scholarly journals Development of Crack Width Prediction Models for RC Beam-Column Joint Subjected to Lateral Cyclic Loading Using Machine Learning

2021 ◽  
Vol 11 (16) ◽  
pp. 7700
Author(s):  
Reventheran Ganasan ◽  
Chee Ghuan Tan ◽  
Zainah Ibrahim ◽  
Fadzli Mohamed Nazri ◽  
Muhammad M. Sherif ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Cyclic Loading ◽  
Crack Width ◽  
Prediction Models ◽  
Design Parameters ◽  
Rc Beam ◽  
Performance Indices ◽  
Column Joint ◽  
And Performance

In recent years, researchers have investigated the development of artificial neural networks (ANN) and finite element models (FEM) for predicting crack propagation in reinforced concrete (RC) members. However, most of the developed prediction models have been limited to focus on individual isolated RC members without considering the interaction of members in a structure subjected to hazard loads, due to earthquake and wind. This research develops models to predict the evolution of the cracks in the RC beam-column joint (BCJ) region. The RC beam-column joint is subjected to lateral cyclic loading. Four machine learning models are developed using Rapidminer to predict the crack width experienced by seven RC beam-column joints. The design parameters associated with RC beam-column joints and lateral cyclic loadings in terms of drift ratio are used as inputs. Several prediction models are developed, and the highest performing neural networks are selected, refined, and optimized using the various split data ratios, number of inputs, and performance indices. The error in predicting the experimental crack width is used as a performance index.

Bridging Finite Element and Machine Learning Modeling: Stress Prediction of Arterial Walls in Atherosclerosis

2019 ◽  
Vol 141 (8) ◽  
Author(s):  
Ali Madani ◽  
Ahmed Bakhaty ◽  
Jiwon Kim ◽  
Yara Mubarak ◽  
Mohammad R. K. Mofrad
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Finite Element ◽  
Deep Neural Networks ◽  
Prediction Models ◽  
Plaque Rupture ◽  
Training Data ◽  
Von Mises Stress ◽  
And Performance ◽  
Von Mises

Finite element and machine learning modeling are two predictive paradigms that have rarely been bridged. In this study, we develop a parametric model to generate arterial geometries and accumulate a database of 12,172 2D finite element simulations modeling the hyperelastic behavior and resulting stress distribution. The arterial wall composition mimics vessels in atherosclerosis–a complex cardiovascular disease and one of the leading causes of death globally. We formulate the training data to predict the maximum von Mises stress, which could indicate risk of plaque rupture. Trained deep learning models are able to accurately predict the max von Mises stress within 9.86% error on a held-out test set. The deep neural networks outperform alternative prediction models and performance scales with amount of training data. Lastly, we examine the importance of contributing features on stress value and location prediction to gain intuitions on the underlying process. Moreover, deep neural networks can capture the functional mapping described by the finite element method, which has far-reaching implications for real-time and multiscale prediction tasks in biomechanics.

scholarly journals Survival prediction models since liver transplantation - comparisons between Cox models and machine learning techniques

2020 ◽  
Author(s):  
Georgios Kantidakis ◽  
Hein Putter ◽  
Carlo Lancia ◽  
Jacob de Boer ◽  
Andries E Braat ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Liver Transplantation ◽  
Prediction Models ◽  
Machine Learning Techniques ◽  
Brier Score ◽  
Survival Prediction ◽  
Cox Models ◽  
Learning Techniques ◽  
Random Survival Forest

Abstract Background: Predicting survival of recipients after liver transplantation is regarded as one of the most important challenges in contemporary medicine. Hence, improving on current prediction models is of great interest.Nowadays, there is a strong discussion in the medical field about machine learning (ML) and whether it has greater potential than traditional regression models when dealing with complex data. Criticism to ML is related to unsuitable performance measures and lack of interpretability which is important for clinicians.Methods: In this paper, ML techniques such as random forests and neural networks are applied to large data of 62294 patients from the United States with 97 predictors selected on clinical/statistical grounds, over more than 600, to predict survival from transplantation. Of particular interest is also the identification of potential risk factors. A comparison is performed between 3 different Cox models (with all variables, backward selection and LASSO) and 3 machine learning techniques: a random survival forest and 2 partial logistic artificial neural networks (PLANNs). For PLANNs, novel extensions to their original specification are tested. Emphasis is given on the advantages and pitfalls of each method and on the interpretability of the ML techniques.Results: Well-established predictive measures are employed from the survival field (C-index, Brier score and Integrated Brier Score) and the strongest prognostic factors are identified for each model. Clinical endpoint is overall graft-survival defined as the time between transplantation and the date of graft-failure or death. The random survival forest shows slightly better predictive performance than Cox models based on the C-index. Neural networks show better performance than both Cox models and random survival forest based on the Integrated Brier Score at 10 years.Conclusion: In this work, it is shown that machine learning techniques can be a useful tool for both prediction and interpretation in the survival context. From the ML techniques examined here, PLANN with 1 hidden layer predicts survival probabilities the most accurately, being as calibrated as the Cox model with all variables.

