Estimating Collapse Pressure of Centralizer Subs From Machine Learning Models

2019 ◽  
Author(s):  
Ishita Chakraborty
Keyword(s):  
Machine Learning ◽  
Finite Element Analysis ◽  
Finite Element ◽  
Regression Model ◽  
Learning Models ◽  
Element Analysis ◽  
Collapse Pressure ◽  
Geometrical Features ◽  
Past Data ◽  
Machine Learning Models

Abstract Centralizer subs are run in conjunction with the casing strings in the oil/gas wells to ensure that the casing is centralized while it is installed down hole. Centralizer subs are fabricated of stronger material than the casing strings and designed such that it can sustain a higher collapse pressure than the attached tubing string. A typical centralizer sub is a tube with some complex geometrical features, so the collapse pressure of a centralizer sub can only be estimated by conducting a finite element analysis or subjecting it to a collapse pressure test. Both the options are time consuming and expensive. In this work, a machine learning based regression model is used to derive a parametric equation for calculating the collapse pressure of a centralizer sub. The data needed to train and cross validate the regression model is obtained from finite element analysis (FEA). This machine learning based equation provides a closer estimate of the collapse pressure of the centralizer subs to the results obtained from the FEA than the existing collapse prediction equations from API RP 1111. This machine learning based estimation of collapse pressure will help in correctly predicting the collapse rating of the centralizer sub without performing FEA or testing for each individual subs. This approach of building machine learning models from data generated from FEA can be used for analysis of other equipment as well. With the availability of past data collected/generated through years, the recent advances in machine learning can be used to save time and resources.

scholarly journals A Machine Learning Approach as a Surrogate for a Finite Element Analysis: Status of Research and Application to One Dimensional Systems

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1654
Author(s):  
Poojitha Vurtur Badarinath ◽  
Maria Chierichetti ◽  
Fatemeh Davoudi Kakhki
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Finite Element Analysis ◽  
Finite Element ◽  
Artificial Neural Networks ◽  
Stress Distribution ◽  
Maintenance Scheduling ◽  
Element Analysis ◽  
One Dimensional ◽  
Artificial Neural

Current maintenance intervals of mechanical systems are scheduled a priori based on the life of the system, resulting in expensive maintenance scheduling, and often undermining the safety of passengers. Going forward, the actual usage of a vehicle will be used to predict stresses in its structure, and therefore, to define a specific maintenance scheduling. Machine learning (ML) algorithms can be used to map a reduced set of data coming from real-time measurements of a structure into a detailed/high-fidelity finite element analysis (FEA) model of the same system. As a result, the FEA-based ML approach will directly estimate the stress distribution over the entire system during operations, thus improving the ability to define ad-hoc, safe, and efficient maintenance procedures. The paper initially presents a review of the current state-of-the-art of ML methods applied to finite elements. A surrogate finite element approach based on ML algorithms is also proposed to estimate the time-varying response of a one-dimensional beam. Several ML regression models, such as decision trees and artificial neural networks, have been developed, and their performance is compared for direct estimation of the stress distribution over a beam structure. The surrogate finite element models based on ML algorithms are able to estimate the response of the beam accurately, with artificial neural networks providing more accurate results.

scholarly journals Real-time biomechanical modeling of the liver using Machine Learning models trained on Finite Element Method simulations

2020 ◽  
Vol 143 ◽  
pp. 113083 ◽  
Author(s):  
Oscar J. Pellicer-Valero ◽  
María José Rupérez ◽  
Sandra Martínez-Sanchis ◽  
José D. Martín-Guerrero
Keyword(s):  
Machine Learning ◽  
Finite Element Method ◽  
Finite Element ◽  
Real Time ◽  
Biomechanical Modeling ◽  
Learning Models ◽  
Machine Learning Models ◽  
Element Method

Machine learning predictions on fracture toughness of multiscale bio-nano-composites

2020 ◽  
Vol 39 (15-16) ◽  
pp. 587-598 ◽  
Author(s):  
Vahid Daghigh ◽  
Thomas E Lacy ◽  
Hamid Daghigh ◽  
Grace Gu ◽  
Kourosh T Baghaei ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Machine Learning ◽  
Fracture Toughness ◽  
Finite Element Analysis ◽  
Finite Element ◽  
Nano Composites ◽  
Natural Material ◽  
Element Analysis ◽  
Adaptive Boosting ◽  
Impact Tests

