Force Estimation Research Articles

In-plane Rotation Response of Skewed Simply Supported Bridges Considering Multi-point Pounding Effect

Previous earthquake events have shown that the in-plane rotation of skewed bridges can induce multi-point pounding at longitudinal expansion joints and transversely between the decks and shear keys. These pounding phenomena may result in damage to girders and excessive deck movement. To explore the interaction between multi-point pounding and in-plane rotation response of decks in multi-span skewed simply supported bridges under seismic excitation, simplified equations for estimating bi-directional pounding forces were derived based on the force equilibrium principle. Finite-element models of skewed simply supported bridges incorporating pounding effects were developed, and time-history analyses were conducted to investigate the influence of skew angle, expansion joint width, and shear key capacity on the seismic response of bridges. The analytical results demonstrate the accuracy of the simplified pounding force estimation equations. The interaction mechanism indicates a positive feedback loop between multi-point pounding and in-plane rotation response, in which the two phenomena mutually amplify the responses. Parametric analysis reveals that the maximum seismic response can occur at a skew angle of 30°, an expansion joint width of 10cm, or a shear key capacity exceeding 20%. With increasing expansion joint width and shear key capacity, the multi-point pounding effect and in-plane rotation response can be mitigated. However, an excessive expansion width may induce greater deck displacements, and a higher shear key capacity can lead to damage in the piers.

An intelligent tire force estimation correction method based on wheel spoke strain

The previous research shows that by installing strain sensors in the strain-sensitive areas of the wheel hub, the strain characteristics of the wheel hub can be measured under different operating conditions. Based on the changing patterns of strain characteristics, a basic method for estimating tire forces can be established. However, when the operating conditions change, the accuracy of the tire force estimation obtained from the basic method is low. This study analyses the strain characteristics of the wheel hub under the variations of factors such as tire pressure, vehicle speed and radial load, and finds that tire pressure is an important correction factor for estimating radial forces, while radial load is an important correction factor for estimating longitudinal and lateral forces. An intelligent correction method for estimating tire forces based on wheel hub strain is proposed according to the changing patterns between key correction factors and wheel hub strain characteristics, and it is validated by simulation experiments. The estimation correction method proposed in this paper comprehensively considers the effects of multiple factors on wheel hub strain based on previous research. Therefore, the constructed tire force estimation model can be applied to a wide range of driving conditions with high estimation accuracy. In addition, this paper conducts research on wheel hub strain instead of tire strain, which takes a new approach to tire force estimation and provides valuable references and insights for the development of intelligent tires in the future.

Robust joint estimation of sideslip angle and tire lateral force for distributed drive electric vehicle

Accurate estimation of vehicle sideslip angle (SA) and tire lateral force (TLF) is essential for the effective functioning of vehicle active safety control systems. However, effective estimation of SA and TLF using low-cost on-board sensors is a major challenge. In this paper, a robust joint estimation method of SA and TLF for distributed drive electric vehicle (DDEV) is proposed. Adaptive sliding-mode observer (ASMO) is used to estimate the TLF and is used as the input to the subsequent filter. Maximum correntropy criterion (MCC) is introduced to improve the robustness of the unscented Kalman filter (UKF) under mixed Gaussian noise, and the maximum correntropy unscented Kalman filter (MCUKF) algorithm is composed to estimate SA. The effectiveness of the proposed ASMO and MCUKF joint estimation method is verified by Simulink/Carsim joint simulation experiments. The experimental results show that compared with the existing methods, the estimation method proposed in this paper effectively improves the estimation accuracy and robustness of SA and TLF under the mixed Gaussian environment, which is of great significance for the improvement of vehicle active safety.

Improved Surface Electromyogram-Based Hand–Wrist Force Estimation Using Deep Neural Networks and Cross-Joint Transfer Learning

Deep neural networks (DNNs) and transfer learning (TL) have been used to improve surface electromyogram (sEMG)-based force estimation. However, prior studies focused mostly on applying TL within one joint, which limits dataset size and diversity. Herein, we investigated cross-joint TL between two upper-limb joints with four DNN architectures using sliding windows. We used two feedforward and two recurrent DNN models with feature engineering and feature learning, respectively. We found that the dependencies between sEMG and force are short-term (<400 ms) and that sliding windows are sufficient to capture them, suggesting that more complicated recurrent structures may not be necessary. Also, using DNN architectures reduced the required sliding window length. A model pre-trained on elbow data was fine-tuned on hand–wrist data, improving force estimation accuracy and reducing the required training data amount. A convolutional neural network with a 391 ms sliding window fine-tuned using 20 s of training data had an error of 6.03 ± 0.49% maximum voluntary torque, which is statistically lower than both our multilayer perceptron model with TL and a linear regression model using 40 s of training data. The success of TL between two distinct joints could help enrich the data available for future deep learning-related studies.

