The Experts below are selected from a list of 24915 Experts worldwide ranked by ideXlab platform
Saeedeh Osouli - One of the best experts on this subject based on the ideXlab platform.
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Prediction of the resilient modulus of flexible pavement subgrade soils using adaptive neuro-fuzzy inference systems
Construction and Building Materials, 2016Co-Authors: Ehsan Sadrossadat, Ali Heidaripanah, Saeedeh OsouliAbstract:The resilient modulus (MR) of pavement subgrade soils essentially describes the structural response of pavements for a reliable design. Due to the elaborate, expensive and complex experimental Estimation of MRfactor, several models are proposed for Indirect Estimation of it which are mostly established based on statistical analyses e.g. regression analyses. The deficiencies of existing models in addition to the complexity of resilient behavior of soils indicate the necessity to develop better models. This study investigates the potential of a powerful hybrid artificial intelligence paradigm, i.e. adaptive neuro-fuzzy inference system (ANFIS), for prediction of MRof flexible pavements subgrade soils. A comprehensive database which comprises a total of 891 experimental datasets conducted on different Ohio cohesive soils is taken from the literature for evolving models. In ANFIS modeling, P#200, LL, PI, wopt, wc, Sr, qu, σ3, σdare considered as input variables and correspondingly the output is MR. Several statistical criteria, validation and verification studies are used for evaluating the performance capability of the obtained model. A sensitivity analysis is utilized to demonstrate the effectiveness of the considered input variables for characterizing MR. Besides, the response of ANFIS based MRmodel to variations of each input variable is examined using a parametric study and results are compared to those experimentally provided in the literature. Eventually, the obtained results approve the robustness of ANFIS approach for Indirect Estimation of MRof subgrade soils.
Ehsan Sadrossadat - One of the best experts on this subject based on the ideXlab platform.
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use of adaptive neuro fuzzy inference system and gene expression programming methods for Estimation of the bearing capacity of rock foundations
Engineering Computations, 2018Co-Authors: Ehsan Sadrossadat, Behnam Ghorbani, Rahimzadeh Oskooei, Mahdi KaboutariAbstract:This study aims to examine the potential of two artificial intelligence (AI)-based algorithms, namely, adaptive neuro-fuzzy inference system (ANFIS) and gene expression programming (GEP), for Indirect Estimation of the ultimate bearing capacity (qult) of rock foundations, which is a considerable civil and geotechnical engineering problem.,The input-processing-output procedures taking place in ANFIS and GEP are represented for developing predictive models. The great importance of simultaneously considering both qualitative and quantitative parameters for Indirect Estimation of qult is taken into account and explained. This issue can be considered as a remarkable merit of using AI-based approaches. Furthermore, the evaluation procedure of various models from both engineering and accuracy viewpoints is also demonstrated in this study.,A new and explicit formula generated by GEP is proposed for the Estimation of the qult of rock foundations, which can be used for further engineering aims. It is also presented that although the ANFIS approach can predict the output with a high degree of accuracy, the obtained model might be a black-box. The results of model performance analyses confirm that ANFIS and GEP can be used as alternative and useful approaches over previous methods for modeling and prediction problems.,The superiorities and weaknesses of GEP and ANFIS techniques for the numerical analysis of engineering problems are expressed and the performance of their obtained models is compared to those provided by other approaches in the literature. The findings of this research provide the researchers with a better insight to using AI techniques for resolving complicated problems.
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towards application of linear genetic programming for Indirect Estimation of the resilient modulus of pavements subgrade soils
Road Materials and Pavement Design, 2018Co-Authors: Ehsan Sadrossadat, Ali Heidaripanah, Behnam GhorbaniAbstract:The success of a flexible pavement design depends on the accuracy of determining the structural response of the pavement to dynamic loads, known as resilient modulus (MR). This paper explores the p...
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Prediction of the resilient modulus of flexible pavement subgrade soils using adaptive neuro-fuzzy inference systems
Construction and Building Materials, 2016Co-Authors: Ehsan Sadrossadat, Ali Heidaripanah, Saeedeh OsouliAbstract:The resilient modulus (MR) of pavement subgrade soils essentially describes the structural response of pavements for a reliable design. Due to the elaborate, expensive and complex experimental Estimation of MRfactor, several models are proposed for Indirect Estimation of it which are mostly established based on statistical analyses e.g. regression analyses. The deficiencies of existing models in addition to the complexity of resilient behavior of soils indicate the necessity to develop better models. This study investigates the potential of a powerful hybrid artificial intelligence paradigm, i.e. adaptive neuro-fuzzy inference system (ANFIS), for prediction of MRof flexible pavements subgrade soils. A comprehensive database which comprises a total of 891 experimental datasets conducted on different Ohio cohesive soils is taken from the literature for evolving models. In ANFIS modeling, P#200, LL, PI, wopt, wc, Sr, qu, σ3, σdare considered as input variables and correspondingly the output is MR. Several statistical criteria, validation and verification studies are used for evaluating the performance capability of the obtained model. A sensitivity analysis is utilized to demonstrate the effectiveness of the considered input variables for characterizing MR. Besides, the response of ANFIS based MRmodel to variations of each input variable is examined using a parametric study and results are compared to those experimentally provided in the literature. Eventually, the obtained results approve the robustness of ANFIS approach for Indirect Estimation of MRof subgrade soils.
