Stress Prediction Model Research Articles

Prediction method and distribution characteristics of in situ stress based on borehole deformation—A case study of coal measure stratum in Shizhuang block, Qinshui Basin

A geometric equation of borehole deformation under stress was deduced based on the basic theory of elasticity. Subsequently, we established the quantitative relationship between the in situ stress and geometrical parameters of borehole deformation. Furthermore, we proposed an in situ stress prediction model based on borehole deformation. Additionally, numerical simulations of borehole morphology in different lithologies under in situ stress were conducted to analyze the deformation effect. Logging parameters that are sensitive to the shear wave time difference, such as longitudinal wave time difference, density, and natural gamma radiation, were selected for training using an artificial neural network (ANN) to predict the shear wave time difference. The results demonstrated that 1) combining the theoretical derivation and numerical simulation, the borehole geometry under stress was quasi-elliptic, and 2) compared with the existing shear wave time difference curve, the predicted geometry by the ANN was consistent with the actual geometry. Consequently, compared with the tested data from acoustic emission, the overall error of the in situ stress predicted using the new method was less than 9.2%. Moreover, the accuracy of the coal seam was the highest, wherein the average errors of the maximum and minimum horizontal principal stresses were 2.01 and 2.56%, respectively, which confirms the feasibility of the proposed method.

New prediction method of horizontal principal stress of deep tight reservoirs

As the tight reservoir of Lucaogou Formation in Jimsar sag is characterized as large burial depth and poor physical properties, hydraulic fracturing technology is greatly needed to increase the production. Firstly, based on elastic theory, the borehole displacement formula under stress is derived, then the borehole quasi-ellipse characteristic model is proposed, on this basis, the in-situ stress prediction model based on borehole deformation is constructed. Secondly, the force analysis of diamond bit during drilling is performed, and the continuous prediction formula of elastic modulus based on weight on bit (WOB) and torque parameters is derived. Combined with these two steps, a new model of continuous in-situ stress prediction based on borehole deformation and drilling parameters is established. Compared with the values measured by the acoustic emission method in the laboratory, the deviation between the predicted in-situ stress values and those measured by the acoustic emission method is less than 4%, which meets the accuracy requirements of the fracturing in the oil field. The prediction results show that the minimum horizontal principal stress of Lucaogou Formation is 51.1–62.7 MPa, and the maximum horizontal principal stress is 58.9–69.1 MPa, respectively. The maximum horizontal principal stress direction is NW–SE direction with an angle of about 159°. The results show that the fracture direction in the study area is in accordance with the present in-situ stress direction, which is beneficial to the secondary reconstruction of natural fractures and keeps good opening property of fractures. This study provides a theoretical basis for in-situ stress prediction and compressibility evaluation of tight reservoir.

Calculation Model of Mechanical and Sealing Properties of NiTi Alloy Corrugated Gaskets under Shape Memory Effect and Hyperelastic Coupling: I Mechanical Properties.

NiTi alloy’s shape memory effect provides additional restoring force under temperature loads, making it an ideal material for gaskets. However, its yield stress is too large to form the initial seal. In this paper, by combining the advantages of corrugated structure and NiTi alloy’s shape memory effect, a NiTi alloy corrugated gasket is proposed. Its mechanical properties were studied using experiments and the finite element method. The influences of geometric parameters on gasket performance were discussed. The results show that the shape memory effect can greatly improve the contact stress of gaskets. The corrugation can effectively reduce the pre-tightening force. The contact stress of NiTi alloy corrugated gasket is significantly affected by plate thickness, gasket height, and corrugation pitch and shows a high nonlinear characteristic. The proposed finite element method (FEM) and the gasket contact stress prediction model are accurate and engineering available.

