Estimation Of Tension Research Articles

Tension estimation for cross-linked cables based on vibration frequencies with a dynamic stiffness approach

ABSTRACT Using vibration measurements to determine tension forces is a well-established technique for diagnosing tensioned slender members. Traditional approaches are unsuitable for cables attached to transverse fasteners or intersect clamps. Therefore, the assessment process for linked cables generally requires unlocking and reconnecting the fasteners and clamps during vibration tests. This paper introduces an integrated idea of using single vibration measurements with simple analytical models to develop a practical scheme to estimate the tension of individual members for cross-linked cables. The development of the method relies on using simple dynamic stiffness formulas of the string model such that iterative approximations or optimised searching with numerical coding can be effectively executed for both the backward estimation and forward verification stages of the assessment procedure. The effectiveness of the proposed methodology has been numerically studied and verified with selected data from field tests. The procedure can provide a better on-site tool for the vibration tests of fastener-restrained suspenders and cross-linked cables.

Cable tension estimation by Rayleigh’s method considering restraint boundary conditions

This paper introduces a method for accurately estimating cable tension, combining the energy approach with the cable’s mode shape. The method simultaneously accounts for the bending rigidity of the cable and the rotational stiffness at both ends. Rayleigh’s energy-based method is applied to analytically derive a formula for cable tension, while the mode shape is approximated using a nonlinear regression analysis algorithm. The accuracy of the method is validated through comparison with available experimental data. The approach is applied to the An Dong Extradosed Bridge in Vietnam, demonstrating its effectiveness in evaluating cable forces for similar bridge structures. Notably, a significant difference of up to 13.13% in cable forces is observed when considering the rotational stiffness at cable ends, highlighting the importance of this factor in structural analysis.

Noncontact tension force measurement of an arch bridge with a developed lightweight millimeter-wave radar system

Abstract Accurate measurement of tension forces in vertical hangers is critical for ensuring the operational safety of arch bridges. To overcome the limitations of traditional contact-based sensors, such as time-consuming deployment and labor-intensive processes, this study introduces a lightweight and -cost-effective millimeter-wave radar system for efficient tension force monitoring. The contributions of this work are twofold: (1) A lightweight radar system is developed, integrating a compact radar unit, embedded data processing modules, and a remote control computer. This system achieves real-time tension force measurement with ultra-high spatial resolution (5 cm), superior to conventional centimeter-wave radar systems, while significantly reducing weight (1 kg) and hardware costs. (2) A theoretical framework is established to correlate vibration displacement (captured by the developed radar) with dynamic tension forces, incorporating the analysis of critical parameters such as measurement alignment ( in-plane vs. out-of-plane directions) and sensor positioning effects. Field experiments on an operational arch bridge validate the proposed methodology. Key findings include : (a) Measurement direction has negligible influence (<1% relative error) on tension estimation, though in-plane alignment yields marginally higher accuracy; (b) While sensor positioning strongly affects displacement amplitudes, vibration frequencies (and thus derived tension forces) remain position-insensitive; (c) Radar-measured tension forces exhibit <3% deviation from reference sensors. These results demonstrate that the developed millimeter-wave radar system has significant potential for tension force measurement and safety evaluation of arch, suspension, and cable-stayed bridges in transportation networks.

Bootstrap for finite N lattice Yang-Mills theory

We introduce a comprehensive framework for analyzing finite N lattice Yang-Mills theory and finite N matrix models. Utilizing this framework, we examine the bootstrap approach to SU(2) Lattice Yang-Mills Theory in 2,3 and 4 dimensions. The SU(2) Makeenko-Migdal loop equations on the lattice are linear and closed exclusively on single-trace Wilson loops. This inherent linearity significantly improves the efficiency of the bootstrap approach by leveraging the problem’s convexity, permitting the inclusion of Wilson loops up to length 24. The exact upper and lower margins for the free energy per plaquette, derived from our bootstrap method, demonstrate good agreement with Monte Carlo data, achieving precision within 0.1% for the physically relevant range of couplings in both three and four dimensions. Additionally, our bootstrap data provides estimates of the string tension, in qualitative agreement with existing Monte Carlo computations.

Contact Angle Measurements of the Apparent Line Tension Are Spurious.