scholarly journals Survival prediction models since liver transplantation - comparisons between Cox models and machine learning techniques

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Georgios Kantidakis ◽  
Hein Putter ◽  
Carlo Lancia ◽  
Jacob de Boer ◽  
Andries E. Braat ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Neural Networks ◽  
Liver Transplantation ◽  
Prediction Models ◽  
Machine Learning Techniques ◽  
Brier Score ◽  
Cox Models ◽  
Learning Techniques ◽  
Random Survival Forest

Abstract Background Predicting survival of recipients after liver transplantation is regarded as one of the most important challenges in contemporary medicine. Hence, improving on current prediction models is of great interest.Nowadays, there is a strong discussion in the medical field about machine learning (ML) and whether it has greater potential than traditional regression models when dealing with complex data. Criticism to ML is related to unsuitable performance measures and lack of interpretability which is important for clinicians. Methods In this paper, ML techniques such as random forests and neural networks are applied to large data of 62294 patients from the United States with 97 predictors selected on clinical/statistical grounds, over more than 600, to predict survival from transplantation. Of particular interest is also the identification of potential risk factors. A comparison is performed between 3 different Cox models (with all variables, backward selection and LASSO) and 3 machine learning techniques: a random survival forest and 2 partial logistic artificial neural networks (PLANNs). For PLANNs, novel extensions to their original specification are tested. Emphasis is given on the advantages and pitfalls of each method and on the interpretability of the ML techniques. Results Well-established predictive measures are employed from the survival field (C-index, Brier score and Integrated Brier Score) and the strongest prognostic factors are identified for each model. Clinical endpoint is overall graft-survival defined as the time between transplantation and the date of graft-failure or death. The random survival forest shows slightly better predictive performance than Cox models based on the C-index. Neural networks show better performance than both Cox models and random survival forest based on the Integrated Brier Score at 10 years. Conclusion In this work, it is shown that machine learning techniques can be a useful tool for both prediction and interpretation in the survival context. From the ML techniques examined here, PLANN with 1 hidden layer predicts survival probabilities the most accurately, being as calibrated as the Cox model with all variables. Trial registration Retrospective data were provided by the Scientific Registry of Transplant Recipients under Data Use Agreement number 9477 for analysis of risk factors after liver transplantation.

scholarly journals A Proof of Concept Study of Using Machine-Learning in Artificial Aortic Valve Design: From Leaflet Design to Stress Analysis

2019 ◽  
Vol 6 (4) ◽  
pp. 104 ◽  
Author(s):  
Liang Liang ◽  
Bill Sun
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Aortic Valve ◽  
Stress Analysis ◽  
Heart Valves ◽  
Deep Neural Networks ◽  
Design Parameters ◽  
Attractive Alternative ◽  
Proof Of Concept ◽  
Concept Study

Artificial heart valves, used to replace diseased human heart valves, are life-saving medical devices. Currently, at the device development stage, new artificial valves are primarily assessed through time-consuming and expensive benchtop tests or animal implantation studies. Computational stress analysis using the finite element (FE) method presents an attractive alternative to physical testing. However, FE computational analysis requires a complex process of numeric modeling and simulation, as well as in-depth engineering expertise. In this proof of concept study, our objective was to develop machine learning (ML) techniques that can estimate the stress and deformation of a transcatheter aortic valve (TAV) from a given set of TAV leaflet design parameters. Two deep neural networks were developed and compared: the autoencoder-based ML-models and the direct ML-models. The ML-models were evaluated through Monte Carlo cross validation. From the results, both proposed deep neural networks could accurately estimate the deformed geometry of the TAV leaflets and the associated stress distributions within a second, with the direct ML-models (ML-model-d) having slightly larger errors. In conclusion, although this is a proof-of-concept study, the proposed ML approaches have demonstrated great potential to serve as a fast and reliable tool for future TAV design.