Pipeline Stress Test Simulation Under Freeze-Thaw Cycling via the XGBoost-Based Prediction Model

This study conducted ten freeze-thaw cyclic tests to clarify the effect of freeze-thaw cycles on the forces acting on the buried oil pipeline. The stress evolution in the Q345 steel pipeline versus the number of freeze-thaw cycles was obtained. The test results were consistent with the COMSOL simulation of the effect of different moisture contents on the pipeline bottom stress. Besides the proposed XGBoost model, eleven machine-learning stress prediction models were also applied to 10–20 freeze-thaw cycling tests. The results showed that during the freeze-thaw process, the compressive stress at the pipeline bottom did not exceed −69.785 MPa. After eight freeze-thaw cycles, the extreme value of the principal stress of -252.437MPa, i.e., 73.17% of the yield stress, was reached. When the initial moisture content exceeded 20%, the eighth freeze-thaw cycle’s pipeline stress decreased remarkably. The XGBoost model effectively predicted the pipeline’s principal stress in each cycle of 10 freeze-thaw cyclic tests, with R2 = 0.978, MSE = 0.021, and MAE = 0.102. The above compressive stress fluctuated from −131.226 to −224.105 MPa. The predicted values well matched the experimental ones, being in concert with the “ratcheting effect” predicted by the freeze-thaw cycle theory. The results obtained provide references for the design, operation, and maintenance of buried oil pipelines.

Happy work: Improving enterprise human resource management by predicting workers’ stress using deep learning

Recently, workers in most enterprises suffer from excessive occupational stress in the workplace, which negatively affects workers' productivity, safety, and health. To deal with stress in workers, it is vital for the human resource management (HRM) department to manage stress effectively, bridging the gap between management and stressed employees. To manage stress effectively, the first step is to predict workers' stress and detect the factors causing stress among workers. Existing methods often rely on the stress assessment questionnaire, which may not be effective to predict workers' stress, due to 1) the difficulty of collecting the questionnaire data, and 2) the bias brought by workers' subjectivity when completing the questionnaires. In this paper, we aim to address this issue and accurately predict workers' stress status based on Deep Learning (DL) approach. We develop two stress prediction models (i.e., stress classification model and stress regression model) and correspondingly design two neural network architectures. We train these two stress prediction models based on workers' data (e.g., salary, working time, KPI). By conducting experiments over two real-world datasets: ESI and HAJP, we validate that our proposed deep learning-based approach can effectively predict workers' stress status with 71.2% accuracy in the classification model and 11.1 prediction loss in the regression model. By accurately predicting workers' stress status with our method, the HRM of enterprises can be improved.

Medically-oriented design for explainable AI for stress prediction from physiological measurements

BackgroundIn the last decade, a lot of attention has been given to develop artificial intelligence (AI) solutions for mental health using machine learning. To build trust in AI applications, it is crucial for AI systems to provide for practitioners and patients the reasons behind the AI decisions. This is referred to as Explainable AI. While there has been significant progress in developing stress prediction models, little work has been done to develop explainable AI for mental health.MethodsIn this work, we address this gap by designing an explanatory AI report for stress prediction from wearable sensors. Because medical practitioners and patients are likely to be familiar with blood test reports, we modeled the look and feel of the explanatory AI on those of a standard blood test report. The report includes stress prediction and the physiological signals related to stressful episodes. In addition to the new design for explaining AI in mental health, the work includes the following contributions: Methods to automatically generate different components of the report, an approach for evaluating and validating the accuracies of the explanations, and a collection of ground truth of relationships between physiological measurements and stress prediction.ResultsTest results showed that the explanations were consistent with ground truth. The reference intervals for stress versus non-stress were quite distinctive with little variation. In addition to the quantitative evaluations, a qualitative survey, conducted by three expert psychiatrists confirmed the usefulness of the explanation report in understanding the different aspects of the AI system.ConclusionIn this work, we have provided a new design for explainable AI used in stress prediction based on physiological measurements. Based on the report, users and medical practitioners can determine what biological features have the most impact on the prediction of stress in addition to any health-related abnormalities. The effectiveness of the explainable AI report was evaluated using a quantitative and a qualitative assessment. The stress prediction accuracy was shown to be comparable to state-of-the-art. The contributions of each physiological signal to the stress prediction was shown to correlate with ground truth. In addition to these quantitative evaluations, a qualitative survey with psychiatrists confirmed the confidence and effectiveness of the explanation report in the stress made by the AI system. Future work includes the addition of more explanatory features related to other emotional states of the patient, such as sadness, relaxation, anxiousness, or happiness.

