The Experts below are selected from a list of 200724 Experts worldwide ranked by ideXlab platform
Holger R Maier - One of the best experts on this subject based on the ideXlab platform.
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recent advances and future challenges for artificial neural systems in Geotechnical Engineering applications
Advances in Artificial Neural Systems, 2009Co-Authors: Mostafa Ali Shahin, Mark B. Jaksa, Holger R MaierAbstract:Artificial neural networks (ANNs) are a form of artificial intelligence that has proved to provide a high level of competency in solving many complex Engineering problems that are beyond the computational capability of classicalmathematics and traditional procedures. In particular, ANNs have been applied successfully to almost all aspects of Geotechnical Engineering problems. Despite the increasing number and diversity of ANN applications in Geotechnical Engineering, the contents of reported applications indicate that the progress in ANN development and procedures is marginal and not moving forward since the mid-1990s. This paper presents a brief overview of ANN applications in Geotechnical Engineering, briefly provides an overview of the operation of ANN modeling, investigates the current research directions of ANNs in Geotechnical Engineering, and discusses some ANN modeling issues that need further attention in the future, including model robustness; transparency and knowledge extraction; extrapolation; uncertainty.
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state of the art of artificial neural networks in Geotechnical Engineering
The electronic journal of geotechnical engineering, 2008Co-Authors: Mostafa Ali Shahin, Mark B. Jaksa, Holger R MaierAbstract:Over the last few years, artificial neural networks (ANNs) have been used successfully for modeling almost all aspects of Geotechnical Engineering problems. Whilst ANNs provide a great deal of promise, they suffer from a number of shortcomings such as knowledge extraction, extrapolation and uncertainty. This paper presents a state-of-the-art examination of ANNs in Geotechnical Engineering and provides insights into the modeling issues of ANNs. The paper also discusses current research directions of ANNs that need further attention in the future.
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Artificial Neural Network Applications in Geotechnical Engineering
Australian Geomechanics, 2001Co-Authors: Mostafa Ali Shahin, Mark B. Jaksa, Holger R MaierAbstract:Over the last few years or so, the use of artificial neural networks (ANNs) has increased in many areas of Engineering. In particular, ANNs have been applied to many Geotechnical Engineering problems and have demonstrated some degree of success. A review of the literature reveals that ANNs have been used successfully in pile capacity prediction, modelling soil behaviour, site characterisation, earth retaining structures, settlement of structures, slope stability, design of tunnels and underground openings, liquefaction, soil permeability and hydraulic conductivity, soil compaction, soil swelling and classification of soils. The objective of this paper is to provide a general view of some ANN applications for solving some types of Geotechnical Engineering problems. It is not intended to describe the ANNs modelling issues in Geotechnical Engineering. The paper also does not intend to cover every single application or scientific paper that found in the literature. For brevity, some works are selected to be described in some detail, while others are acknowledged for reference purposes. The paper then discusses the strengths and limitations of ANNs compared with the other modelling approaches.
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artificial neural network applications in Geotechnical Engineering
2001Co-Authors: Mostafa Ali Shahin, Mark B. Jaksa, Holger R MaierAbstract:Over the last few years or so, the use of artificial neural networks ( ANNs) has increased in many areas of Engineering. In particular, ANNs have been applied to many Geotechnical Engineering problems and have demonstrated some degree of success. A review of the literature reveals that ANNs have been used successfully in pile capacity prediction, modelling soil behaviour, site characterisation, earth retaining structures, settlement of structures, slope stability, design of tunnels and underground openings, liquefaction, soil permeability and hydraulic conductivity, soil compaction, soil swelling and classification of soils. The objective of this paper is to provide a general view of some ANN applications for solving some types of Geotechnical Engineering problems. It is not intended to describe the ANNs modelling issues in Geotechnical Engineering. The paper also does not intend to cover every single application or scientific paper that found in the literature. For brevity, some works are selected to be described in some detail, while others are acknowledged for reference purposes. The paper then discusses the strengths and limitations of ANNs compared with the other modelling approaches. The Engineering properties of soil and rock exhibit varied and uncertain behaviour due to the complex and imprecise physical processes associated with the formation of these materials (Jaksa 1995). This is in contrast to most other civil Engineering materials, such as steel, concrete and timber, which exhibit far greater homogeneity and isotropy. In order to cope with the complexity of Geotechnical behaviour, and the spatial variability of these materials, traditional forms of Engineering design models are justifiably simplified. An alternative approach, which has been shown to have some degree of success, is based on the data alone to determine the structure and parameters of the model . The technique is known as artificial neural networks ( ANNs) and is well suited to model complex problems where the relationship between the model variables is unknown (Hubick 1992). This paper is intended to be for readers in the field of Geotechnical Engineering who are not familiar with artificial neural networks. The paper aims to detail some features associated with ANNs through a review for some of their applications to-date in Geotechnical Engineering. It is hoped that this review may attract more Geotechnical engineers to pay better attention to this promising tool. The paper starts with a brief overview of the structure and operation of the ANNs and gives a general overview of most ANN applications that have appeared in the geotechncial Engineering literature. Finally, the paper discusses the relative success of ANNs in predicting various Geotechnical Engineering properties and behaviour.
