANN model is compared with the simple ANN in addition to 10 well-known models proposed in the literature. The proposed model performance is superior to the single ANN and other existing attenuation models. The SA-ANN model is highly correlated to the actual records (R ¼ 0.835 and r ¼ 0.0908) and it is subsequently converted into a tractable design equation.
41 citations
Cites methods from "Site Characterization Model Using A..."
...…widely utilized for geotechnical engineering modeling in the last two decades (e.g. Goh, 1994; Azmathullah et al., 2005; Das and Basudhar, 2008; Samui and Sitharam, 2010; Gandomi and Alavi, 2011; Guven et al., 2012; Fister et al., 2014) and have recently been used to predict groundmotion…...
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...This soft computing technique, ANN, has been widely utilized for geotechnical engineering modeling in the last two decades (e.g. Goh, 1994; Azmathullah et al., 2005; Das and Basudhar, 2008; Samui and Sitharam, 2010; Gandomi and Alavi, 2011; Guven et al., 2012; Fister et al., 2014) and have recently been used to predict groundmotion characteristics (e....
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References
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TL;DR: In this paper, the authors clarified the meaning of the values of standard penetration resistance used in correlations of field observations of soil liquefaction with values of N1 measured in SPT tests.
Abstract: The purpose of this paper is to clarify the meaning of the values of standard penetration resistance used in correlations of field observations of soil liquefaction with values of N1 measured in SPT tests. The field data are reinterpreted and plotted in terms of a newly recommended standard, (N1)60, determined in SPT tests where the driving energy in the drill rods is 60% of the theoretical free‐fall energy. Energies associated with different methods of performing SPT tests in different countries and with different equipment are summarized and can readily be used to convert any measured N‐value to the standard (N1)60 value. Liquefaction resistance curves for sands with different (N1)60 values and with different fines contents are proposed. It is believed that these curves are more reliable than previous curves expressed in terms of mean grain size. The results presented are in good accord with recommended practice in Japan and China and should, thus, provide a useful basis for liquefaction evaluations in ...
1,180 citations
TL;DR: In this paper, the authors proposed a technique of modeling the statistical character of soil profiles, which provides a format for quantifying the information gathered during site investigation and testing, about the subsurface conditions at a site.
Abstract: New concepts and methods for modeling the natural variability of soil properties are presented and illustrated. The proposed technique of modeling the statistical character of soil profiles serves a dual function: (1)It provides a format for quantifying the information gathered during site investigation and testing, about the subsurface conditions at a site; and (2)it provides the basis for predicting performance and for quantifying the reliability of performance predictions. Probabilistic soil profiles are characterized, first, by best estimates of layer depths and of pertinent engineering properties; and secondly, by the coefficient of variation and the correlation scales for the profile parameters of interest. Methodology is developed for dealing with problems that can be formulated in terms of extremes of averages of soil properties. The problems of limit equilibrium slope stability and differential settlement prediction fall into this category.
913 citations
TL;DR: In this paper, the average values of soil properties over areas rather than point values can be obtained by block kriging, and the maps of sodium and stone content at Plas Gogerddan, Central Wales, kriged over blocks 920m2, and thickness of cover loam at Hole Farm, Norfolk, krng over blocks of 400m2.
Abstract: Summary
Soil properties mapped in two intensive surveys had large nugget variances, leading to large estimation variances and erratic isarithms when mapped by punctual kriging. It is likely that both surveyors and survey clients are interested in average values of soil properties over areas rather than point values, and such values can be obtained by block kriging. Estimation variances are very much smaller, and maps of sodium and stone content at Plas Gogerddan, Central Wales, kriged over blocks 920m2, and thickness of cover loam at Hole Farm, Norfolk, kriged over blocks of 400m2, are much smoother than the punctually kriged maps. The map of Hole Farm has a distinct and meaningful regional pattern.
784 citations
TL;DR: Kriging as mentioned in this paper is a form of weighted local averaging, which is optimal in the sense that it provides estimates of values at unrecorded places without bias and with minimum and known variance.
Abstract: Kriging is a means of spatial prediction that can be used for soil properties. It is a form of weighted local averaging. It is optimal in the sense that it provides estimates of values at unrecorded places without bias and with minimum and known variance. Isarithmic maps made by kriging are alternatives to conventional soil maps where properties can be measured at close spacings.
Kriging depends on first computing an accurate semi‐variogram, which measures the nature of spatial dependence for the property. Estimates of semi‐variance are then used to determine the weights applied to the data when computing the averages, and are presented in the kriging equations.
The method is applied to three sets of data from detailed soil surveys in Central Wales and Norfolk. Sodium content at Plas Gogerddan was shown to vary isotropically with a linear semi‐variogram. Ordinary punctual kriging produced a map with intricate isarithms and fairly large estimation variance, attributed to a large nugget effect. Stoniness on the same land varied anisotropically with a linear semi‐variogram, and again the estimation error of punctual kriging was fairly large. At Hole Farm, Norfolk, the thickness of cover loam varied isotropically, but with a spherical semi‐variogram. Its parameters were estimated and used to krige point values and produce a map showing substantial short‐range variation.
770 citations
13 May 1997
TL;DR: In this paper, the authors present a synthesis of classic and geostatistical methods with a focus on the most practical linear minimum-variance estimation methods, and include suggestions on how to test and extend the applicability of such methods.
Abstract: Engineers and applied geophysicists routinely encounter interpolation and estimation problems when analysing data from field observations. Introduction to Geostatistics presents practical techniques for the estimation of spatial functions from sparse data. The author's unique approach is a synthesis of classic and geostatistical methods with a focus on the most practical linear minimum-variance estimation methods, and includes suggestions on how to test and extend the applicability of such methods. The author includes many useful methods (often not covered in other geostatistics books) such as estimating variogram parameters, evaluating the need for a variable mean, parameter estimation and model testing in complex cases (e.g. anisotropy, variable mean, and multiple variables), and using information from deterministic mathematical models. Well illustrated with exercises and worked examples taken from hydrogeology, Introduction to Geostatistics assumes no background in statistics and is suitable for graduate-level courses in earth sciences, hydrology, and environmental engineering, and also for self-study.
758 citations