Prediction of load-displacement curve in a complex structure using artificial neural networks: A study on a long bone

Rahmanpanah, Hadi; Mouloodi, Saeed; Burvill, Colin; Gohari, Soheil; Davies, Helen M.S.

doi:10.1016/j.ijengsci.2020.103319

Cited by 47 publications

(15 citation statements)

References 58 publications

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“…The values of the variables present in the dataset vary across different ranges, which can difficult the learning process of the ANN. Normalizing all the input variables to the same range can improve the training process performance and is a recommended practice (Rahmanpanah et al 2020). In the present work, the input variables were normalized using the mean-std method.…”

Section: Data Pre-processingmentioning

confidence: 99%

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Modeling and optimization of a continuous electrocoagulation process using an artificial intelligence approach

2021

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An artificial neural network (ANN) with the topology 8-94-85-2 (input – hidden layer 1 - hidden layer 2 - output) was used to model the operation of the continuous electrocoagulation (CEC) process for the removal of fluoride from water. After the ANN training, the sum of the squared errors (MSE) and the determination coefficient (R2) of the testing set model predictions were 0.0088 and 0.999, respectively, showing a good generalization and model's predictive capacity. The optimization of the process cost using the genetic algorithm (GA) showed that the optimal conditions are highly dependent on the feed concentration and the fluoride removal requirements. For a 5 L of water containing 10 mg/L of fluoride, the optimal conditions to reduce the fluoride concentration below the permissible limit (1.5 mg/L) are 88.3 mA of current intensity, a flow rate of 73.6 mL/min, and the use of a series monopolar (SM) electrode configuration, corresponding to a fluoride removal of 85% and an operating cost of 0.05 €/L.

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Section: Data Pre-processingmentioning

confidence: 99%

“…A common procedure is the introduction of Gaussian noise to the input variables during the ANN training. The introduction of noise is achieved by (Zadpoor et al 2013;Rahmanpanah et al 2020):…”

Section: Data Pre-processingmentioning

confidence: 99%

Modeling and optimization of a continuous electrocoagulation process using an artificial intelligence approach

2021

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“…Refs. [22,23] study the usage of an artificial neural network to predict bone loading using strain and displacement measurements. To achieve our goal, we used a convolutional neural network, which has the advantage of automatically extracting features from raw accelerometer data using powerful computing capabilities.…”

Section: Related Workmentioning

confidence: 99%

Horse Jumping and Dressage Training Activity Detection Using Accelerometer Data

Eerdekens

Deruyck

Fontaine

et al. 2021

Animals

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Equine training activity detection will help to track and enhance the performance and fitness level of riders and their horses. Currently, the equestrian world is eager for a simple solution that goes beyond detecting basic gaits, yet current technologies fall short on the level of user friendliness and detection of main horse training activities. To this end, we collected leg accelerometer data of 14 well-trained horses during jumping and dressage trainings. For the first time, 6 jumping training and 25 advanced horse dressage activities are classified using specifically developed models based on a neural network. A jumping training could be classified with a high accuracy of 100 %, while a dressage training could be classified with an accuracy of 96.29%. Assigning the dressage movements to 11, 6 or 4 superclasses results in higher accuracies of 98.87%, 99.10% and 100%, respectively. Furthermore, during dressage training, the side of movement could be identified with an accuracy of 97.08%. In addition, a velocity estimation model was developed based on the measured velocities of seven horses performing the collected, working, and extended gaits during a dressage training. For the walk, trot, and canter paces, the velocities could be estimated accurately with a low root mean square error of 0.07 m/s, 0.14 m/s, and 0.42 m/s, respectively.

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“…Since its inception [ 45 ], artificial intelligence (AI) has shown that it can be applied to a wide spectrum of disciplines not directly related to computing. Among the most significant new uses, medicine [ 46 , 47 ], warfare [ 48 ], ecology [ 49 ], security [ 50 ], education [ 51 ], oil exploration [ 52 ], or material science [ 44 , 53 ] can be emphasized. Today, ANNs have become one of the most notable AI methods due to their incredible accomplishments and unstoppable advancement [ 10 , 54 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Mechanical Properties by Artificial Neural Networks to Characterize the Plastic Behavior of Aluminum Alloys

2020

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In metal forming, the plastic behavior of metallic alloys is directly related to their formability, and it has been traditionally characterized by simplified models of the flow curves, especially in the analysis by finite element simulation and analytical methods. Tools based on artificial neural networks have shown high potential for predicting the behavior and properties of industrial components. Aluminum alloys are among the most broadly used materials in challenging industries such as aerospace, automotive, or food packaging. In this study, a computer-aided tool is developed to predict two of the most useful mechanical properties of metallic materials to characterize the plastic behavior, yield strength and ultimate tensile strength. These prognostics are based on the alloy chemical composition, tempers, and Brinell hardness. In this study, a material database is employed to train an artificial neural network that is able to make predictions with a confidence greater than 95%. It is also shown that this methodology achieves a performance similar to that of empirical equations developed expressly for a specific material, but it provides greater generality since it can approximate the properties of any aluminum alloy. The methodology is based on the usage of artificial neural networks supported by a big data collection about the properties of thousands of commercial materials. Thus, the input data go above 2000 entries. When the relevant information has been collected and organized, an artificial neural network is defined, and after the training, the artificial intelligence is able to make predictions about the material properties with an average confidence greater than 95%.

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Prediction of load-displacement curve in a complex structure using artificial neural networks: A study on a long bone

Cited by 47 publications

References 58 publications

Modeling and optimization of a continuous electrocoagulation process using an artificial intelligence approach

Modeling and optimization of a continuous electrocoagulation process using an artificial intelligence approach

Horse Jumping and Dressage Training Activity Detection Using Accelerometer Data

Prediction of Mechanical Properties by Artificial Neural Networks to Characterize the Plastic Behavior of Aluminum Alloys

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