Machine Learning Based Depression, Anxiety, and Stress Predictive Model During COVID-19 Crisis

Corona Virus Disease-2019 (COVID-19) was reported at first in Wuhan city, China by December 2019. World Health Organization (WHO) declared COVID-19 as a pandemic i.e., global health crisis on March 11, 2020. The outbreak of COVID-19 pandemic and subsequent lockdowns to curb the spread, not only affected the economic status of a number of countries, but it also resulted in increased levels of Depression, Anxiety, and Stress (DAS) among people. Therefore, there is a need exists to comprehend the relationship among psycho-social factors in a country that is hypothetically affected by high levels of stress and fear; with tremendously-limiting measures of social distancing and lockdown in force; and with high rates of new cases and mortalities. With this motivation, the current study aims at investigating the DAS levels among college students during COVID-19 lockdown since they are identified as a highly-susceptible population. The current study proposes to develop Intelligent Feature Subset Selection with Machine Learning-based DAS predictive (IFSSML-DAS) model. The presented IFSSML-DAS model involves data preprocessing, Feature Subset Selection (FSS), classification, and parameter tuning. Besides, IFSSML-DAS model uses Group Gray Wolf Optimization based FSS (GGWO-FSS) technique to reduce the curse of dimensionality. In addition, Beetle Swarm Optimization based Least Square Support Vector Machine (BSO-LSSVM) model is also employed for classification in which the weight and bias parameters of the LSSVM model are optimally adjusted using BSO algorithm. The performance of the proposed IFSSML-DAS model was tested using a benchmark DASS-21 dataset and the results were investigated under different measures. The outcome of the study suggests the development of specialized programs to handle DAS among population so as to overcome COVID-19 crisis.

An Expressway Driving Stress Prediction Model Based on Vehicle, Road and Environment Features

Driving stress is the demand for reserved cognitive space after a driver perceives changes in vehicle, road and environmental factors during driving, which has been proven to affect driving behaviour, interfering with driving safety. Traditional stress prediction relies extensively on psychological data and is limited by the unpopularity of psychological data collection technology, which cannot be applied in daily life on a large scale. In recent years, advances in high-precision visual analysis technology represented by deep learning have laid the foundation for automated and large-scale visual environment analysis. This study proposes a framework for the quantitative analysis of highway driving stress based on multiple vehicle, road, and environmental factors. A dilated residual network model and other methods were used to extract visual environmental indexes. Combined with multisource data such as traffic volume and road design parameters, the LightGBM method was used to construct an expressway driving stress prediction model with high accuracy. The MAE, RMSE and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> values of the proposed model are 0.042, 0.004 and 0.881, respectively, demonstrating the usefulness for scaled and efficient assessment of expressway stress loads. The SHAP method was used to explore the relationship between different influencing factors and driving stress to quantify the mechanism of vehicle, road and environment influences on stress load, and to propose recommendations for highway design and planning from the perspective of reducing stress load. This study provides a new way of thinking to quantitatively investigate the link between multiple road traffic factors and driving stress, providing efficient and large-scale assessment of expressway driving stress, as well as proposing some suggestions for highway design and planning to enhance stress reduction.

Fatigue Limit Prediction Models of 6060 Aluminium Extruded Alloy

The fatigue limit stress prediction models make it possible to determine the durability of metals, considering experimental fatigue tests, mechanical properties, hardness, manufacturing technology (namely through intrinsic defects in the process), and empirical constants depending on the manufacturing processes and components’ dimension. The manufacturing defects in the materials lead to failures at much lower applied stresses. Therefore, considering such defect features as input parameters in fatigue limit assessment is of primary importance. This work aims to develop fatigue life prediction models for extruded 6060 aluminium. The methodology used in this study is based on experimental work. The materials used were obtained from three extruded pieces of 6060 aluminium with different geometries (a rectangular section with smooth faces and recess faces and a solid rectangular section) and different thermal treatments (T1 and T4). In addition, the manufacturing defects of the stress concentration zones were studied. The algorithm to predict durability is based on the experimental results obtained. The Murakami, Ueno and Schӧnbauer models were studied. In conclusion, new models to predict fatigue limits were developed for the 6060-aluminium extruded alloy, considering different geometries and heat treatment.