R. Kalatehjari - One of the best experts on this subject based on the ideXlab platform.
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Applications of Particle Swarm Optimization in Geotechnical Engineering: A Comprehensive Review
Geotechnical and Geological Engineering, 2018Co-Authors: M. Hajihassani, D. Jahed Armaghani, R. KalatehjariAbstract:Particle swarm optimization (PSO) is an evolutionary computation approach to solve nonlinear global optimization problems. The PSO idea was made based on simulation of a simplified social system, the graceful but unpredictable choreography of birds flock. This system is initialized with a population of random solutions that are updated during iterations. Over the last few years, PSO has been extensively applied in various Geotechnical Engineering aspects such as slope stability analysis, pile and foundation Engineering, rock and soil mechanics, and tunneling and underground space design. A review on the literature shows that PSO has utilized more widely in Geotechnical Engineering compared with other civil Engineering disciplines. This is due to comprehensive uncertainty and complexity of problems in Geotechnical Engineering which can be solved by using the PSO abilities in solving the complex and multi-dimensional problems. This paper provides a comprehensive review on the applicability, advantages and limitation of PSO in different disciplines of Geotechnical Engineering to provide an insight to an alternative and superior optimization method compared with the conventional optimization techniques for Geotechnical engineers.
Mostafa Ali Shahin - One of the best experts on this subject based on the ideXlab platform.
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recent advances and future challenges for artificial neural systems in Geotechnical Engineering applications
Advances in Artificial Neural Systems, 2009Co-Authors: Mostafa Ali Shahin, Mark B. Jaksa, Holger R MaierAbstract:Artificial neural networks (ANNs) are a form of artificial intelligence that has proved to provide a high level of competency in solving many complex Engineering problems that are beyond the computational capability of classicalmathematics and traditional procedures. In particular, ANNs have been applied successfully to almost all aspects of Geotechnical Engineering problems. Despite the increasing number and diversity of ANN applications in Geotechnical Engineering, the contents of reported applications indicate that the progress in ANN development and procedures is marginal and not moving forward since the mid-1990s. This paper presents a brief overview of ANN applications in Geotechnical Engineering, briefly provides an overview of the operation of ANN modeling, investigates the current research directions of ANNs in Geotechnical Engineering, and discusses some ANN modeling issues that need further attention in the future, including model robustness; transparency and knowledge extraction; extrapolation; uncertainty.
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state of the art of artificial neural networks in Geotechnical Engineering
The electronic journal of geotechnical engineering, 2008Co-Authors: Mostafa Ali Shahin, Mark B. Jaksa, Holger R MaierAbstract:Over the last few years, artificial neural networks (ANNs) have been used successfully for modeling almost all aspects of Geotechnical Engineering problems. Whilst ANNs provide a great deal of promise, they suffer from a number of shortcomings such as knowledge extraction, extrapolation and uncertainty. This paper presents a state-of-the-art examination of ANNs in Geotechnical Engineering and provides insights into the modeling issues of ANNs. The paper also discusses current research directions of ANNs that need further attention in the future.
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Artificial Neural Network Applications in Geotechnical Engineering
Australian Geomechanics, 2001Co-Authors: Mostafa Ali Shahin, Mark B. Jaksa, Holger R MaierAbstract:Over the last few years or so, the use of artificial neural networks (ANNs) has increased in many areas of Engineering. In particular, ANNs have been applied to many Geotechnical Engineering problems and have demonstrated some degree of success. A review of the literature reveals that ANNs have been used successfully in pile capacity prediction, modelling soil behaviour, site characterisation, earth retaining structures, settlement of structures, slope stability, design of tunnels and underground openings, liquefaction, soil permeability and hydraulic conductivity, soil compaction, soil swelling and classification of soils. The objective of this paper is to provide a general view of some ANN applications for solving some types of Geotechnical Engineering problems. It is not intended to describe the ANNs modelling issues in Geotechnical Engineering. The paper also does not intend to cover every single application or scientific paper that found in the literature. For brevity, some works are selected to be described in some detail, while others are acknowledged for reference purposes. The paper then discusses the strengths and limitations of ANNs compared with the other modelling approaches.
