The Experts below are selected from a list of 267 Experts worldwide ranked by ideXlab platform
Hossein Mola-abasi - One of the best experts on this subject based on the ideXlab platform.
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Pressuremeter Modulus and Limit Pressure of Clayey Soils Using GMDH-Type Neural Network and Genetic Algorithms
Geotechnical and Geological Engineering, 2018Co-Authors: Reza Ziaie Moayed, Afshin Kordnaeij, Hossein Mola-abasiAbstract:Pressuremeter modulus ( $$E_{M}$$ E M ) and Limit Pressure ( $$P_{L}$$ P L ) are used for the calculation of the settlement and bearing capacity of foundation respectively. As the determination of these parameters from Pressuremeter test ( PMT ) is relatively time-consuming and expensive, various empirical correlations have been proposed to correlate the $$E_{M}$$ E M and $$P_{L}$$ P L to other soil parameters. For the existing equations are incapable of estimating these PMT parameters well, in present research group method of data handling type neural network is used to estimate the $$E_{M}$$ E M and $$P_{L}$$ P L of clayey soils. The $$E_{M}$$ E M and $$P_{L}$$ P L were modeled as a function of three variables including the moisture content ( $$\omega$$ ω ), plasticity index and corrected SPT blow counts ( $$N_{60}$$ N 60 ). A database containing 51 data sets have been used for training and testing of the models. The performances of proposed models are compared with those of existing empirical equations. The results demonstrate that appreciable improvement with respect to the other correlations has been achieved. At the end, sensitivity analysis of the obtained models has been performed to study the influence of input parameters on model outputs and shows that the $$N_{60}$$ N 60 is the most influential parameter on the PMT parameters.
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Pressuremeter Modulus and Limit Pressure of Clayey Soils Using GMDH -Type Neural Network and Genetic Algorithms
Geotechnical and Geological Engineering, 2017Co-Authors: Reza Ziaie Moayed, Afshin Kordnaeij, Hossein Mola-abasiAbstract:Pressuremeter modulus (\(E_{M}\)) and Limit Pressure (\(P_{L}\)) are used for the calculation of the settlement and bearing capacity of foundation respectively. As the determination of these parameters from Pressuremeter test (PMT) is relatively time-consuming and expensive, various empirical correlations have been proposed to correlate the \(E_{M}\) and \(P_{L}\) to other soil parameters. For the existing equations are incapable of estimating these PMT parameters well, in present research group method of data handling type neural network is used to estimate the \(E_{M}\) and \(P_{L}\) of clayey soils. The \(E_{M}\) and \(P_{L}\) were modeled as a function of three variables including the moisture content (\(\omega\)), plasticity index and corrected SPT blow counts (\(N_{60}\)). A database containing 51 data sets have been used for training and testing of the models. The performances of proposed models are compared with those of existing empirical equations. The results demonstrate that appreciable improvement with respect to the other correlations has been achieved. At the end, sensitivity analysis of the obtained models has been performed to study the influence of input parameters on model outputs and shows that the \(N_{60}\) is the most influential parameter on the PMT parameters.
Afshin Kordnaeij - One of the best experts on this subject based on the ideXlab platform.
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Pressuremeter Modulus and Limit Pressure of Clayey Soils Using GMDH-Type Neural Network and Genetic Algorithms
Geotechnical and Geological Engineering, 2018Co-Authors: Reza Ziaie Moayed, Afshin Kordnaeij, Hossein Mola-abasiAbstract:Pressuremeter modulus ( $$E_{M}$$ E M ) and Limit Pressure ( $$P_{L}$$ P L ) are used for the calculation of the settlement and bearing capacity of foundation respectively. As the determination of these parameters from Pressuremeter test ( PMT ) is relatively time-consuming and expensive, various empirical correlations have been proposed to correlate the $$E_{M}$$ E M and $$P_{L}$$ P L to other soil parameters. For the existing equations are incapable of estimating these PMT parameters well, in present research group method of data handling type neural network is used to estimate the $$E_{M}$$ E M and $$P_{L}$$ P L of clayey soils. The $$E_{M}$$ E M and $$P_{L}$$ P L were modeled as a function of three variables including the moisture content ( $$\omega$$ ω ), plasticity index and corrected SPT blow counts ( $$N_{60}$$ N 60 ). A database containing 51 data sets have been used for training and testing of the models. The performances of proposed models are compared with those of existing empirical equations. The results demonstrate that appreciable improvement with respect to the other correlations has been achieved. At the end, sensitivity analysis of the obtained models has been performed to study the influence of input parameters on model outputs and shows that the $$N_{60}$$ N 60 is the most influential parameter on the PMT parameters.
