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11 results

1. Predictive map of soil texture classes using decision tree model and neural network with features of geomorphology level.

Author

Sabbaghi, Mohammad Ali, Esfandiari, Mehrdad, Eftekhari, Kamran, and Torkashvand, Ali Mohammadi

Subjects
ARTIFICIAL neural networks, DIGITAL soil mapping, SOIL texture, GEOMORPHOLOGY, TEXTURE mapping, DECISION trees
Abstract

This study aims to compare decision tree (DT) and artificial neural network (ANN) models, in addition, the efficiency of geomorphic surface attributes in predicting soil texture classes. The study area is located in the north of Chaharmahal and Bakhtiari province, central west Iran, and covers 6875 ha. Ninety-six pedons were excavated on separated geoforms. Soil samples of top soil (A horizon) were analyzed for clay, sand, and silt contents. Totally 57 auxiliary variables, including the derivatives of digital elevation model (DEM), Landsat 8 images, geomorphic surface map, geology map, and land-use map, were used to predict both soil texture classes and soil particle size fractions. Root-mean-square error (RMSE), R² or the coefficient of determination (R_square), overall accuracy, and Kappa coefficient were selected as criteria for evaluating model performance. The R-square coefficients of clay, silt, and sand fractions for both models, respectively, were 0.41, 0.25, and 0.63 for ANN and 0.52, 0.62, and 0.75 for DT. According to RMSE, R-square, overall accuracy, and Kapa coefficient of validation data, the DT model produced better prediction fits to the both soil particle-size fraction and soil texture classes and was the most accurate classifier model. The parameters were 0.59, 0.09, 0.66, and 0.24 for ANN and 0.41, 0.75, 0.76, and 0.60 for DT models, respectively. The accuracy of each individual soil texture class was generally dependent upon the number of soil texture observations in each texture class. According to this fact, both models had better prediction for silty clay loam and clay loam texture classes. [ABSTRACT FROM AUTHOR]

Published
2024
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2. Advanced approaches to hot-mix asphalt dynamic modulus prediction.

Author

Ceylan, Halil, Gopalakrishnan, Kasthurirangan, and Sunghwan Kim

Subjects
ASPHALT emulsion mixtures, BITUMINOUS materials, PREDICTION models, ARTIFICIAL neural networks, COMPUTER simulation, PAVEMENT design & construction, COMPUTER-aided design
Abstract

The dynamic modulus (|E*|) is one of the primary hot-mix asphalt (HMA) material property inputs at all three hierarchical levels in the new Mechanistic-empirical pavement design guide (MEPDG). The existing |E*| prediction models were developed mainly from regression analysis of an |E*| database obtained from laboratory testing over many years and, in general, lack the necessary accuracy for making reliable predictions. This paper describes the development of a simplified HMA |E*| prediction model employing artificial neural network (ANN) methodology. The intelligent |E*| prediction models were developed using the latest comprehensive |E*| database that is available to researchers (from National Cooperative Highway Research Program Report 547) containing 7400 data points from 346 HMA mixtures. The ANN model predictions were compared with the Hirsch |E*| prediction model, which has a logical structure and a relatively simple prediction model in terms of the number of input parameters needed with respect to the existing |E*| models. The ANN-based |E*| predictions showed significantly higher accuracy compared with the Hirsch model predictions. The sensitivity of input variables to the ANN model predictions were also examined and discussed. Le module dynamique (|E*|) est l’une des principales propriétés du mélange d’asphalte à chaud utilisée en entrée aux trois niveaux hiérarchiques dans le nouveau guide de conception mécanisto-empirique des chaussées (« Mechanistic-empirical pavement design guide »). Les modèles actuels de prédiction de |E*| sont principalement développés à l’aide d’analyses de régression de bases de données de |E*| obtenues à partir de tests en laboratoire sur de nombreuses années. Ils manquent en général de la précision nécessaire pour faire des prédictions fiables. Cet article décrit la mise au point d’un modèle de prédiction simplifié du |E*| du mélange d’asphalte à chaud qui emploie la méthodologie des réseaux de neurones artificiels (RNA). Les modèles de prédiction intelligents de |E*| ont été développés en utilisant la base de données complète de |E*| la plus récente (rapport no 547 du National Cooperative Highway Research Program) à la disposition des chercheurs et contenant 7400 exemples provenant de 346 mélanges d’asphalte à chaud. Les prédictions de |E*| à partir du modèle des RNA ont été comparées à celles du modèle de Hirsch, lequel a une structure logique et un modèle de prédiction relativement simple en termes du nombre de paramètres nécessaires en entrée comparativement aux autres modèles de prédiction de |E*| existants. Les prédictions de |E*| fondées sur le modèle des RNA ont montré une précision significativement plus élevée comparativement à celles du modèle de Hirsch. La sensibilité des variables d’entrée aux prédictions du modèle des RNA a aussi été examinée et discutée. [ABSTRACT FROM AUTHOR]

Published
2008
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3. Prediction of friction capacity of driven piles in clay using the support vector machine.