Estimation of joint kinetics during manual material handling using inertial motion capture: a follow-up study.

Musculoskeletal models based on inertial motion capture and ground reaction force (GRF) prediction hold great potential for field-based risk assessment of manual material handling. However, previous evaluations have identified inaccuracies in the methodology?s estimation of spinal forces, while the accuracy of other key outcome variables is currently unclear. This study evaluated knee, shoulder, and L5-S1 joint reaction forces (JRFs) derived from a musculoskeletal model based on inertial motion capture and GRF prediction against a model based on simultaneously collected optical motion capture and force plate measurements. Data from 19 healthy subjects performing lifts with various horizontal locations, deposit heights and asymmetry angles were analyzed, and the consistency and absolute agreement of the model estimates statistically compared. Despite varying levels of agreement across tasks and variables, considerable absolute differences were identified for the L5-S1 axial compression (RMSE = 63.0-94.2%BW) and anteroposterior shear forces (RMSE = 40.9-80.6%BW) as well as the bilateral knee JRFs (RMSE = 78.9-117%BW). Glenohumeral JRFs and vertical GRFs exhibited the highest overall consistency (r = 0.33-0.91, median 0.78) and absolute agreement (RMSE = 7.63-34.9%BW), while the L5-S1 axial compression forces also showed decent consistency (r = 0.04-0.89, median 0.80). The findings generally align with prior evaluations, indicating persistent challenges with the accuracy of key outcome variables. While the modelling framework shows promise, further development of the methodology is encouraged to enhance its applicability in ergonomic evaluations.

X-DoF: Automatic Degree of Freedom Subset Selection for Inverse Blocked Force Characterization

Abstract Selecting the proper set of Degrees of Freedom is essential in inverse (Blocked) Force calculation. Including too many Degrees of Freedom in the computation can lead to overfitting, resulting in inaccurate force estimations and poor prediction quality. The discrepancy arises from errors within the dataset, such as measurement noise or other artifacts. This paper presents a solution to the overfitting problem, introducing the X-DoF procedure to automatically identify the relevant subset of Blocked Force Degrees of Freedom. Its effectiveness is showcased through numerical and experimental validation and compared against regularization techniques.

Expansion force signal based rapid detection of early thermal runaway for pouch batteries

With the extensive application of lithium-ion batteries in electric vehicles and energy storage stations, thermal safety issues have increasingly become the focus of public attention. To alleviate the safety concerns, a qualified battery management system (BMS) must be able to detect and warn of thermal abuse before it develops into a thermal runaway (TR). For the first time, the expansion force characteristics of the pouch battery module during the early heating stage of TR under typical working conditions are analyzed, which proves that the expansion force signal can reflect the internal abnormal heating earlier than the terminal voltage and the surface temperature, and has better noise resistance. Furthermore, the electromechanical coupling model (EmCM) is improved for more accurate open-loop expansion force estimation. On this basis, an expansion force based early TR rapid detection framework is proposed with less 3 min expend in tests, in which the Kalman filter ensures the stability and convergence/divergence of the observer estimation error, and the reliability of fault judgment is ensured by the adaptive threshold designed by the model structure.

Oscillometric blood pressure measurements on smartphones using vibrometric force estimation

