Prediction of Surface Treatment Effects on the Tribological Performance of Tool Steels Using Artificial Neural Networks

Cavaleri, Liborio; Asteris, Panagiotis G.; Psyllaki, P.; Douvika, Maria G.; Skentou, Athanasia D.; Vaxevanidis, Nikolaos M.

doi:10.3390/app9142788

Cited by 66 publications

(39 citation statements)

References 78 publications

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“…The NN model exhibits crucial profits not found in traditional computational methods. Hypotheses or constraints are not necessary when optimizing NNs [111][112][113], and they are also able to analyze and explore complex (even nonlinear) relationships in data [114][115][116]. From a computational point of view, NNs are powerful at solving high dimensional problems because of their processing capabilities in parallel [19,117,118].…”

Section: Neural Network (Nn)mentioning

confidence: 99%

Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams

et al. 2020

Sustainability

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Understanding shear behavior is crucial for the design of reinforced concrete beams and sustainability in construction and civil engineering. Although numerous studies have been proposed, predicting such behavior still needs further improvement. This study proposes a soft-computing tool to predict the ultimate shear capacities (USCs) of concrete beams reinforced with steel fiber, one of the most important factors in structural design. Two hybrid machine learning (ML) algorithms were created that combine neural networks (NNs) with two distinct optimization techniques (i.e., the Real-Coded Genetic Algorithm (RCGA) and the Firefly Algorithm (FFA)): the NN-RCGA and the NN-FFA. A database of 463 experimental data was gathered from reliable literature for the development of the models. After the construction, validation, and selection of the best model based on common statistical criteria, a comparison with the empirical equations available in the literature was carried out. Further, a sensitivity analysis was conducted to evaluate the importance of 16 inputs and reveal the dependency of structural parameters on the USC. The results showed that the NN-RCGA (R = 0.9771) was better than the NN-FFA and other analytical models (R = 0.5274–0.9075). The sensitivity analysis results showed that web width, effective depth, and a clear depth ratio were the most important parameters in modeling the shear capacity of steel fiber-reinforced concrete beams.

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Section: Neural Network (Nn)mentioning

confidence: 99%

Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams

et al. 2020

Sustainability

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“…However, there are several limitations in ANFIS model such as ANFIS is not powerful in searching the best firing strength (i.e., weight) [56,57], which greatly impact the prediction effectiveness [47]. Various investigations have employed different optimization methods to find the weighs of parameters in a better way, for instance using Genetic Algorithm, Particle Swarm Optimization, Grey Wolf Optimizer or Invasive Weed Optimization [58][59][60][61][62][63]. In this paper, we proposed the Teaching-Learning-based Optimization (TLBO) to optimize the parameter's weights in ANFIS.…”

Section: Adaptive Neuro Fuzzy Inference System (Anfis)mentioning

confidence: 99%

Improvement of ANFIS Model for Prediction of Compressive Strength of Manufactured Sand Concrete

Pham

Dao

et al. 2019

Applied Sciences

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Use of manufactured sand to replace natural sand is increasing in the last several decades. This study is devoted to the assessment of using Principal Component Analysis (PCA) together with Teaching-Learning-Based Optimization (TLBO) for enhancing the prediction accuracy of individual Adaptive Neuro Fuzzy Inference System (ANFIS) in predicting the compressive strength of manufactured sand concrete (MSC). The PCA technique was applied for reducing the noise in the input space, whereas, TLBO was employed to increase the prediction performance of single ANFIS model in searching the optimal weights of input parameters. A number of 289 configurations of MSC were used for the simulation, especially including the sand characteristics and the MSC long-term compressive strength. Using various validation criteria such as Correlation Coefficient (R), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), the proposed method was validated and compared with several models, including individual ANFIS, Artificial Neural Networks (ANN) and existing empirical equations. The results showed that the proposed model exhibited great prediction capability compared with other models. Thus, it appeared as a robust alternative computing tool or an efficient soft computing technique for quick and accurate prediction of the MSC compressive strength.

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“…where m is the number of samples, e i and e i are the output and the target values of the i-th samples, respectively. RMSE has the same error metric units as the data, and smaller RMSE values indicate better performance of a model [60,[63][64][65][66][67][68]. AUC is defined as the area under the ROC curve which is constructed using two statistical values: "Sensitivity" and "100-specificity" [52,69,70].…”

Section: Validation Criteriamentioning

confidence: 99%

GIS Based Novel Hybrid Computational Intelligence Models for Mapping Landslide Susceptibility: A Case Study at Da Lat City, Vietnam

Nguyen

Tran

et al. 2019

Sustainability

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Landslides affect properties and the lives of a large number of people in many hilly parts of Vietnam and in the world. Damages caused by landslides can be reduced by understanding distribution, nature, mechanisms and causes of landslides with the help of model studies for better planning and risk management of the area. Development of landslide susceptibility maps is one of the main steps in landslide management. In this study, the main objective is to develop GIS based hybrid computational intelligence models to generate landslide susceptibility maps of the Da Lat province, which is one of the landslide prone regions of Vietnam. Novel hybrid models of alternating decision trees (ADT) with various ensemble methods, namely bagging, dagging, MultiBoostAB, and RealAdaBoost, were developed namely B-ADT, D-ADT, MBAB-ADT, RAB-ADT, respectively. Data of 72 past landslide events was used in conjunction with 11 landslide conditioning factors (curvature, distance from geological boundaries, elevation, land use, Normalized Difference Vegetation Index (NDVI), relief amplitude, stream density, slope, lithology, weathering crust and soil) in the development and validation of the models. Area under the receiver operating characteristic (ROC) curve (AUC), and several statistical measures were applied to validate these models. Results indicated that performance of all the models was good (AUC value greater than 0.8) but B-ADT model performed the best (AUC= 0.856). Landslide susceptibility maps generated using the proposed models would be helpful to decision makers in the risk management for land use planning and infrastructure development.

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Prediction of Surface Treatment Effects on the Tribological Performance of Tool Steels Using Artificial Neural Networks

Cited by 66 publications

References 78 publications

Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams

Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams

Improvement of ANFIS Model for Prediction of Compressive Strength of Manufactured Sand Concrete

GIS Based Novel Hybrid Computational Intelligence Models for Mapping Landslide Susceptibility: A Case Study at Da Lat City, Vietnam

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