The Experts below are selected from a list of 10152 Experts worldwide ranked by ideXlab platform
Shamsolla Abdolahpour - One of the best experts on this subject based on the ideXlab platform.
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prediction of tractor Repair and Maintenance costs using artificial neural network
Expert Systems With Applications, 2011Co-Authors: Abbas Rohani, Mohammad Hossein Abbaspourfard, Shamsolla AbdolahpourAbstract:The prediction of Repair and Maintenance costs has significant impacts on proper economical decisions making of machinery managers, such as machine's replacement and substitution. In this article the potential of Artificial Neural Network (ANN) technique has evaluated as an alternative method for the prediction of machinery (specifically tractor) Repair and Maintenance costs. The study was conducted using empirical data on 60 two-wheel drive tractors from Astan Ghodse Razavi agro-industry in Iran. Optimal parameters for the network were selected via a trial and error procedure on the available data. In this paper, the performance of Basic Back-propagation (BB) training algorithm was also compared with Back-propagation with Declining Learning-Rate Factor algorithm (BDLRF). It was found that BDLRF has a better performance for the prediction of tractor's costs. The prediction of Repair and Maintenance cost components of tractors with a single network produced a better result than using separate networks for prediction of each cost component. It has been concluded that ANN represents a promising tool for predicting Repair and Maintenance costs.
Salvador García Fortes - One of the best experts on this subject based on the ideXlab platform.
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Microblasting cleaning for façade Repair and Maintenance: Selecting technical parameters for treatment efficiency
Construction and Building Materials, 2015Co-Authors: Manuel Ángel Iglesias-campos, José Luis Prada Pérez, Salvador García FortesAbstract:Abstract This research examines abrasive blasting cleaning on a soft render coating and its effects on the surface. After analysing substrate properties by SEM-EDS, XRD and FTIR-ATR, and their potential influence on treatment, cleaning tests were made with glass beads, micronized glass and aluminium silicate at 45°and 75° blasting angle keeping other parameters constant. As microblasting can leave a rougher surface, influencing subsequent substrate decay, analysis of area field roughness obtained through 3D stereomicroscopy, complemented with macrophotography, portable microscope and spectrophotometry analysis, were used to evaluate cleaning surfaces. Best results were obtained with glass beads, due to its shape and size, using 75° blasting angle. Findings concerning microblasting efficiency and effectiveness, including damage to the substrate and other side effects, are also described. Results allow the selection of parameters of the technique when it is needed for façade Repair and Maintenance.
Abbas Rohani - One of the best experts on this subject based on the ideXlab platform.
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prediction of tractor Repair and Maintenance costs using artificial neural network
Expert Systems With Applications, 2011Co-Authors: Abbas Rohani, Mohammad Hossein Abbaspourfard, Shamsolla AbdolahpourAbstract:The prediction of Repair and Maintenance costs has significant impacts on proper economical decisions making of machinery managers, such as machine's replacement and substitution. In this article the potential of Artificial Neural Network (ANN) technique has evaluated as an alternative method for the prediction of machinery (specifically tractor) Repair and Maintenance costs. The study was conducted using empirical data on 60 two-wheel drive tractors from Astan Ghodse Razavi agro-industry in Iran. Optimal parameters for the network were selected via a trial and error procedure on the available data. In this paper, the performance of Basic Back-propagation (BB) training algorithm was also compared with Back-propagation with Declining Learning-Rate Factor algorithm (BDLRF). It was found that BDLRF has a better performance for the prediction of tractor's costs. The prediction of Repair and Maintenance cost components of tractors with a single network produced a better result than using separate networks for prediction of each cost component. It has been concluded that ANN represents a promising tool for predicting Repair and Maintenance costs.
Manuel Ángel Iglesias-campos - One of the best experts on this subject based on the ideXlab platform.
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Microblasting cleaning for façade Repair and Maintenance: Selecting technical parameters for treatment efficiency
Construction and Building Materials, 2015Co-Authors: Manuel Ángel Iglesias-campos, José Luis Prada Pérez, Salvador García FortesAbstract:Abstract This research examines abrasive blasting cleaning on a soft render coating and its effects on the surface. After analysing substrate properties by SEM-EDS, XRD and FTIR-ATR, and their potential influence on treatment, cleaning tests were made with glass beads, micronized glass and aluminium silicate at 45°and 75° blasting angle keeping other parameters constant. As microblasting can leave a rougher surface, influencing subsequent substrate decay, analysis of area field roughness obtained through 3D stereomicroscopy, complemented with macrophotography, portable microscope and spectrophotometry analysis, were used to evaluate cleaning surfaces. Best results were obtained with glass beads, due to its shape and size, using 75° blasting angle. Findings concerning microblasting efficiency and effectiveness, including damage to the substrate and other side effects, are also described. Results allow the selection of parameters of the technique when it is needed for façade Repair and Maintenance.
Mohammad Hossein Abbaspourfard - One of the best experts on this subject based on the ideXlab platform.
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prediction of tractor Repair and Maintenance costs using artificial neural network
Expert Systems With Applications, 2011Co-Authors: Abbas Rohani, Mohammad Hossein Abbaspourfard, Shamsolla AbdolahpourAbstract:The prediction of Repair and Maintenance costs has significant impacts on proper economical decisions making of machinery managers, such as machine's replacement and substitution. In this article the potential of Artificial Neural Network (ANN) technique has evaluated as an alternative method for the prediction of machinery (specifically tractor) Repair and Maintenance costs. The study was conducted using empirical data on 60 two-wheel drive tractors from Astan Ghodse Razavi agro-industry in Iran. Optimal parameters for the network were selected via a trial and error procedure on the available data. In this paper, the performance of Basic Back-propagation (BB) training algorithm was also compared with Back-propagation with Declining Learning-Rate Factor algorithm (BDLRF). It was found that BDLRF has a better performance for the prediction of tractor's costs. The prediction of Repair and Maintenance cost components of tractors with a single network produced a better result than using separate networks for prediction of each cost component. It has been concluded that ANN represents a promising tool for predicting Repair and Maintenance costs.