Person and Stressor Independent Generic Model for Stress Detection Using GSR.

Stress detection is a widely researched topic and is important for overall well-being of an individual. Several approaches are used for prediction/classification of stress. Most of these approaches perform well for subject and activity specific scenarios as stress is highly subjective. So, it is difficult to create a generic model for stress prediction. Here, we have proposed an approach for creating a generic stress prediction model by utilizing knowledge from three different datasets. Proposed model has been validated using two open datasets as well as on a set of data collected in our lab. Results show that the proposed generic model performs well across studies conducted independently and hence can be used for monitoring stress in real life scenarios and to create mass-market stress prediction products.

Research on Prediction Algorithm of Employees’ Psychological Stress Based on Multifeature Fusion

A multifeature fusion-based enterprise employee psychological stress prediction algorithm is suggested to address the concerns of low prediction accuracy, long duration, and poor results in current psychological stress prediction approaches. Examine ECG signal generation and properties, as well as the notion and causes of heart rate variability. The ECG signal is gathered according to the psychological stress reaction mechanism, and the digital filter is utilized to filter and preprocess the noise interference of the ECG signal. The linear discriminant analysis algorithm extracts the time domain linear features, frequency domain linear features, and nonlinear features of the ECG signal and then selects the ECG signal characteristics. D-S evidence theory is used to fuse the time domain linear characteristics, frequency domain linear characteristics, and nonlinear characteristics of the ECG signal, construct the psychological stress prediction model, obtain the final result of psychological stress state prediction, and realize the psychological stress prediction of enterprise employees, all based on multifeature fusion technology. The results of the experiments reveal that the suggested algorithm has a greater predictive effect on employee psychological stress, allowing it to enhance forecast accuracy and reduce prediction time.

Stress analysis of marine gears considering 3D mixed lubrication-contact state

Marine transmission gears usually have large size, and often experience severe working conditions, such as heavy load, large transmission power and high speed ratio, together with the effect of surface asperities, which cause coexisting lubrication-contact state and seriously stress concentration, and further may lead to micro-pitting and fatigue failure. In this paper, the subsurface stress prediction model of the timing transmission spur gears of marine engine is developed, together with considering real 3D machined surface roughness and mixed lubrication-contact state. Results show, when the contact bodies with perfect elastic behavior,the maximum von Mises stress appears on the surface and no longer shows regular distributions along the line of action because of the 3D roughness.The transverse turned surface can reduce the subsurface stress greatly, the shaved and ground surfaces cause large subsurface stress and high-pressure area on the surface. The influence of surface roughness on the stress state is increased under the special conditions of low velocity and high power density, and the contact state is greatly improved by optimizing the parameters of tooth width. Effects of different materials indicate nylon-nylon contact surfaces has the smallest maximum stress compared with that in other four pairs. However, nylon-nylon contact surfaces have the highest friction coefficients, which can easily lead to severe wear or scuffing failures.

Experimental Analysis and Prediction Model of Milling-Induced Residual Stress of Aeronautical Aluminum Alloys

Aeronautical thin-walled frame workpieces are usually obtained by milling aluminum alloy plates. The residual stress within the workpiece has a significant influence on the deformation due to the relatively low rigidity of the workpiece. To accurately predict the milling-induced residual stress, this paper describes an orthogonal experiment for milling 7075 aluminum alloy plates. The milling-induced residual stress at different surface depths of the workpiece, without initial stress, is obtained. The influence of the milling parameters on the residual stress is revealed. The parameters include milling speed, feed per tooth, milling width, and cutting depth. The experimental results show that the residual stress depth in the workpiece surface is within 0.12 mm, and the residual stress depth of the end milling is slightly greater than that of the side milling. The calculation models of residual stress and milling parameters for two milling methods are formulated based on regression analysis, and the sensitivity coefficients of parameters to residual stress are calculated. The residual stress prediction model for milling 7075 aluminum alloy plates is proposed based on a back-propagation neural network and genetic algorithm. The findings suggest that the proposed model has a high accuracy, and the prediction error is between 0–14 MPa. It provides basic data for machining deformation prediction of aluminum alloy thin-walled workpieces, which has significant application potential.