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Pressuremeter Modulus and Limit Pressure of Clayey Soils Using GMDH -Type Neural Network and Genetic Algorithms
Geotechnical and Geological Engineering, 2017Co-Authors: Reza Ziaie Moayed, Afshin Kordnaeij, Hossein Mola-abasiAbstract:Pressuremeter modulus (\(E_{M}\)) and Limit Pressure (\(P_{L}\)) are used for the calculation of the settlement and bearing capacity of foundation respectively. As the determination of these parameters from Pressuremeter test (PMT) is relatively time-consuming and expensive, various empirical correlations have been proposed to correlate the \(E_{M}\) and \(P_{L}\) to other soil parameters. For the existing equations are incapable of estimating these PMT parameters well, in present research group method of data handling type neural network is used to estimate the \(E_{M}\) and \(P_{L}\) of clayey soils. The \(E_{M}\) and \(P_{L}\) were modeled as a function of three variables including the moisture content (\(\omega\)), plasticity index and corrected SPT blow counts (\(N_{60}\)). A database containing 51 data sets have been used for training and testing of the models. The performances of proposed models are compared with those of existing empirical equations. The results demonstrate that appreciable improvement with respect to the other correlations has been achieved. At the end, sensitivity analysis of the obtained models has been performed to study the influence of input parameters on model outputs and shows that the \(N_{60}\) is the most influential parameter on the PMT parameters.
Radhi Alzubaidi - One of the best experts on this subject based on the ideXlab platform.
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Distinctive Effects of Horizontal At-Rest Pressure on Conventional Limit Pressure Results
Geotechnical and Geological Engineering, 2017Co-Authors: Radhi AlzubaidiAbstract:Pressuremeter testing plays an important role in the design of foundations, where several parameters can be deduced from a single test result. Conventional and theoretical Limit Pressures can be interpreted from Pressuremeter tests and are considered key to bearing capacity calculations and shallow foundation designs. Limit Pressure can be evaluated using different methods, all of which show discrepancies in values obtained from the same tests. Horizontal at-rest earth Pressure can similarly be deduced from different methods of analysis, which also show distinctive differences in results for the same tests. This research explored the important finding that the values of the conventional Limit Pressure experience considerable discrepancies when using different values of the horizontal at-rest Pressure. Values calculated for conventional Limit Pressure showed considerable increases when increasing the value of the horizontal at-rest Pressure, but there was no effect on the theoretical Limit Pressure.
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Essential Findings in Pressuremeter Theories
Geotechnical and Geological Engineering, 2015Co-Authors: Radhi AlzubaidiAbstract:The Pressuremeter, used for in situ soil testing, has undergone significant development both in its technical applications and in the interpretation methods employed for a range of parameters. Several methods have been developed to evaluate the undrained strength of a soil using a Pressuremeter. Test results based on these methods show distinctive discrepancies. Different methods for evaluating the Limit Pressure are also presented. The values of these Limit Pressure evaluations vary based on the evaluation method used. In any given test, the Limit Pressure results also affect the values deduced for undrained shear strength. The discrepancies in the undrained shear strength values exceeded 80 % for the same test when evaluations were made with different interpretation methods. Because of the large discrepancies in the results of the undrained shear strength when using different analysis methods, the Gibson and Anderson method is recommended as being most reliable in deducing undrained shear strength values from Pressuremeter tests, particularly for use in the design of foundations.