This paper proposes a smartphone-based method for measuring Blood Pressure (BP) using the oscillometric method. For oscillometry, it is necessary to measure (1) the pressure applied to the artery and (2) the local blood volume change. This is accomplished by performing an oscillometric measurement at the finger’s digital artery, whereby a user presses down on the phone’s camera with steadily increasing force. The camera is used to capture the blood volume change using photoplethysmography. We devised a novel method for measuring the force applied of the finger without the use of specialized smartphone hardware with a technique called Vibrometric Force Estimation (VFE). The fundamental concept of VFE relies on a phenomenon where a vibrating object is dampened when an external force is applied on to it. This phenomenon can be recreated using the phone’s own vibration motor and measured using the phone’s Inertial Measurement Unit (IMU). A cross device reliability study with three smartphones of different manufacturers, shape, and prices results in similar force estimation performance across all smartphone models. In an N = 24 proof of concept study of the BP measurement, the smartphone technique achieves a mean absolute error of 9.21 mmHg and 7.77 mmHg of systolic and diastolic BP, respectively, compared to an FDA approved BP cuff. The vision for this technology is not necessarily to replace existing BP monitoring solutions, but rather to introduce a downloadable smartphone software application that could serve as a low-barrier hypertension screening measurement fit for widespread adoption.

Compliant Grasp Control Method for the Underactuated Prosthetic Hand Based on the Estimation of Grasping Force and Muscle Stiffness with sEMG

Human muscles can generate force and stiffness during contraction. When in contact with objects, human hands can achieve compliant grasping by adjusting the grasping force and the muscle stiffness based on the object’s characteristics. To realize humanoid-compliant grasping, most prosthetic hands obtain the stiffness parameter of the compliant controller according to the environmental stiffness, which may be inconsistent with the amputee’s intention. To address this issue, this paper proposes a compliant grasp control method for an underactuated prosthetic hand that can directly obtain the control signals for compliant grasping from surface electromyography (sEMG) signals. First, an estimation method of the grasping force is established based on the Huxley muscle model. Then, muscle stiffness is estimated based on the muscle contraction principle. Subsequently, a relationship between the muscle stiffness of the human hand and the stiffness parameters of the prosthetic hand controller is established based on fuzzy logic to realize compliant grasp control for the underactuated prosthetic hand. Experimental results indicate that the prosthetic hand can adjust the desired force and stiffness parameters of the impedance controller based on sEMG, achieving a quick and stable grasp as well as a slow and gentle grasp on different objects.

Development of Low-Resistance Coastal Stow Net Using Numerical Analysis and Model Experiments

In coastal stow net fishing, the heavy weight of a typical anchor (750–1000 kg) can increase the risk of capsizing the boat and crew member injury during hoisting operations. Thus, to prevent these accidents, a reduction in the anchor weight is required. One strategy to achieve this is to reduce the resistance force of the fishing gear used, which would allow lighter anchors to be employed. This requires the accurate estimation of the resistance force for various gear designs. Therefore, the resistance force and shape during the operation of two representative types of coastal stow nets currently employed in the Korean coastal stow net fishing industry were investigated using simulations and modeling experiments. The modeled fishing gear was divided into four sections according to the mesh size. Based on the results, the twine thickness was reduced in order to target areas of the gear where the greatest resistance was observed, while the front part of the gear was redesigned to prevent the front of the net from being pushed back into a suboptimal shape. The proposed low-resistance fishing gear has the potential to improve occupational safety in the coastal stow net fishing industry.

Force control for the robot-assisted laparoscopic ultrasound scanning system

Laparoscopic ultrasound scanning, an acritical diagnostic imaging method for liver diseases, necessitates precise contact force control that is typically dependent on skilled physicians. Given its high mobility and controllability, the robot is anticipated to supplant physicians in performing laparoscopic ultrasound scans. Consequently, this study introduces a laparoscopic ultrasound scanning system paired with a learning-based force controller. Compared with traditional methods, our approach offers precise control over the contact force between the ultrasound probe and human organ surfaces during scanning, reducing the learning curve for surgeons in laparoscopic liver ultrasound scanning. Acknowledging the complex nonlinear mechanical model involved when the laparoscopic ultrasound probe contacts human organs and tissues, we propose a learning-based method utilizing a long short-term memory network to estimate the contact force. This method allows for the precise estimation of contact force with human organ tissues by analyzing tendon force. The results indicate that the proposed LSTM neural network can accurately fit the mechanical model of the robotic catheter, with a root mean square error of 3.44 mN. Finally, we conducted a force control experiment for ultrasound scanning on a silicone liver model. The results indicate that the proposed method can achieve relatively stable force control for laparoscopic ultrasound scanning, with a maximum error of 2.41 mN during the transient phase of system control and a maximum absolute error of 1.8 mN in the steady state.