Comparing stress prediction models using smartwatch physiological signals and participant self-reports

Recent advances in wearable technology have facilitated the non-obtrusive monitoring of physiological signals, creating opportunities to monitor and predict stress. Researchers have utilized machine learning methods using these physiological signals to develop stress prediction models. Many of these prediction models have utilized objective stressor tasks (e.g., a public speaking task or solving math problems). Alternatively, the subjective user responses with self-reports have also been used for measuring stress. In this paper, we describe a methodological approach (a) to compare the prediction performance of models developed using objective markers of stress using participant-reported subjective markers of stress from self-reports; and (b) to develop personalized stress models by accounting for inter-individual differences. Towards this end, we conducted a laboratory-based study with 32 healthy volunteers. Participants completed a series of stressor tasks—social, cognitive and physical—wearing an instrumented commercial smartwatch that collected physiological signals and participant responses using timed self-reports. After extensive data preprocessing using a combination of signal processing techniques, we developed two types of models: objective stress models using the stressor tasks as labels; and subjective stress models using participant responses to each task as the label for that stress task. We trained and tested several machine learning algorithms—support vector machine (SVM), random forest (RF), gradient boosted trees (GBT), AdaBoost, and Logistic Regression (LR)—and evaluated their performance. SVM had the best performance for the models using the objective stressor (i.e., stressor tasks) with an AUROC of 0.790 and an F-1 score of 0.623. SVM also had the highest performance for the models using the subjective stress (i.e., participant self-reports) with an AUROC of 0.719 and an F-1 score of 0.520. Model performance improved with a personalized threshold model to an AUROC of 0.751 and an F-1 score of 0.599. The performance of the stress models using an instrumented commercial smartwatch was comparable to similar models from other state-of-the-art laboratory-based studies. However, the subjective stress models had a lower performance, indicating the need for further research on the use of self-reports for stress-related studies. The improvement in performance with the personalized threshold-based models provide new directions for building stress prediction models.

Study on formation mechanism and regularity of residual stress in ultrasonic vibration grinding of high strength alloy steel

The surface residual stress of the part has a crucial influence on its service performance. This paper studies the formation mechanism of residual stress in Ultrasonic Vibration Grinding (UVG) of high strength alloy steel, and obtains the correlation law between processing parameters and residual stress. First, based on the kinematics analysis of UVG, the formulas of ultrasonic grinding force and ultrasonic grinding transient moving heat source temperature field are analyzed. A thermal-mechanical coupled ultrasonic vibration grinding residual stress prediction model was established by FEM and verified by experiments. Second, the thermal behavior of the workpiece surface during UVG is analyzed, and the formation mechanism of the residual stress on the workpiece surface is explained. Finally, combining simulation and experimental results, a comprehensive correlation law between multiple grinding process parameters and residual stress is obtained. The research results show that compared with common grinding (CG), UVG can reduce the grinding temperature by about 8%. Both UVG and CG will reduce the original compressive stress on the surface, but UVG can make the surface of the workpiece retain greater compressive residual stress and prevent the emergence of tensile residual stress. This work of the thesis provides a basis for the research of residual stress in UVG.

Experimental and numerical investigations of hot-rolled stainless steel channel section columns susceptible to flexural buckling

A thorough testing and numerical modelling programme has been conducted to investigate the minor-axis flexural buckling behaviour and resistances of pin-ended hot-rolled stainless steel channel section columns. The testing programme included material coupon tests, membrane residual stress measurements, initial global and local geometric imperfection measurements and ten pin-ended channel section column tests about the minor principal axis. A membrane residual stress predictive model was developed based on the measured data. The testing programme was followed by a numerical modelling programme; finite element models were developed to simulate the test results and then adopted to perform parametric studies to generate further numerical data on hot-rolled stainless steel channel section columns over a wide range of cross-section dimensions and member effective lengths. The obtained test and numerical data were then adopted to assess the accuracy of relevant design rules for hot-rolled stainless steel channel section columns failing by flexural buckling about the minor principal axis, as given in the European code and American design guide. The assessment results revealed that (i) the European code yields many unsafe flexural buckling resistance predictions and (ii) the American design guide yields overly conservative and scattered flexural buckling resistance predictions. Finally, a revised Eurocode buckling curve was proposed and shown to offer accurate, consistent and safe-sided flexural buckling resistance predictions for hot-rolled stainless steel channel section columns over the whole considered range of member non-dimensional slendernesses. The reliability of the revised Eurocode buckling curve was also confirmed using statistical analyses based on EN 1990.