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artificial neural network applications in Geotechnical Engineering
2001Co-Authors: Mostafa Ali Shahin, Mark B. Jaksa, Holger R MaierAbstract:Over the last few years or so, the use of artificial neural networks ( ANNs) has increased in many areas of Engineering. In particular, ANNs have been applied to many Geotechnical Engineering problems and have demonstrated some degree of success. A review of the literature reveals that ANNs have been used successfully in pile capacity prediction, modelling soil behaviour, site characterisation, earth retaining structures, settlement of structures, slope stability, design of tunnels and underground openings, liquefaction, soil permeability and hydraulic conductivity, soil compaction, soil swelling and classification of soils. The objective of this paper is to provide a general view of some ANN applications for solving some types of Geotechnical Engineering problems. It is not intended to describe the ANNs modelling issues in Geotechnical Engineering. The paper also does not intend to cover every single application or scientific paper that found in the literature. For brevity, some works are selected to be described in some detail, while others are acknowledged for reference purposes. The paper then discusses the strengths and limitations of ANNs compared with the other modelling approaches. The Engineering properties of soil and rock exhibit varied and uncertain behaviour due to the complex and imprecise physical processes associated with the formation of these materials (Jaksa 1995). This is in contrast to most other civil Engineering materials, such as steel, concrete and timber, which exhibit far greater homogeneity and isotropy. In order to cope with the complexity of Geotechnical behaviour, and the spatial variability of these materials, traditional forms of Engineering design models are justifiably simplified. An alternative approach, which has been shown to have some degree of success, is based on the data alone to determine the structure and parameters of the model . The technique is known as artificial neural networks ( ANNs) and is well suited to model complex problems where the relationship between the model variables is unknown (Hubick 1992). This paper is intended to be for readers in the field of Geotechnical Engineering who are not familiar with artificial neural networks. The paper aims to detail some features associated with ANNs through a review for some of their applications to-date in Geotechnical Engineering. It is hoped that this review may attract more Geotechnical engineers to pay better attention to this promising tool. The paper starts with a brief overview of the structure and operation of the ANNs and gives a general overview of most ANN applications that have appeared in the geotechncial Engineering literature. Finally, the paper discusses the relative success of ANNs in predicting various Geotechnical Engineering properties and behaviour.
Mark B. Jaksa - One of the best experts on this subject based on the ideXlab platform.
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recent advances and future challenges for artificial neural systems in Geotechnical Engineering applications
Advances in Artificial Neural Systems, 2009Co-Authors: Mostafa Ali Shahin, Mark B. Jaksa, Holger R MaierAbstract:Artificial neural networks (ANNs) are a form of artificial intelligence that has proved to provide a high level of competency in solving many complex Engineering problems that are beyond the computational capability of classicalmathematics and traditional procedures. In particular, ANNs have been applied successfully to almost all aspects of Geotechnical Engineering problems. Despite the increasing number and diversity of ANN applications in Geotechnical Engineering, the contents of reported applications indicate that the progress in ANN development and procedures is marginal and not moving forward since the mid-1990s. This paper presents a brief overview of ANN applications in Geotechnical Engineering, briefly provides an overview of the operation of ANN modeling, investigates the current research directions of ANNs in Geotechnical Engineering, and discusses some ANN modeling issues that need further attention in the future, including model robustness; transparency and knowledge extraction; extrapolation; uncertainty.
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state of the art of artificial neural networks in Geotechnical Engineering
The electronic journal of geotechnical engineering, 2008Co-Authors: Mostafa Ali Shahin, Mark B. Jaksa, Holger R MaierAbstract:Over the last few years, artificial neural networks (ANNs) have been used successfully for modeling almost all aspects of Geotechnical Engineering problems. Whilst ANNs provide a great deal of promise, they suffer from a number of shortcomings such as knowledge extraction, extrapolation and uncertainty. This paper presents a state-of-the-art examination of ANNs in Geotechnical Engineering and provides insights into the modeling issues of ANNs. The paper also discusses current research directions of ANNs that need further attention in the future.