Phenomena in diverse contexts such as wetting, biological assembly, and manufacturing are attributed to the three-phase line tension. However, decades of line tension estimates based on contact angles of droplets controversially span 6 orders of magnitude, raising the question of which measurements are authoritative. Here, we show with experiments and calculations that contact angles fail to estimate line tension regardless of length scale, technique, and measurement quality. Line tension measurements based on contact angles are driven by two distinct and spurious mechanisms: body forces under ideal conditions, and data scatter under noisy conditions.

Mode-shape-based tension and bending stiffness estimation method for Nielsen–Lohse bridge cables without removing intersection clamps

Mode-shape-based tension and bending stiffness estimation method for Nielsen–Lohse bridge cables without removing intersection clamps

Stay cable tension estimation using a vision-based monitoring system under complex environments

Stay cable tension estimation using a vision-based monitoring system under complex environments

Machine learning-based estimation of crude oil-nitrogen interfacial tension

Accurate estimation of interfacial tension (IFT) between nitrogen and crude oil during nitrogen-based gas injection into oil reservoirs is imperative. The previous research works dealing with prediction of IFT of oil and nitrogen systems consider synthetic oil samples such n-alkanes. In this work, we aim to utilize eight machine learning methods of Decision Tree (DT), AdaBoost (AB), Random Forest (RF), K-nearest Neighbors (KNN), Ensemble Learning (EL), Support Vector Machine (SVM), Convolutional Neural Network (CNN) and Multilayer Perceptron Artificial Neural Network (MLP-ANN) to construct data-driven intelligent models to predict crude oil – nitrogen IFT based upon experimental data of real crude oils samples encountered in underground oil reservoirs. Several statistical indices and graphical approaches are used as accuracy performance indicators. The results show that virtually all the gathered datapoints are suitable for the purpose of model development. The sensitivity analysis indicated that pressure, temperature and crude oil API all negatively affect the IFT, with pressure being the most effective factor. The evaluation study proved that Random Forest is the most accurate developed intelligent model as it was characterized with acceptable R-squared (0.959), mean square error (1.65), average absolute relative error (6.85%) of unseen test datapoints as well as with correct trend prediction of IFT with regard to all input parameters of pressure, temperature and crude oil API. The developed model can be considered an accurate an easy-to-use tool for the prediction of crude oil/N2 IFT values for enhance oil recovery study optimization and upstream reservoir investigations.

Overhead Power Line Tension Estimation Method Using Accelerometers

Overhead power lines are important components of power grids, and the status of transmission line equipment directly affects the safe and reliable operation of power grids. In order to guarantee the reliable operation of lines and efficient usage of the power grid, the tension of overhead power is an important parameter to be measured. The tension of power lines can be calculated from the modal frequency, but the measured acceleration data obtained from the accelerometer is severely contaminated with noises. In this paper, a multiscale-based peak detection (M-AMPD) algorithm is used to find possible modal frequencies in the power spectral density of acceleration data. To obtain a reliable noise-free signal, median absolute deviations with baseline correction (MAD-BS) algorithm are applied. An accurate estimation of modal frequencies used for tension estimation is obtained by iteration of the MAD-BS algorithm and reduction in frequency range technique. The iterative range reduction technique improves the accuracy of the estimated tension of overhead power lines. An accurate estimation of overhead power line tension can contribute to improving the reliability and efficiency of the power grid. The proposed algorithm is implemented in MATLAB R2020a and verified by comparison with measured data by a tensiometer.

Dynamic response and tension estimation of stay cables with disturbed images acquired from vision sensors

ABSTRACT This study introduces a novel approach to measure cable responses of such bridges using a vision sensor comprising a digital camcorder mounted on a tripod. Prioritizing simplicity and efficiency, this setup requires minimal equipment. The sensor is strategically positioned to ensure the target cable remains within the camcorder’s field of view. However, operational bridge imagery is frequently disturbed by vehicular traffic, leading to defocusing and blurring due to the camcorder’s automatic focus adjustments. These disturbances complicate the identification of reference points essential for accurate cable response measurements, potentially skewing results. To address this, we apply an image quality evaluation method to distinguish reference points from disturbed images, mitigating the impact of these disturbances on measurement accuracy. We evaluated the method’s effectiveness in estimating cable tension under disturbances by capturing images of an operational cable-stayed bridge experiencing ambient vibrations. Cable tension estimations were derived from the analysed responses using the vibration method and validated against data from a long-term monitoring accelerometer. This comparison highlights the potential of the proposed vision sensor method to enhance the accuracy of cable tension measurements in cable-stayed bridges, even in the presence of disturbed images.