Author

Samui, Pijush

Subjects
MACHINE learning, MACHINE theory, PARAMETERS (Statistics), FRICTION, MECHANICS (Physics), CLAY, PILES & pile driving
Abstract

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.)

Published
2008
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4. Part 1: Artificial neural network theory / Partie 1 : la théorie des réseaux neuronaux artificiels - Internal workings of feed-forward neural networks.

Author

Zhang, Qing J., Stanley, Stephen J., and Smith, Daniel W.

Subjects
ARTIFICIAL neural networks, FEEDFORWARD control systems, BACK propagation, NOISE, ARTIFICIAL intelligence
Abstract

Copyright of Journal of Environmental Engineering & Science 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.)

Published
2004
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5. Part 4: Artificial neural network applications in other areas of environmental engineering and science / Partie 4 : les applications des réseaux neuronaux artificiels dans d’autres secteurs du génie et de la science de l’environnement - Artificial neural network modelling of oil sands extraction processes

Author

Zhang, Qing J., Sawatzky, Ronald P., Wallace, E. Dean, London, Michael J., and Stanley, Stephen J.

Subjects
ARTIFICIAL neural networks, OIL sands, PETROLEUM geology, PATTERN perception, OIL sands industry
Abstract

Copyright of Journal of Environmental Engineering & Science 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.)

Published
2004
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6. Part 2: Artificial neural network applications in drinking water supply / Partie 2 : les applications des réseaux neuronaux artificiels à l’approvisionnement en eau potable - Implementing artificial neural network models for real-time water colour forecasting in a water treatment plant

Author

Zhang, Qing J., Cudrak, Audrey A., Shariff, Riyaz, and Stanley, Stephen J.

Subjects
ARTIFICIAL neural networks, WATER purification, WATER treatment plants
Abstract

Copyright of Journal of Environmental Engineering & Science 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.)

Published
2004
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7. Prediction of seismic demand model for pulse-like ground motions using artificial neural networks.

Author

Yazdannejad, Kowsar and Yazdani, Azad

Subjects
ARTIFICIAL neural networks, SEISMIC response, EARTHQUAKE resistant design, GEOLOGIC faults, PREDICTION models
Abstract

Copyright of Canadian Journal of Civil Engineering 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.)

Published
2017
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8. Cost assessment of construction projects through neural networks.

Author

Gulcicek, Umit, Ozkan, Omer, Gunduz, Murat, and Demir, Ismail Hakki

Subjects
COST estimates, MATHEMATICAL models, ARTIFICIAL neural networks, CONSTRUCTION projects, HOUSE construction, CONSTRUCTION project management
Abstract

Copyright of Canadian Journal of Civil Engineering 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.)

Published
2013
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9. Prediction of gas-phase reduced ion mobility constants ( K0) from three-dimensional molecular structure representation.

Subjects
POTASSIUM, IONIC mobility, MOLECULAR structure, QSAR models, GAS phase reactions, REGRESSION analysis, PREDICTION models, ARTIFICIAL neural networks
Abstract

Copyright of Canadian Journal of Chemistry 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.)

Published
2012
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10. Initial assessment of bridge backwater using an artificial neural network approach.

Author

Cobaner, Murat, Seckin, Galip, and Kisi, Ozgur

Subjects
BACKWATER, ENERGY dissipation, HYDRAULIC engineering, WATERWAYS, FLOODS, REGRESSION analysis, FLOOD damage
Abstract

Copyright of Canadian Journal of Civil Engineering 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.)

Published
2008
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11. Modeling of hourly NOx concentrations using artificial neural networks.

Author

Hasham, Faizal A., Kindzierski, Warren B., and Stanley, Stephen J.

Subjects
AIR quality, ARTIFICIAL neural networks, AIR quality management, NITROGEN oxides, FORECASTING
Abstract

Copyright of Journal of Environmental Engineering & Science 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.)

Published
2004
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