Ali Heidaripanah - One of the best experts on this subject based on the ideXlab platform.
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towards application of linear genetic programming for Indirect Estimation of the resilient modulus of pavements subgrade soils
Road Materials and Pavement Design, 2018Co-Authors: Ehsan Sadrossadat, Ali Heidaripanah, Behnam GhorbaniAbstract:The success of a flexible pavement design depends on the accuracy of determining the structural response of the pavement to dynamic loads, known as resilient modulus (MR). This paper explores the p...
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Prediction of the resilient modulus of flexible pavement subgrade soils using adaptive neuro-fuzzy inference systems
Construction and Building Materials, 2016Co-Authors: Ehsan Sadrossadat, Ali Heidaripanah, Saeedeh OsouliAbstract:The resilient modulus (MR) of pavement subgrade soils essentially describes the structural response of pavements for a reliable design. Due to the elaborate, expensive and complex experimental Estimation of MRfactor, several models are proposed for Indirect Estimation of it which are mostly established based on statistical analyses e.g. regression analyses. The deficiencies of existing models in addition to the complexity of resilient behavior of soils indicate the necessity to develop better models. This study investigates the potential of a powerful hybrid artificial intelligence paradigm, i.e. adaptive neuro-fuzzy inference system (ANFIS), for prediction of MRof flexible pavements subgrade soils. A comprehensive database which comprises a total of 891 experimental datasets conducted on different Ohio cohesive soils is taken from the literature for evolving models. In ANFIS modeling, P#200, LL, PI, wopt, wc, Sr, qu, σ3, σdare considered as input variables and correspondingly the output is MR. Several statistical criteria, validation and verification studies are used for evaluating the performance capability of the obtained model. A sensitivity analysis is utilized to demonstrate the effectiveness of the considered input variables for characterizing MR. Besides, the response of ANFIS based MRmodel to variations of each input variable is examined using a parametric study and results are compared to those experimentally provided in the literature. Eventually, the obtained results approve the robustness of ANFIS approach for Indirect Estimation of MRof subgrade soils.
Matthew R Golden - One of the best experts on this subject based on the ideXlab platform.
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chlamydia screening coverage estimates derived using healthcare effectiveness data and information system procedures and Indirect Estimation vary substantially
Sexually Transmitted Diseases, 2013Co-Authors: Jennifer M Broad, Lisa E Manhart, Roxanne P Kerani, Delia Scholes, James P Hughes, Matthew R GoldenAbstract:BackgroundScreening coverage is an important determinant of chlamydial control program success.ObjectivesThe aim of this study was to compare chlamydial screening coverage estimates.MethodsWe compared 9 estimates among women aged 15 to 25 years in Washington State, 2009. Four used Healthcare Effecti
Debra J Mosure - One of the best experts on this subject based on the ideXlab platform.
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Indirect Estimation of chlamydia screening coverage using public health surveillance data
American Journal of Epidemiology, 2004Co-Authors: William C Levine, Linda W Dicker, Owen Devine, Debra J MosureAbstract:Although routine screening of all sexually active adolescent females for Chlamydia trachomatis infection is recommended at least annually in the United States, no national or state-specific population-based estimates of chlamydia screening coverage are known to exist. Conclusions regarding screening coverage have often been based on surveys of health care provider or facility screening practices, but such surveys do not consider persons who do not seek care at these facilities or who seek care at more than one facility. The authors developed a method to estimate the proportion of sexually active females aged 15-19 years screened for chlamydia in 45 states and the District of Columbia by using national data on chlamydia positivity, estimates of sexual activity from the National Survey of Family Growth, and chlamydial infections reported to the Centers for Disease Control and Prevention. Because of uncertainty regarding these values and related assumptions, credibility intervals were calculated by using a Monte Carlo model. When this model was used, the median state-specific proportion of sexually active females aged 15-19 years screened in 2000 was 60% (90% credibility interval: 55, 66). These results and this method should be evaluated for their utility in guiding implementation of national and state chlamydia control programs.