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Artificial Neural Network Applications in Geotechnical Engineering
Australian Geomechanics, 2001Co-Authors: Mostafa Ali Shahin, Mark B. Jaksa, Holger R MaierAbstract:Over the last few years or so, the use of artificial neural networks (ANNs) has increased in many areas of Engineering. In particular, ANNs have been applied to many Geotechnical Engineering problems and have demonstrated some degree of success. A review of the literature reveals that ANNs have been used successfully in pile capacity prediction, modelling soil behaviour, site characterisation, earth retaining structures, settlement of structures, slope stability, design of tunnels and underground openings, liquefaction, soil permeability and hydraulic conductivity, soil compaction, soil swelling and classification of soils. The objective of this paper is to provide a general view of some ANN applications for solving some types of Geotechnical Engineering problems. It is not intended to describe the ANNs modelling issues in Geotechnical Engineering. The paper also does not intend to cover every single application or scientific paper that found in the literature. For brevity, some works are selected to be described in some detail, while others are acknowledged for reference purposes. The paper then discusses the strengths and limitations of ANNs compared with the other modelling approaches.
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artificial neural network applications in Geotechnical Engineering
2001Co-Authors: Mostafa Ali Shahin, Mark B. Jaksa, Holger R MaierAbstract:Over the last few years or so, the use of artificial neural networks ( ANNs) has increased in many areas of Engineering. In particular, ANNs have been applied to many Geotechnical Engineering problems and have demonstrated some degree of success. A review of the literature reveals that ANNs have been used successfully in pile capacity prediction, modelling soil behaviour, site characterisation, earth retaining structures, settlement of structures, slope stability, design of tunnels and underground openings, liquefaction, soil permeability and hydraulic conductivity, soil compaction, soil swelling and classification of soils. The objective of this paper is to provide a general view of some ANN applications for solving some types of Geotechnical Engineering problems. It is not intended to describe the ANNs modelling issues in Geotechnical Engineering. The paper also does not intend to cover every single application or scientific paper that found in the literature. For brevity, some works are selected to be described in some detail, while others are acknowledged for reference purposes. The paper then discusses the strengths and limitations of ANNs compared with the other modelling approaches. The Engineering properties of soil and rock exhibit varied and uncertain behaviour due to the complex and imprecise physical processes associated with the formation of these materials (Jaksa 1995). This is in contrast to most other civil Engineering materials, such as steel, concrete and timber, which exhibit far greater homogeneity and isotropy. In order to cope with the complexity of Geotechnical behaviour, and the spatial variability of these materials, traditional forms of Engineering design models are justifiably simplified. An alternative approach, which has been shown to have some degree of success, is based on the data alone to determine the structure and parameters of the model . The technique is known as artificial neural networks ( ANNs) and is well suited to model complex problems where the relationship between the model variables is unknown (Hubick 1992). This paper is intended to be for readers in the field of Geotechnical Engineering who are not familiar with artificial neural networks. The paper aims to detail some features associated with ANNs through a review for some of their applications to-date in Geotechnical Engineering. It is hoped that this review may attract more Geotechnical engineers to pay better attention to this promising tool. The paper starts with a brief overview of the structure and operation of the ANNs and gives a general overview of most ANN applications that have appeared in the geotechncial Engineering literature. Finally, the paper discusses the relative success of ANNs in predicting various Geotechnical Engineering properties and behaviour.
M. Hajihassani - One of the best experts on this subject based on the ideXlab platform.
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Applications of Particle Swarm Optimization in Geotechnical Engineering: A Comprehensive Review
Geotechnical and Geological Engineering, 2018Co-Authors: M. Hajihassani, D. Jahed Armaghani, R. KalatehjariAbstract:Particle swarm optimization (PSO) is an evolutionary computation approach to solve nonlinear global optimization problems. The PSO idea was made based on simulation of a simplified social system, the graceful but unpredictable choreography of birds flock. This system is initialized with a population of random solutions that are updated during iterations. Over the last few years, PSO has been extensively applied in various Geotechnical Engineering aspects such as slope stability analysis, pile and foundation Engineering, rock and soil mechanics, and tunneling and underground space design. A review on the literature shows that PSO has utilized more widely in Geotechnical Engineering compared with other civil Engineering disciplines. This is due to comprehensive uncertainty and complexity of problems in Geotechnical Engineering which can be solved by using the PSO abilities in solving the complex and multi-dimensional problems. This paper provides a comprehensive review on the applicability, advantages and limitation of PSO in different disciplines of Geotechnical Engineering to provide an insight to an alternative and superior optimization method compared with the conventional optimization techniques for Geotechnical engineers.