Seismic Behavior of RC Beam to Partially Steel Tubed Concrete (PSTC) Column Joints Under High Axial Load and Cyclic Loading

2016 ◽  
Vol 16 (01) ◽  
pp. 1640015 ◽  
Author(s):  
Yun Tian Wu ◽  
Yu Shan Fu ◽  
Chong-Ming Dai
Keyword(s):  
Energy Dissipation ◽  
Cyclic Loading ◽  
Axial Load ◽  
Deformation Energy ◽  
Rc Beam ◽  
Weak Beam ◽  
Column Joint ◽  
Energy Dissipation Capacity ◽  
Beam Mechanism ◽  
Strength And Stiffness

A new type of partially steel tubed concrete (PSTC) column is proposed that is suitable to be used in new high rise reinforced concrete (RC) buildings. Three exterior joint specimens consisting of RC beams and PSTC columns and two exterior RC joint specimens were designed and tested under high axial load and cyclic loading to investigate the joint behavior in terms of failure pattern, hysteresis response, deformation, energy dissipation capacity and degradation of strength and stiffness. Test results indicate that the PSTC column can benefit the performance of the joint in terms of strength, ductility and energy dissipation capacity and can partly compensate for the unfavorable effect induced by slab. The strong column–weak beam mechanism can also be ensured in RC beam to PSTC column joint.

scholarly journals Evaluation of crowdsourced mortality prediction models as a framework for assessing AI in medicine

2021 ◽  
Author(s):  
Timothy Bergquist ◽  
Thomas Schaffter ◽  
Yao Yan ◽  
Thomas Yu ◽  
Justin Prosser ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Improve Patient Care ◽  
Mortality Prediction ◽  
Direct Access ◽  
Healthcare Outcomes ◽  
Patient Privacy ◽  
Mortality Prediction Models ◽  
And Performance ◽  
Similar Accuracy

AbstractApplications of machine learning in healthcare are of high interest and have the potential to significantly improve patient care. Yet, the real-world accuracy and performance of these models on different patient subpopulations remains unclear. To address these important questions, we hosted a community challenge to evaluate different methods that predict healthcare outcomes. To overcome patient privacy concerns, we employed a Model-to-Data approach, allowing citizen scientists and researchers to train and evaluate machine learning models on private health data without direct access to that data. We focused on the prediction of all-cause mortality as the community challenge question. In total, we had 345 registered participants, coalescing into 25 independent teams, spread over 3 continents and 10 countries. The top performing team achieved a final area under the receiver operator curve of 0.947 (95% CI 0.942, 0.951) and an area under the precision-recall curve of 0.487 (95% CI 0.458, 0.499) on patients prospectively collected over a one year observation of a large health system. Post-hoc analysis after the challenge revealed that models differ in accuracy on subpopulations, delineated by race or gender, even when they are trained on the same data and have similar accuracy on the population. This is the largest community challenge focused on the evaluation of state-of-the-art machine learning methods in a healthcare system performed to date, revealing both opportunities and pitfalls of clinical AI.

scholarly journals State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1301 ◽  
Author(s):  
Amir Mosavi ◽  
Mohsen Salimi ◽  
Sina Faizollahzadeh Ardabili ◽  
Timon Rabczuk ◽  
Shahaboddin Shamshirband ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Demand ◽  
Prediction Models ◽  
State Of The Art ◽  
Energy Systems ◽  
Demand Prediction ◽  
Energy Governance ◽  
Challenges And Opportunities ◽  
And Performance ◽  
Energy Type

Machine learning (ML) models have been widely used in the modeling, design and prediction in energy systems. During the past two decades, there has been a dramatic increase in the advancement and application of various types of ML models for energy systems. This paper presents the state of the art of ML models used in energy systems along with a novel taxonomy of models and applications. Through a novel methodology, ML models are identified and further classified according to the ML modeling technique, energy type, and application area. Furthermore, a comprehensive review of the literature leads to an assessment and performance evaluation of the ML models and their applications, and a discussion of the major challenges and opportunities for prospective research. This paper further concludes that there is an outstanding rise in the accuracy, robustness, precision and generalization ability of the ML models in energy systems using hybrid ML models. Hybridization is reported to be effective in the advancement of prediction models, particularly for renewable energy systems, e.g., solar energy, wind energy, and biofuels. Moreover, the energy demand prediction using hybrid models of ML have highly contributed to the energy efficiency and therefore energy governance and sustainability.

scholarly journals Predicting at-risk university students based on their e-book reading behaviours by using machine learning classifiers

2021 ◽  
pp. 130-144
Author(s):  
Cheng-Huan Chen ◽  
Stephen J. H. Yang ◽  
Jian-Xuan Weng ◽  
Hiroaki Ogata ◽  
Chien-Yuan Su
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Academic Achievement ◽  
At Risk ◽  
Logistic Regression ◽  
Prediction Models ◽  
At Risk Students ◽  
Prediction Performance ◽  
Online Reading ◽  
Machine Learning Classifiers

