Application of artificial neural network for predicting plain strain fracture toughness using tensile test results

2006 ◽  
Vol 29 (4) ◽  
pp. 321-329 ◽  
Author(s):  
J. Y. KANG ◽  
B. I. CHOI ◽  
H. J. LEE
Keyword(s):  
Neural Network ◽  
Fracture Toughness ◽  
Artificial Neural Network ◽  
Tensile Test ◽  
Test Results ◽  
Strain Fracture ◽  
Artificial Neural ◽  
Plain Strain

scholarly journals Prediction of the Dynamic Stiffness of Resilient Materials using Artificial Neural Network (ANN) Technique

2019 ◽  
Vol 9 (6) ◽  
pp. 1088 ◽  
Author(s):  
Changhyuk Kim ◽  
Jung-Yoon Lee ◽  
Moonhyun Kim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Residential Buildings ◽  
Dynamic Stiffness ◽  
Test Results ◽  
High Rise ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Sound Reduction ◽  
Reduction Capacity

High-rise residential buildings are constructed in countries with high population density in response to the need to utilize small development areas. As many high-rise buildings are being constructed, issues of floor impact sound tend to occur in buildings. In general, resilient materials are implemented between the slab and the finishing mortar to control the floor impact sound. Various mechanical properties of resilient materials can affect the floor impact sound. To investigate the impact sound reduction capacity, various experimental tests were conducted. The test results show that the floor impact sound reduction capacity has a close relationship with the dynamic stiffness of resilient materials. A total of six different kinds of resilient materials were loaded under four loading conditions. The test results show that loading time, loading, and material properties influence the change in dynamic stiffness. Artificial neural network (ANN) technique was implemented to obtain the responses between the deflection and dynamic stiffness. Three different algorithms were considered in the ANN models and the trained results were analyzed based on the root mean square error. The feasibility of using the ANN technique was verified with a high and consistent level of accuracy.

Soft Computing Tools to Predict Progression of Percent Embedment of Aggregates in Chip Seals

2018 ◽  
Vol 2672 (12) ◽  
pp. 32-39 ◽  
Author(s):  
Aksel Seitllari ◽  
M. Emin Kutay
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soft Computing ◽  
Embedment Depth ◽  
Test Results ◽  
Multilinear Regression ◽  
Neuro Fuzzy ◽  
Chip Seal ◽  
Regression Techniques ◽  
Artificial Neural

In this study, soft computing and multilinear regression techniques were employed to develop models for prediction of progression of chip seal percent embedment depth ( Pe). The model uses inputs such as cumulative equivalent traffic volume, Vialit test results, dust content of aggregates, and initial embedment depth. Multilinear regression, adaptive neuro-fuzzy system, and artificial neural network techniques were used to estimate the Pe. The contribution of the variables affecting Pe was evaluated through a sensitivity analysis. The results indicate that while most of the proposed models were able to predict the Pe reasonably, the artificial neural network model performed the best.

Modeling and computation by artificial neural network of fracture toughness of low alloy steel to study the effect of alloy composition

2018 ◽  
Vol 09 (06) ◽  
pp. 1850051
Author(s):  
Subir Paul ◽  
Shibasish Bhattacharjee
Keyword(s):  
Neural Network ◽  
Fracture Toughness ◽  
Artificial Neural Network ◽  
Alloy Steel ◽  
Alloy Composition ◽  
Low Alloy Steel ◽  
Ann Model ◽  
Material Chemistry ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The unpredictable structure failures of carbon steel and low alloy steel leading to accidents may be caused by the propagation of a flaw or crack already present in the structure. Fracture toughness which describes the ability of a material containing a crack to resist fracture is one of the most important material properties for design applications of metallic structures. Since this material property is influenced by several parameters, namely material chemistry, heat treatment, morphology of structure, it requires millions of experiments to be conducted to understand and predict it. So, mathematical modeling is one of the solutions to find the effect of these parameters and design future alloys. Stress–intensity factor [Formula: see text] is a quantitative parameter of fracture toughness determining a maximum value of stress which may be applied to a specimen containing a crack (notch) of a certain length. An artificial neural network (ANN) model was developed using over 100 sets of data to study the effect of alloying elements on fracture toughness, [Formula: see text] for the low alloy steel. 20% of data was used for training, 60% to develop predictive model and rest of the 20% for validation. The model can predict the fracture toughness of unknown new data close to 80% accuracy which is good enough for statistical modeling. The details of program code with ANN modeling steps have been explained. Prediction of fracture toughness by the model with variation of alloy composition as well as yield stress gives interesting and important information which may help in designing alloy which will resist crack propagation in a structure and hence enhance the life of structure to fail.