Tailorability is an important advantage of composites. Incorporating new bio-reinforcements into composites can contribute to using agricultural wastes and creating tougher and more reliable materials. Nevertheless, the huge number of possible natural material combinations works against finding optimal composite designs. Here, machine learning was employed to effectively predict fracture toughness properties of multiscale bio-nano-composites. Charpy impact tests were conducted on composites with various combinations of two new bio fillers, pistachio shell powders, and fractal date seed particles, as well as nano-clays and short latania fibers, all which reinforce a poly(propylene)/ethylene–propylene–diene-monomer matrix. The measured energy absorptions obtained were used to calculate strain energy release rates as a fracture toughness parameter using linear elastic fracture mechanics and finite element analysis approaches. Despite the limited number of training data obtained from these impact tests and finite element analysis, the machine learning results were accurate for prediction and optimal design. This study applied the decision tree regressor and adaptive boosting regressor machine learning methods in contrast to the K-nearest neighbor regressor machine learning approach used in our previous study for heat deflection temperature predictions. Scanning electron microscopy, optical microscopy, and transmission electron microscopy were used to study the nano-clay dispersion and impact fracture morphology.

scholarly journals Prediction of perinatal death using machine learning models: a birth registry-based cohort study in northern Tanzania

BMJ Open ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. e040132
Author(s):  
Innocent B Mboya ◽  
Michael J Mahande ◽  
Mohanad Mohammed ◽  
Joseph Obure ◽  
Henry G Mwambi
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Perinatal Death ◽  
Learning Models ◽  
Net Benefit ◽  
Birth Registry ◽  
Perinatal Deaths ◽  
Machine Learning Models

ObjectiveWe aimed to determine the key predictors of perinatal deaths using machine learning models compared with the logistic regression model.DesignA secondary data analysis using the Kilimanjaro Christian Medical Centre (KCMC) Medical Birth Registry cohort from 2000 to 2015. We assessed the discriminative ability of models using the area under the receiver operating characteristics curve (AUC) and the net benefit using decision curve analysis.SettingThe KCMC is a zonal referral hospital located in Moshi Municipality, Kilimanjaro region, Northern Tanzania. The Medical Birth Registry is within the hospital grounds at the Reproductive and Child Health Centre.ParticipantsSingleton deliveries (n=42 319) with complete records from 2000 to 2015.Primary outcome measuresPerinatal death (composite of stillbirths and early neonatal deaths). These outcomes were only captured before mothers were discharged from the hospital.ResultsThe proportion of perinatal deaths was 3.7%. There were no statistically significant differences in the predictive performance of four machine learning models except for bagging, which had a significantly lower performance (AUC 0.76, 95% CI 0.74 to 0.79, p=0.006) compared with the logistic regression model (AUC 0.78, 95% CI 0.76 to 0.81). However, in the decision curve analysis, the machine learning models had a higher net benefit (ie, the correct classification of perinatal deaths considering a trade-off between false-negatives and false-positives)—over the logistic regression model across a range of threshold probability values.ConclusionsIn this cohort, there was no significant difference in the prediction of perinatal deaths between machine learning and logistic regression models, except for bagging. The machine learning models had a higher net benefit, as its predictive ability of perinatal death was considerably superior over the logistic regression model. The machine learning models, as demonstrated by our study, can be used to improve the prediction of perinatal deaths and triage for women at risk.

scholarly journals Machine Learning and Prediction of All-Cause Mortality among Chinese Older Adults

2021 ◽  
Author(s):  
Xurui Jin ◽  
Yiyang Sun ◽  
Tinglong Zhu ◽  
Yu Leng ◽  
Shuyi Guan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Regression Model ◽  
Predictive Value ◽  
Cox Regression ◽  
Brier Score ◽  
Baseline Survey ◽  
Learning Models ◽  
All Cause Mortality ◽  
Machine Learning Models

AbstractBackground and aimMortality risk stratification was vital for targeted intervention. This study aimed at building the prediction model of all-cause mortality among Chinese dwelling elderly with different methods including regression models and machine learning models and to compare the performance of machine learning models with regression model on predicting mortality. Additionally, this study also aimed at ranking the predictors of mortality within different models and comparing the predictive value of different groups of predictors using the model with best performance.MethodI used data from the sub-study of Chinese Longitudinal Healthy Longevity Survey (CLHLS) - Healthy Ageing and Biomarkers Cohort Study (HABCS). The baseline survey of HABCS was conducted in 2008 and covered similar domains that CLHLS has investigated and shared the sampling strategy. The follow-up of HABCS was conducted every 2-3 years till 2018.The analysis sample included 2,448 participants from HABCS. I used totally 117 predictors to build the prediction model for survival using the HABCS cohort, including 61 questionnaire, 41 biomarker and 15 genetics predictors. Four models were built (XG-Boost, random survival forest [RSF], Cox regression with all variables and Cox-backward). We used C-index and integrated Brier score (Brier score for the two years’ mortality prediction model) to evaluate the performance of those models.ResultsThe XG-Boost model and RSF model shows slightly better predictive performance than Cox models and Cox-backward models based on the C-index and integrated Brier score in predicting surviving. Age. Activity of daily living and Mini-Mental State Examination score were identified as the top 3 predictors in the XG-Boost and RSF models. Biomarker and questionnaire predictors have a similar predictive value, while genetic predictors have no addictive predictive value when combined with questionnaire or biomarker predictors.ConclusionIn this work, it is shown that machine learning techniques can be a useful tool for both prediction and its performance sightly outperformed the regression model in predicting survival.