Prediction of feed force with machine learning algorithms in boring of AISI P20 plastic mold steel

Feed force plays a critical role in machining performance, including efficiency, hole quality, energy consumption, and tool life. While feed force is typically measured using dynamometers, these devices are often impractical in real-world experiments due to their complexity and cost. In particular, boring operations, which are essential for enlarging holes within tight tolerances, depend on precise feed force control to maintain high-quality hole outcomes. Achieving this precision requires reliable pre-process feed force estimation. However, traditional analytical methods for calculating feed force in boring operations often fall short, largely due to factors such as in-hole dynamics, the inclination of the rake surface, and the slenderness of the boring bar. This study aims to apply single and hybrid machine learning methods to accurately model and predict feed force in the boring process. Unlike previous research, which focused on feature selection, we propose the use of all possible combinations of input feature subsets to determine the optimal input features. The results indicate that feed force can be predicted effectively using these optimized feature combinations. Such accurate feed force prediction is crucial for effective process optimization, material selection, and process planning.

Enhanced Drag Force Estimation in Automotive Design: A Surrogate Model Leveraging Limited Full-Order Model Drag Data and Comprehensive Physical Field Integration

In this paper, a novel surrogate model for shape-parametrized vehicle drag force prediction is proposed. It is assumed that only a limited dataset of high-fidelity CFD results is available, typically less than ten high-fidelity CFD solutions for different shape samples. The idea is to take advantage not only of the drag coefficients but also physical fields such as velocity, pressure, and kinetic energy evaluated on a cutting plane in the wake of the vehicle and perpendicular to the road. This additional “augmented” information provides a more accurate and robust prediction of the drag force compared to a standard surface response methodology. As a first step, an original reparametrization of the shape based on combination coefficients of shape principal components is proposed, leading to a low-dimensional representation of the shape space. The second step consists in determining principal components of the x-direction momentum flux through a cutting plane behind the car. The final step is to find the mapping between the reduced shape description and the momentum flux formula to achieve an accurate drag estimation. The resulting surrogate model is a space-parameter separated representation with shape principal component coefficients and spatial modes dedicated to drag-force evaluation. The algorithm can deal with shapes of variable mesh by using an optimal transport procedure that interpolates the fields on a shared reference mesh. The Machine Learning algorithm is challenged on a car concept with a three-dimensional shape design space. With only two well-chosen samples, the numerical algorithm is able to return a drag surrogate model with reasonable uniform error over the validation dataset. An incremental learning approach involving additional high-fidelity computations is also proposed. The leading algorithm is shown to improve the model accuracy. The study also shows the sensitivity of the results with respect to the initial experimental design. As feedback, we discuss and suggest what appear to be the correct choices of experimental designs for the best results.

Estimation of the muscle force by perineural intramuscular electrical stimulation in healthy volunteers.

The present study aimed to evaluate the elbow flexor force induced by perineural intramuscular stimulation compared with surface electrical stimulation (ES) and maximal voluntary contraction. Thirty nondominant arms of healthy volunteers were evaluated. Isometric elbow flexion force was evaluated using a surface electrode stimulation at the biceps brachii muscle, a perineural intramuscular stimulation around the musculocutaneous nerve, and maximum voluntary contraction. The elbow flexion force was measured at the wrist volar area in a 90° elbow flexion posture, fixed with a rigid elbow orthosis. Pain and discomfort associated with ES were evaluated using a numeric rating scale. The mean maximum elbow flexion force was 16.6 ± 4.1 kgf via voluntary contraction. The mean elbow flexion force by ES was 2.9 ± 2.0 kgf, stimulation intensity was 24.8 ± 5.5 mA, and the numeric rating scale was 5.0 ± 2.5 via surface electrode stimulation and 3.1 ± 2.0 kgf, 5.0 mA, and 3.8 ± 1.9 via perineural stimulation, respectively. ES provides 16% to 18% of the maximal voluntary contraction force in elbow flexion, which corresponds to a fair grade of muscle force. Perineural intramuscular stimulation can generate an equivocal contraction force with less discomfort in elbow flexion than surface electrode stimulation.

Design and Modelling of Continuum Robot for Endoscopic Submucosal Dissection Surgery With Lifting Force Estimation.