In Silico Evaluation and Experimental Validation of Interfacial Properties in 3-5 Residue Peptides.

Predicting the interfacial properties of peptides is important for replacing oil-derived surfactants in cosmetics, oil, and agricultural applications. This work validated experimentally the estimations of surface tension at the critical micelle concentration (STCMC) of six peptides performed through a random forest (RF) model in a previous contribution. In silico interfacial tensions of the peptides were obtained in the system decane-water, and dilational experiments were applied to elucidate the foaming potential. The RF model accurately classified the peptides into high and low potential to reduce the STCMC. The simulations at the decane-water interface correctly identified peptides with high, intermediate, and low interfacial properties, and the dilational rheology allowed the estimation of the possible potential of three peptides to produce foams. This study sets the basis for identifying surface-active peptides, but future work is necessary to improve the estimations and the correlation between dilational properties and foam stabilization.

Artificial neural network framework for the selection of deep eutectic solvents promoted enhanced oil recovery by interfacial tension reduction mechanism

Artificial neural network framework for the selection of deep eutectic solvents promoted enhanced oil recovery by interfacial tension reduction mechanism

Self diffusion, viscosity, and surface tension estimations of amines by using AUA4 force field

Self diffusion, viscosity, and surface tension estimations of amines by using AUA4 force field

Maximum-A-Posteriori Estimation of Retinal Oxygen Tension Based on Photon Counting Statistics.

The regularization of retinal oxygen tension estimation was previously proposed with an assumption that phosphorescence intensity images are corrupted by additive Gaussian noise. Based on this assumption, a regularized least-squares estimate has been shown to be better than a conventional least-squares estimation. However, this assumption is inconsistent with the acquisition process of phosphorescence intensity images acquired using an intensified charge-coupled device camera. Almost the entire acquisition process is governed by the natural aspects of photons. Therefore, a method based on photon counting statistics is more appropriate. In this study, we propose a regularized oxygen tension estimation method based on photon counting statistics and a phosphorescence lifetime imaging model.

Physics Informed Gaussian Process for Bolt Tension Estimation

Bolted joints are fundamental components in many engineering applications. Therefore, the need for monitoring their tension over their life span is an essential for securing their integrity. Modelling of the dynamics of a bolt has shown success through Euler-Bernoulli beam theory where a relationship of boundary conditions and tension allows determining the changes in the modal parameters. However, the widespread adoption of this approach has faces challenges, as obtaining high-fidelity data for bolts under all tension phases is often unfeasible in practice, particularly for those in low tension. Nevertheless, merging data and prior physics knowledge can provide practical constraints for bolt tension estimation in areas lacking observational data. This study establishes its foundation by developing a stochastic model for bolt tension estimation through the integration of bolt data-driven and physics-based predictions using Gaussian Process Regression (GPR). The core concept of this approach involves predicting data observations through a stochastic simulation using a physics-based model, particularly in scenarios where observational data is absent. The proposed methodology is validated with experimental data to critically evaluate its performance.

Research on Optimization of Nonlinear Extended State Observer for the Upper-Limb Rehabilitation Robot Controller

Background: In recent years, patents have shown that upper-limb rehabilitation robots play a significant role in home-based rehabilitation training for stroke hemiplegia patients. Changes in muscle tension during the flaccid paralysis phase cause the rehabilitation robot to deviate from the predetermined motion trajectory. This poses a challenge to the mathematical modeling of the rehabilitation robot and the design of the nonlinear extended state observer (NESO). Objective: To address the problem of trajectory tracking errors caused by the rehabilitation robot when the patient's muscle tension changes, a method based on optimizing the nonlinear function within the NESO is proposed. Methods: First, a mathematical model of the upper-limb rehabilitation robot is established to obtain the dynamic relationship between the robot's velocity and the input voltage. Second, the nonlinear function of NESO is optimized by adjusting the gain values of the sine and tangent functions to effectively estimate the muscle tension and reduce the problem of accumulative error due to changes in muscle tension. Finally, comparative simulations of trajectory tracking including the optimized nonlinear function as well as the sine, tangent and power functions are performed on the MATLAB platform. Results: NESO is constructed to estimate changes in muscle tension based on sine, tangent, and power functions, as well as optimized nonlinear functions. The equivalent gain values of each function and the muscle tension estimation curves of NESO are compared. The simulation results show that the equivalent gain value of the optimized nonlinear function acting in NESO is increased to more than 8 and the convergence time is reduced by 11.9% accordingly. This conclusion is validated by simulations and practical tests. Conclusion: The optimized nonlinear function shortens the estimation time of the NESO for changes in muscle tension, which can provide a reference value for the design of observers for upper-limb rehabilitation robots of patients in the flaccid paralysis stage.