Providing early predictions of academic performance is necessary for identifying at-risk students and subsequently providing them with timely intervention for critical factors affecting their academic performance. Although e-book systems are often used to provide students with teaching/learning materials in university courses, seldom has research made the early prediction based on their online reading behaviours by implementing machine learning classifiers. This study explored to what extent university students’ academic achievement can be predicted, based on their reading behaviours in an e-book supported course, using the classifiers. It further investigated which of the features extracted from the reading logs influence the predictions. The participants were 100 first-year undergraduates enrolled in a compulsory course at a university in Taiwan. The results suggest that logistic regression supports vector classification, decision trees, and random forests, and neural networks achieved moderate prediction performance with accuracy, precision, and recall metrics. The Bayes classifier identified almost all at-risk students. Additionally, student online reading behaviours affecting the prediction models included: turning pages, going back to previous pages and jumping to other pages, adding/deleting markers, and editing/removing memos. These behaviours were significantly positively correlated to academic achievement and should be encouraged during courses supported by e-books. Implications for practice or policy: For identifying at-risk students, educators could prioritise using Gaussian naïve Bayes in an e-book supported course, as it shows almost perfect recall performance. Assessors could give priority to logistic regression and neural networks in this context because they have stable achievement prediction performance with different evaluation metrics. The prediction models are strongly affected by student online reading behaviours, in particular by locating/returning to relevant pages and modifying markers.

scholarly journals Survival prediction models since liver transplantation - comparisons between Cox models and machine learning techniques

2020 ◽  
Author(s):  
Georgios Kantidakis ◽  
Hein Putter ◽  
Carlo Lancia ◽  
Jacob de Boer ◽  
Andries E Braat ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Liver Transplantation ◽  
Prediction Models ◽  
Machine Learning Techniques ◽  
Brier Score ◽  
Survival Prediction ◽  
Cox Models ◽  
Learning Techniques ◽  
Random Survival Forest

Abstract Background: Predicting survival of recipients after liver transplantation is regarded as one of the most important challenges in contemporary medicine. Hence, improving on current prediction models is of great interest. Nowadays, there is a strong discussion in the medical field about machine learning (ML) and whether it has greater potential than traditional regression models when dealing with complex data. Criticism to ML is related to unsuitable performance measures and lack of interpretability which is important for clinicians. Methods: In this paper, ML techniques such as random forests and neural networks are applied to large data of 62294 patients from the United States with 97 predictors selected on clinical/statistical grounds, over more than 600, to predict survival from transplantation. Of particular interest is also the identification of potential risk factors. A comparison is performed between 3 different Cox models (with all variables, backward selection and LASSO) and 3 machine learning techniques: a random survival forest and 2 partial logistic artificial neural networks (PLANNs). For PLANNs, novel extensions to their original specification are tested. Emphasis is given on the advantages and pitfalls of each method and on the interpretability of the ML techniques. Results: Well-established predictive measures are employed from the survival field (C-index, Brier score and Integrated Brier Score) and the strongest prognostic factors are identified for each model. Clinical endpoint is overall graft-survival defined as the time between transplantation and the date of graft-failure or death. The random survival forest shows slightly better predictive performance than Cox models based on the C-index. Neural networks show better performance than both Cox models and random survival forest based on the Integrated Brier Score at 10 years. Conclusion: In this work, it is shown that machine learning techniques can be a useful tool for both prediction and interpretation in the survival context. From the ML techniques examined here, PLANN with 1 hidden layer predicts survival probabilities the most accurately, being as calibrated as the Cox model with all variables.

Flexural Performance of Exterior Steel Fiber Reinforced Self-Compacting Concrete Beam-Column Joint Subjected to Reversed Cyclic Loading

2020 ◽  
Vol 17 (9) ◽  
pp. 3934-3939
Author(s):  
B. Nambiyanna ◽  
M. N. Chandan ◽  
R. Prabhakara
Keyword(s):  
Energy Dissipation ◽  
Cyclic Loading ◽  
Steel Fibers ◽  
Self Compacting Concrete ◽  
Reversed Cyclic Loading ◽  
Moment Resisting Frame ◽  
Moment Resisting ◽  
Column Joint ◽  
And Performance ◽  
Reversed Cyclic

Beam-Column Joint plays an imperative part in the seismic enactment of Reinforced concrete moment resisting frame. The response of the structure under the earthquake loads mainly depends on the behavior and performance of the Beam-Column Joint (BCJ). An experimental investigation on exterior reinforced beam-column joints done using steel fibre reinforced self-compacting concrete subjected to reversed cyclic loading is presented in this investigation. The attempt has been made to demonstrate the influence of steel fibers reinforced self-compacting concrete (SCC). Three exterior beam-column joints having the same geometry were cast and tested through reversed cyclic load using displacement-controlled mechanism. All three specimens are designed adequately in accordance with IS 13920:2016 code. The hysteresis loop, ductility factor, stiffness degradation, energy dissipation characteristics, and damage propagation are considered to quantify the response of joint specimens. The results show enhanced ductility, energy dissipation capacity and damage tolerance behavior with the increase in steel fibers percentage in SCC.

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