Endoscopic submucosal dissection (ESD) is an effective treatment for early-stage gastrointestinal cancers. However, traditional surgical instruments lack accuracy and force-sensing. A new type of continuum robot for ESD is designed. An accurate static model of the proposed continuum robot is established, considering cases where the robot bends into C-shapes and S-shapes. A force estimation method based on an accurate static model is proposed. Then, the accuracy of the static model and force estimation is verified through experiments. Finally, an ex-organ experiment is carried out. The average position error of the proposed static model is 0.72mm, accounting for 2.57% of the total robot length. The average error of force estimation is 19.53mN. By gripping and cutting ex-porcine gastric mucosa, the robot's functionality is validated. This paper contributes to precise control and safe interaction of continuum robots.

Cavum Septum Pellucidum in Former American Football Players: Findings From the DIAGNOSE CTE Research Project.

Exposure to repetitive head impacts (RHI) is linked to the development of chronic traumatic encephalopathy (CTE), which can only be diagnosed at post-mortem. The presence of a cavum septum pellucidum (CSP) is a common finding in post-mortem studies of confirmed CTE and in neuroimaging studies of individuals exposed to RHI. This study examines CSP in living former American football players, investigating its association with RHI exposure, traumatic encephalopathy syndrome (TES) diagnosis, and provisional levels of certainty for CTE pathology. Data from the DIAGNOSE CTE Research Project were used to compare the presence and ratio of CSP in former American football players (n = 175), consisting of former college (n = 58) and former professional players (n = 117), and asymptomatic unexposed controls without RHI exposure (n = 55). We further evaluated potential associations between CSP measures and cumulative head impact index (CHII) measures (frequency, linear acceleration, and rotational force), a TES diagnosis (yes/no), and a provisional level of certainty for CTE pathology (suggestive, possible, and probable). Former American football players exhibited a higher CSP presence and ratio than unexposed asymptomatic controls. Among player subgroups, professional players showed a greater CSP ratio than former college players and unexposed asymptomatic controls. Among all football players, CHII rotational forces correlated with an increased CSP ratio. No significant associations were found between CSP measures and diagnosis of TES or provisional levels of certainty for CTE pathology. This study confirms previous findings, highlighting a greater prevalence of CSP and a greater CSP ratio in former American football players compared with unexposed asymptomatic controls. In addition, former professional players showed a greater CSP ratio than college players. Moreover, the relationship between estimates of CHII rotational forces and CSP measures suggests that cumulative frequency and strength of rotational forces experienced in football are associated with CSP. However, CSP does not directly correlate with TES diagnosis or provisional levels of certainty for CTE, indicating that it may be a consequence of RHI associated with rotational forces. Further research, especially longitudinal studies, is needed for confirmation and to explore changes over time.

Accurate drift-invariant single-molecule force calibration using the Hadamard variance

Single-molecule force spectroscopy (SMFS) techniques play a pivotal role in unraveling the mechanics and conformational transitions of biological macromolecules under external forces. Among these techniques, multiplexed magnetic tweezers (MT) are particularly well suited to probe very small forces, ≤1 pN, critical for studying noncovalent interactions and regulatory conformational changes at the single-molecule level. However, to apply and measure such small forces, a reliable and accurate force-calibration procedure is crucial. Here, we introduce a new approach to calibrate MT based on thermal motion using the Hadamard variance (HV). To test our method, we perform bead-tether Brownian dynamics simulations that mimic our experimental system and compare the performance of the HV method against two established techniques: power spectral density (PSD) and Allan variance (AV) analyses. Our analysis includes an assessment of each method’s ability to mitigate common sources of additive noise, such as white and pink noise, as well as drift, which often complicate experimental data analysis. We find that the HV method exhibits overall similar or higher precision and accuracy, yielding lower force estimation errors across a wide range of signal/noise ratios (SNRs) and drift speeds compared with the PSD and AV methods. Notably, the HV method remains robust against drift, maintaining consistent uncertainty levels across the entire studied SNR and drift speed spectrum. We also explore the HV method using experimental MT data, where we find overall smaller force estimation errors compared with PSD and AV approaches. Overall, the HV method offers a robust method for achieving sub-pN resolution and precision in multiplexed MT measurements. Its potential extends to other SMFS techniques, presenting exciting opportunities for advancing our understanding of mechanosensitivity and force generation in biological systems. To make our methods widely accessible to the research community, we provide a well-documented Python implementation of the HV method as an extension to the Tweezepy package.

Handrim Reaction Force and Moment Assessment Using a Minimal IMU Configuration and Non-Linear Modeling Approach during Manual Wheelchair Propulsion.