scholarly journals Prediction of Waste Heat Energy Recovery Performance in a Naturally Aspirated Engine Using Artificial Neural Network

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Safarudin Gazali Herawan ◽  
Abdul Hakim Rohhaizan ◽  
Azma Putra ◽  
Ahmad Faris Ismail
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Waste Heat ◽  
Simulation Method ◽  
Spark Ignition Engine ◽  
Test Results ◽  
Simulation Test ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Heat Energy Recovery

The waste heat from exhaust gases represents a significant amount of thermal energy, which has conventionally been used for combined heating and power applications. This paper explores the performance of a naturally aspirated spark ignition engine equipped with waste heat recovery mechanism (WHRM). The experimental and simulation test results suggest that the concept is thermodynamically feasible and could significantly enhance the system performance depending on the load applied to the engine. The simulation method is created using an artificial neural network (ANN) which predicts the power produced from the WHRM.

scholarly journals Assessing the risk of dengue severity using demographic information and laboratory test results with machine learning

2020 ◽  
Vol 14 (12) ◽  
pp. e0008960
Author(s):  
Sheng-Wen Huang ◽  
Huey-Pin Tsai ◽  
Su-Jhen Hung ◽  
Wen-Chien Ko ◽  
Jen-Ren Wang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Laboratory Test ◽  
Prognostic Models ◽  
Severe Dengue ◽  
Test Results ◽  
Demographic Information ◽  
Laboratory Test Results ◽  
Artificial Neural

Background Dengue virus causes a wide spectrum of disease, which ranges from subclinical disease to severe dengue shock syndrome. However, estimating the risk of severe outcomes using clinical presentation or laboratory test results for rapid patient triage remains a challenge. Here, we aimed to develop prognostic models for severe dengue using machine learning, according to demographic information and clinical laboratory data of patients with dengue. Methodology/Principal findings Out of 1,581 patients in the National Cheng Kung University Hospital with suspected dengue infections and subjected to NS1 antigen, IgM and IgG, and qRT-PCR tests, 798 patients including 138 severe cases were enrolled in the study. The primary target outcome was severe dengue. Machine learning models were trained and tested using the patient dataset that included demographic information and qualitative laboratory test results collected on day 1 when they sought medical advice. To develop prognostic models, we applied various machine learning methods, including logistic regression, random forest, gradient boosting machine, support vector classifier, and artificial neural network, and compared the performance of the methods. The artificial neural network showed the highest average discrimination area under the receiver operating characteristic curve (0.8324 ± 0.0268) and balance accuracy (0.7523 ± 0.0273). According to the model explainer that analyzed the contributions/co-contributions of the different factors, patient age and dengue NS1 antigenemia were the two most important risk factors associated with severe dengue. Additionally, co-existence of anti-dengue IgM and IgG in patients with dengue increased the probability of severe dengue. Conclusions/Significance We developed prognostic models for the prediction of dengue severity in patients, using machine learning. The discriminative ability of the artificial neural network exhibited good performance for severe dengue prognosis. This model could help clinicians obtain a rapid prognosis during dengue outbreaks. However, the model requires further validation using external cohorts in future studies.

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