Prediction of Welding Deformation and Residual Stress of a Thin Plate by Improved Support Vector Regression.

Thin plates are widely utilized in aircraft, shipbuilding, and automotive industries to meet the requirements of lightweight components. Especially in modern shipbuilding, the thin plate structures not only meet the economic requirements of shipbuilding but also meet the strength and rigidity requirements of the ship. However, a thin plate is less stable and prone to destabilizing deformation in the welding process, which seriously affects the accuracy and performance of the thin plate welding structure. Therefore, it is beneficial to predict welding deformation and residual stress before welding. In this paper, a thin plate welding deformation and residual stress prediction model based on particle swarm optimization (PSO) and grid search(GS) improved support vector regression (PSO-GS-SVR) is established. The welding speed, welding current, welding voltage, and plate thickness are taken as input parameters of the improved support vector regression model, while longitudinal and transverse deformation and residual stress are taken as corresponding outputs. To improve the prediction accuracy of the support vector regression model, particle swarm optimization and grid search are used to optimize the parameters. The results show that the improved support regression model can accurately evaluate the deformation and residual stress of butt welding and has important engineering guiding significance.

Contact stress prediction model of double‐layer elastomer with functionally graded and composite structure

AbstractAs hard and brittle materials are widely used in the modern industry, it is essential to effectively achieve uniform material removal. In the present study, a contact stress prediction model for double‐layer elastomer under uniform displacement load was proposed. It aims to predict the contact stress distribution based on the structure size and material parameters of the double‐layer elastomer and optimize the relevant parameters to improve the uniformity of material removal. The functionally graded and composite structured lapping and polishing plate (FG/CS‐LPP) was developed by mixing abrasive particles and rubber with different mass ratios to get different Young's moduli. Through optical microscope, the surface morphology was observed, and the mechanism of Young's modulus gradient change was demonstrated. After numerical simulation analysis, the effect of Young's modulus fluctuation on contact stress distribution trend was evaluated. As for the contact stress prediction model, an equivalent Young's modulus model and compensation function H(z) was introduced to simplify the analytical process. Further, the obtained equivalent equation was verified by simulations and experiments with an average percentage deviation of ~2% and ~6%, respectively. By fitting calculated and simulated values with a deviation of less than 5%, the compensation function H(z) was established. Finally, the contact stress prediction model was obtained and validated by simulations and experiments, the percentage deviation is less than 13%. Accordingly, the contact stress prediction model can provide a theoretical basis for FG/CS‐LPP to achieve uniform material removal.

Membrane residual stresses and local buckling of S960 ultra-high strength steel welded I-section stub columns

The membrane residual stresses and local buckling behaviour of S960 ultra-high strength steel welded I-section stub columns have been investigated through a comprehensive testing and numerical modelling programme. The testing programme included material testing, membrane residual stress measurements, initial local geometric imperfection measurements and sixteen stub column tests. On the basis of the measured data, a membrane residual stress predictive model was developed. Following the testing programme, a numerical modelling programme was conducted, where finite element models were firstly developed and validated against the test results and then adopted to conduct parametric studies to generate further numerical data. The obtained test and numerical data were used to assess the applicability of the relevant design provisions for S700 (or S690) high strength steel welded I-section stub columns, as given in the European code, American specification and Australian standard, to their S960 ultra-high strength steel counterparts. The assessment results generally revealed that (i) all three sets of codified slenderness limits can be accurately and consistently used for cross-section classification of S960 ultra-high strength steel welded I-section stub columns and (ii) the local buckling design provisions in the three design codes also lead to accurate and consistent cross-section compression resistance predictions.

Contact Stress Prediction Model for Variable Hyperbolic Circular Arc Gear Based on the Optimized Kriging-Response Surface Model

Contact Stress Prediction Model for Variable Hyperbolic Circular Arc Gear Based on the Optimized Kriging-Response Surface Model