Manual wheelchair propulsion represents a repetitive and constraining task, which leads mainly to the development of joint injury in spinal cord-injured people. One of the main reasons is the load sustained by the shoulder joint during the propulsion cycle. Moreover, the load at the shoulder joint is highly correlated with the force and moment acting at the handrim level. The main objective of this study is related to the estimation of handrim reactions forces and moments during wheelchair propulsion using only a single inertial measurement unit per hand. Two approaches are proposed here: Firstly, a method of identification of a non-linear transfer function based on the Hammerstein-Wiener (HW) modeling approach was used. The latter represents a typical multi-input single output in a system engineering modeling approach. Secondly, a specific variant of recurrent neural network called BiLSTM is proposed to predict the time-series data of force and moments at the handrim level. Eleven subjects participated in this study in a linear propulsion protocol, while the forces and moments were measured by a dynamic platform. The two input signals were the linear acceleration as well the angular velocity of the wrist joint. The horizontal, vertical and sagittal moments were estimated by the two approaches. The mean average error (MAE) shows a value of 6.10 N and 4.30 N for the horizontal force for BiLSTM and HW, respectively. The results for the vertical direction show a MAE of 5.91 N and 7.59 N for BiLSTM and HW, respectively. Finally, the MAE for the sagittal moment varies from 0.96 Nm (BiLSTM) to 1.09 Nm for the HW model. The approaches seem similar with respect to the MAE and can be considered accurate knowing that the order of magnitude of the uncertainties of the dynamic platform was reported to be 2.2 N for the horizontal and vertical forces and 2.24 Nm for the sagittal moments. However, it should be noted that HW necessitates the knowledge of the average force and patterns of each subject, whereas the BiLSTM method do not involve the average patterns, which shows its superiority for time-series data prediction. The results provided in this study show the possibility of measuring dynamic forces acting at the handrim level during wheelchair manual propulsion in ecological environments.

A machine learning paradigm for necessary observations to reduce uncertainties in aerosol climate forcing.

Uncertainties in estimates of climate cooling by anthropogenic aerosols have not decreased significantly in the last two decades, partly because observational constraints on crucial aerosol properties simulated in Earth System Models are insufficient. To help address this insufficiency in aerosol observations, we describe a paradigm for deriving higher-level aerosol properties with machine learning algorithms that use only lidar observations and reanalysis data as predictors. Our paradigm employs high-accuracy suborbital lidar and collocated in situ measurements to train and test two fully-connected neural network algorithms. We use two lidar data sets as input to our machine learning algorithms. The first data set consists of suborbital lidar observations not previously used in the training of the machine learning algorithms. The second data set consists of simulated UV-only observations to preview the algorithms' predictive capabilities in anticipation of data from the ATmospheric LIDar system on the EarthCARE satellite, which was launched in May 2024. Here we show that our algorithms predict two crucial aerosol properties, aerosol light absorption and cloud condensation nuclei concentrations with unprecedented accuracy, yielding mean relative errors of 21% and 13%, respectively, when suborbital lidar data are used as predictors. These errors represent significant improvements over conventional aerosol retrievals. Applied to future satellite missions, the paradigm presented here has great potential for constraining Earth System Models and reducing uncertainties in their estimates of aerosol climate forcing and future global warming.

Estimation of breakout force of spudcan footing in structured clay

For a jack-up rig installed in cohesive soil, assessing the extraction force is critical to ensure the safe and successful retrieval of each spudcan footing. The extraction force depends on the preceding stages, including installation and operation of the rig, due to soil strength changes around the spudcan. To investigate the entire ‘penetration - operation - extraction’ process of spudcan, an effective stress large deformation finite element analysis is conducted, with a Structured Cam-Clay model being incorporated to describe the behaviour of structured soil. The large deformation approach is validated against existing centrifuge tests. Parametric studies are then carried out to explore the influential factors on breakout force, including operation period, vertical load ratio, penetration depth, destructuring rate and soil sensitivity. A unique curve is formulated to predict the evolution of the recovery degree of breakout force over the operation period in structured cohesive soil. The soil sensitivity is identified as the dominant factor that affects the ultimate net resistance ratio, which is the key parameter required in applying the prediction curve. A simple method is proposed to estimate the ultimate net resistance ratio and a procedure to predict the breakout force is suggested.