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Copyright of Canadian Geotechnical Journal is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Canadian Geotechnical Journal is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Canadian Geotechnical Journal is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Canadian Geotechnical Journal is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Canadian Geotechnical Journal is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Canadian Geotechnical Journal is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Canadian Geotechnical Journal is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Canadian Geotechnical Journal is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Canadian Geotechnical Journal is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Canadian Geotechnical Journal is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, a neural network approach is used to describe the mechanical behavior of unsaturated soils. A sequential architecture was chosen for the network, that is, a multilayer perception network with feedback capability. The input layer consisted of nine neurons, where six of them represented the initial soil conditions and the remaining three neurons were continuously updated for each increment of axial strain based on outputs from the previous increment. The output layer consisted of three neurons representing values of deviatoric stress, volumetric strain, and change in suction at the end of each increment. Next, a database was developed from triaxial test results available in the literature. The database was used to train and test the network. Neural network simulations were compared with experimental results. The comparison indicates the good performance of the proposed network for predicting the mechanical behavior of unsaturated soils. Moreover, the trained network was employed to simulate other stress paths not present in the database to model the so-called "collapse phenomena." The results were promising. [ABSTRACT FROM AUTHOR]

Compaction curves (or density–moisture relationships) of cohesive soils are essential components for establishing practical and reliable criteria for effective control of field compaction. In this paper, modules built from empirical models for simulating the compaction curves of cohesive soils based on easily measured basic soil properties and compaction energy were developed using both statistical regression and artificial neural networks (ANNs) techniques. A large number of compaction curves pertaining to a wide variety of fine-grained soils were collected and used in modeling. The developed modules were able to predict compaction curves of soils with good accuracy, with the ANN-based module outperforming the statistical-based analog. The compaction modules were utilized to inquire about the compactibility behavior of fine-grained soils in relation to their properties and the compaction energy used. Besides their use as independent compaction curve predictors, the compaction modules can be used as supplementary units in numerical models for solving geotechnical engineering problems and as tools useful in preliminary design phases and feasibility studies.Key words: cohesive soils, compaction curve, modeling, neural networks, regression.Les courbes de compactage (ou relations poids volumique sec – teneur en eau) des sols cohérents sont des composantes essentielles dans la définition de critères pratiques et fiables pour le contrôle du compactage sur le terrain. Dans cet article, des modules mis au point pour simuler les courbes de compactage des sols cohérents à partir de propriétés fondamentales des sols et de l'énergie de compactage ont été développés au moyen de la régression statistique de même que des techniques de réseaux de neurones artificiels (ANNs). Un grand nombre de courbes de compactage provenant d'un large éventail de sols à grains fins ont été colligés et utilisés dans la modélisation. Les modules développés ont pu prédire les courbes de compactage de sols avec une bonne précision; mais le module basé sur le ANN a offert une meilleure performance que l'analogie basée sur la statistique. Les modules de compactage ont été utilisés pour se renseigner sur le potentiel de compactage des sols fins en relation avec leurs propriétés et l'énergie de compactage utilisée. En plus de leur utilisation comme moyen indépendant de prédire la courbe de compactage, les modules de compactage peuvent être utilisés comme des éléments supplémentaires dans les modèles numériques pour résoudre des problèmes géotechniques, et comme des outils utiles dans les phases préliminaires de conception et dans les études de faisabilité.Mots clés : sols cohérents, courbe de compactage, modélisation, réseaux de neurones, régression.[Traduit par la Rédaction] [ABSTRACT FROM AUTHOR]

Copyright of Canadian Geotechnical Journal is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The article offers the author's insights on the discussion regarding the article "Intelligent computing for modeling axial capacity of pile foundations." It discusses the development of artificial neural network (ANN) models to improve model generalization. It also comments on the suggestion to use either maximum absolute error (MAE) or normalized mean biased error (NMBE) instead of the normalized mean squared error (NMSE) for comparing architecture and optimization of ANN models.

Copyright of Canadian Geotechnical Journal is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Canadian Geotechnical Journal is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Canadian Geotechnical Journal is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Simplified techniques based on in situ testing methods are commonly used to assess seismic liquefaction potential. Many of these simplified methods are based on finding the liquefaction boundary separating two categories (the occurrence or non-occurrence of liquefaction) through the analysis of liquefaction case histories. As the liquefaction classification problem is highly nonlinear in nature, it is difficult to develop a comprehensive model taking into account all the independent variables, such as the seismic and soil properties, using conventional modeling techniques. Hence, in many of the conventional methods that have been proposed, simplified assumptions have been made. In this study, a probabilistic neural network (PNN) approach based on the Bayesian classifier method is used to evaluate seismic liquefaction potential based on actual field records. Two separate analyses are performed, one based on cone penetration test data and one based on shear wave velocity data. The PNN model effectively explores the relationship between the independent and dependent variables without any assumptions about the relationship between the various variables. Through the iterative presentation of the data (the learning phase), this study serves to demonstrate that the PNN can "discover" the intrinsic relationship between the seismic and soil parameters and the liquefaction potential. Comparisons indicate that the PNN models perform far better than the conventional methods in predicting the occurrence or non-occurrence of liquefaction.Key words: cone penetration test, neural networks, prediction, probabilistic neural network, sand, seismic liquefaction, shear wave velocity.Des techniques simplifiées basées sur les méthodes d'essais in situ sont couramment utilisées pour évaluer le potentiel de liquéfaction sismique. Plusieurs de ces méthodes sont basées sur la détermination de la frontière de la liquéfaction séparant deux catégories (la génération ou la non génération de liquéfaction) définie au moyen de l'analyse d'histoires de cas de liquéfaction. Comme le problème de classification de la liquéfaction est par nature fortement non linéaire, il est difficile de développer un modèle complet prenant en compte toutes les variables indépendantes telles que les propriétés des sols et des séismes, en utilisant les techniques conventionnelles de modélisation. En conséquence, dans plusieurs des méthodes conventionnelles qui ont été proposées, des hypothèses simplificatrices ont été faites. Dans cette étude, une approche probabilistique de réseau de neurones (PNN) basée sur la méthode Bayesienne de classification est utilisée pour évaluer le potentiel de liquéfaction sismique sur la base des données réelles de terrain. Deux analyses séparées ont été réalisées, une basée sur le données du CPT, et l'autre sur les données de vitesses des ondes de cisaillement. De fait, le modèle PNN explore la relation entre les variables dépendantes et indépendantes sans aucunes hypothèses concernant la relation entre les diverses variables. Par l'intermédiaire de la présentation itérative des données (la phase d'apprentissage), cette étude sert à démontrer que le PNN peut « découvrir » la relation intrinsèque entre les paramètres sismiques et du sol, et le potentiel de liquéfaction. Des comparaisons indiquent que les modèles PNN fonctionnent beaucoup mieux que les méthodes conventionnelles pour prédire la génération ou la non génération de la liquéfaction.Mots clés : essai de pénétration au cône, réseaux de neurones, prédiction, réseau probabilistique de neurones, sable, liquéfaction, vitesse des ondes de cisaillement.[Traduit par la Rédaction] [ABSTRACT FROM AUTHOR]

The article discusses a study which highlights a method of predicting axial load capacities of steel and concrete piles as well as drilled shaft piers with the use of an artificial neural network (ANN). It states that a tahn transfer function has been used in devising the ANN model. It also tells that the authors of the study made a comparison of the models considering normalized mean squared error (NMSE). Moreover, it says that Garson's algorithm was used to calculate ANN model sensitivity.