scholarly journals Machine learning-based prediction model for responses of bDMARDs in patients with rheumatoid arthritis and ankylosing spondylitis

2021 ◽  
Vol 23 (1) ◽  
Author(s):  
Seulkee Lee ◽  
Seonyoung Kang ◽  
Yeonghee Eun ◽  
Hong-Hee Won ◽  
Hyungjin Kim ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Machine Learning ◽  
Logistic Regression ◽  
Ankylosing Spondylitis ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Prediction Performance ◽  
Learning Models ◽  
Independent Test ◽  
Machine Learning Models

Abstract Background Few studies on rheumatoid arthritis (RA) have generated machine learning models to predict biologic disease-modifying antirheumatic drugs (bDMARDs) responses; however, these studies included insufficient analysis on important features. Moreover, machine learning is yet to be used to predict bDMARD responses in ankylosing spondylitis (AS). Thus, in this study, machine learning was used to predict such responses in RA and AS patients. Methods Data were retrieved from the Korean College of Rheumatology Biologics therapy (KOBIO) registry. The number of RA and AS patients in the training dataset were 625 and 611, respectively. We prepared independent test datasets that did not participate in any process of generating machine learning models. Baseline clinical characteristics were used as input features. Responders were defined as those who met the ACR 20% improvement response criteria (ACR20) and ASAS 20% improvement response criteria (ASAS20) in RA and AS, respectively, at the first follow-up. Multiple machine learning methods, including random forest (RF-method), were used to generate models to predict bDMARD responses, and we compared them with the logistic regression model. Results The RF-method model had superior prediction performance to logistic regression model (accuracy: 0.726 [95% confidence interval (CI): 0.725–0.730] vs. 0.689 [0.606–0.717], area under curve (AUC) of the receiver operating characteristic curve (ROC) 0.638 [0.576–0.658] vs. 0.565 [0.493–0.605], F1 score 0.841 [0.837–0.843] vs. 0.803 [0.732–0.828], AUC of the precision-recall curve 0.808 [0.763–0.829] vs. 0.754 [0.714–0.789]) with independent test datasets in patients with RA. However, machine learning and logistic regression exhibited similar prediction performance in AS patients. Furthermore, the patient self-reporting scales, which are patient global assessment of disease activity (PtGA) in RA and Bath Ankylosing Spondylitis Functional Index (BASFI) in AS, were revealed as the most important features in both diseases. Conclusions RF-method exhibited superior prediction performance for responses of bDMARDs to a conventional statistical method, i.e., logistic regression, in RA patients. In contrast, despite the comparable size of the dataset, machine learning did not outperform in AS patients. The most important features of both diseases, according to feature importance analysis were patient self-reporting scales.

scholarly journals Patient-Specific Phantomless Estimation of Bone Mineral Density and Its Effects on Finite Element Analysis Results: A Feasibility Study

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Young Han Lee ◽  
Jung Jin Kim ◽  
In Gwun Jang
Keyword(s):  
Bone Mineral Density ◽  
Finite Element Analysis ◽  
Finite Element ◽  
Lumbar Spine ◽  
Regression Model ◽  
Proximal Femur ◽  
Strain Energy ◽  
Mineral Density ◽  
Element Analysis ◽  
Reference Phantom

Objectives. This study proposes a regression model for the phantomless Hounsfield units (HU) to bone mineral density (BMD) conversion including patient physical factors and analyzes the accuracy of the estimated BMD values. Methods. The HU values, BMDs, circumferences of the body, and cross-sectional areas of bone were measured from 39 quantitative computed tomography images of L2 vertebrae and hips. Then, the phantomless HU-to-BMD conversion was derived using a multiple linear regression model. For the statistical analysis, the correlation between the estimated BMD values and the reference BMD values was evaluated using Pearson’s correlation test. Voxelwise BMD and finite element analysis (FEA) results were analyzed in terms of root-mean-square error (RMSE) and strain energy density, respectively. Results. The HU values and circumferences were statistically significant (p<0.05) for the lumbar spine, whereas only the HU values were statistically significant (p<0.05) for the proximal femur. The BMD values estimated using the proposed HU-to-BMD conversion were significantly correlated with those measured using the reference phantom: Pearson’s correlation coefficients of 0.998 and 0.984 for the lumbar spine and proximal femur, respectively. The RMSEs of the estimated BMD values for the lumbar spine and hip were 4.26 ± 0.60 (mg/cc) and 8.35 ± 0.57 (mg/cc), respectively. The errors of total strain energy were 1.06% and 0.91%, respectively. Conclusions. The proposed phantomless HU-to-BMD conversion demonstrates the potential of precisely estimating BMD values from CT images without the reference phantom and being utilized as a viable tool for FEA-based quantitative assessment using routine CT images.

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