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Different Results in Pressuremeter Theories
Geotechnical and Geological Engineering, 2014Co-Authors: Radhi AlzubaidiAbstract:The Limit Pressure that evaluated from Pressuremeter tests has been shown to represent a key constitutive relationship for bearing capacity and shallow foundation design. The theoretical and the conventional Limits Pressure have been evaluated from different methods of interpretation using different theories. This paper provides a new method for interpretation the conventional Limit Pressure, the new method showed very good agreements with other methods used for evaluating the conventional Limit Pressure. The new method named as conventional Limit Pressure. The results of Menard Pressuremeter conducted in Abu-Dhabi site been analyzed in five methods of interpretation for conventional and theoretical Limit Pressure. The deduced results from different methods showed some discrepancies for the same tests. The tested soil can be described as poorly graded sand with silt.
Reza Ziaie Moayed - One of the best experts on this subject based on the ideXlab platform.
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Pressuremeter Modulus and Limit Pressure of Clayey Soils Using GMDH -Type Neural Network and Genetic Algorithms
Geotechnical and Geological Engineering, 2017Co-Authors: Reza Ziaie Moayed, Afshin Kordnaeij, Hossein Mola-abasiAbstract:Pressuremeter modulus (\(E_{M}\)) and Limit Pressure (\(P_{L}\)) are used for the calculation of the settlement and bearing capacity of foundation respectively. As the determination of these parameters from Pressuremeter test (PMT) is relatively time-consuming and expensive, various empirical correlations have been proposed to correlate the \(E_{M}\) and \(P_{L}\) to other soil parameters. For the existing equations are incapable of estimating these PMT parameters well, in present research group method of data handling type neural network is used to estimate the \(E_{M}\) and \(P_{L}\) of clayey soils. The \(E_{M}\) and \(P_{L}\) were modeled as a function of three variables including the moisture content (\(\omega\)), plasticity index and corrected SPT blow counts (\(N_{60}\)). A database containing 51 data sets have been used for training and testing of the models. The performances of proposed models are compared with those of existing empirical equations. The results demonstrate that appreciable improvement with respect to the other correlations has been achieved. At the end, sensitivity analysis of the obtained models has been performed to study the influence of input parameters on model outputs and shows that the \(N_{60}\) is the most influential parameter on the PMT parameters.
Reza Ziaie Moayed - One of the best experts on this subject based on the ideXlab platform.
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Pressuremeter Modulus and Limit Pressure of Clayey Soils Using GMDH-Type Neural Network and Genetic Algorithms
Geotechnical and Geological Engineering, 2018Co-Authors: Reza Ziaie Moayed, Afshin Kordnaeij, Hossein Mola-abasiAbstract:Pressuremeter modulus ( $$E_{M}$$ E M ) and Limit Pressure ( $$P_{L}$$ P L ) are used for the calculation of the settlement and bearing capacity of foundation respectively. As the determination of these parameters from Pressuremeter test ( PMT ) is relatively time-consuming and expensive, various empirical correlations have been proposed to correlate the $$E_{M}$$ E M and $$P_{L}$$ P L to other soil parameters. For the existing equations are incapable of estimating these PMT parameters well, in present research group method of data handling type neural network is used to estimate the $$E_{M}$$ E M and $$P_{L}$$ P L of clayey soils. The $$E_{M}$$ E M and $$P_{L}$$ P L were modeled as a function of three variables including the moisture content ( $$\omega$$ ω ), plasticity index and corrected SPT blow counts ( $$N_{60}$$ N 60 ). A database containing 51 data sets have been used for training and testing of the models. The performances of proposed models are compared with those of existing empirical equations. The results demonstrate that appreciable improvement with respect to the other correlations has been achieved. At the end, sensitivity analysis of the obtained models has been performed to study the influence of input parameters on model outputs and shows that the $$N_{60}$$ N 60 is the most influential parameter on the PMT parameters.