Remote estimation of cable tension using catenary theory and point clouds obtained by terrestrial laser scanning

This study addressed the structural health monitoring of civil engineering cable structures, in which single cable tension is the key parameter influencing safe and intended behaviour. Traditional methods for monitoring the tension force in a cable are labour-intensive and require direct interaction with the cable. In contrast, the proposed approach provides a remote and rapid assessment of the tension force in the cables of engineering structures using only the shape of the cable. First, a terrestrial laser scanner was used to acquire a point cloud of the cable geometry from a distance. Catenary theory was subsequently applied to the point cloud data to calculate the theoretical tensile force in the observed cable. When the results were compared with those obtained through conventional direct measurements using a dynamometer, the differences were negligible. The proposed approach was subsequently demonstrated on the actual in-service overhead contact lines of a tram system. The results indicated that the precision of the proposed approach is highly dependent on the accuracy of the data describing the physical properties of the cable (cable linear mass). Thus, the proposed approach was proven to be precise, reliable, and rapid, and directions for future research were discussed accordingly.

Application of a simple two-point measurement strategy with emphasizing low-frequency signals to the force monitoring of cables

When dealing with vibration tests of tensioned suspenders and cables, signals containing relatively high frequency components in comparing to the fundamental frequency are normally required in order to carry out reliable diagnoses for force estimations. Meanwhile, the necessity of obtaining a precise and broadband spectrum of the vibration signals would inevitably need to acquire relatively detailed time responses. One typical difficulty, which arises when incorporating vibration analyses into monitoring tasks, is the inefficiency caused by the post- processing for the huge amounts of time data. In this work, a set of simple yet effective formulas associated with two-point measurements will be briefly addressed. The proposed formulas are derived based on the continuous formulations of the string model, and the corresponding applications to the tension estimations of slender members will be demonstrated using numerical and experimental data related to stayed cables of various restrained scenarios and linked-suspenders as well. The purposes of this work are twofold; firstly to introduce the potential usefulness of the proposed measurement strategy to form effective schemes for the tension assessments; secondly to evolve an efficient incorporation of wireless measuring techniques without the need of processing cumbersome time data which are the common consequences the analysts would encounter in the analysis tasks. Finally, this work aims to highlight practical ideas in guiding the use of the proposed techniques in the monitoring of cables/suspenders in field.

A novel analysis method for tension estimation of cross-linked suspenders based on single vibration measurements

While the use of vibration measurements to detect axially loaded forces is a well-established scheme for the diagnosis of tensioned slender members, the traditional approaches are not suitable for suspenders which are attached with transverse fasteners. As a result, the evaluation process for linked suspenders generally requires unlocking and reconnecting of the fasteners during performing vibration tests. This paper presents an innovated idea of using single vibration measurements with simple analytical models to develop an effective technique to estimate tension of individual member for cross-linked suspenders. The development of the technique relies on the use of simplified string formulas such that iterative approximations with numerical coding can be effectively executed for both the backward estimation and forward verification stages of the assessment procedure. The feasibility of the proposed method has been numerically studied and verified with selected data from field tests. It is anticipated that the procedure can serve as a better on-site tool for the vibration tests of fastener- restrained suspenders and cables.

A Tension Sensor Array for Cable-Driven Surgical Robots.

Tendon-sheath structures are commonly utilized to drive surgical robots due to their compact size, flexibility, and straightforward controllability. However, long-distance cable tension estimation poses a significant challenge due to its frictional characteristics affected by complicated factors. This paper proposes a miniature tension sensor array for an endoscopic cable-driven parallel robot, aiming to integrate sensors into the distal end of long and flexible surgical instruments to sense cable tension and alleviate friction between the tendon and sheath. The sensor array, mounted at the distal end of the robot, boasts the advantages of a small size (16 mm outer diameter) and reduced frictional impact. A force compensation strategy was presented and verified on a platform with a single cable and subsequently implemented on the robot. The robot demonstrated good performance in a series of palpation tests, exhibiting a 0.173 N average error in force estimation and a 0.213 N root-mean-square error. In blind tests, all ten participants were able to differentiate between silicone pads with varying hardness through force feedback provided by a haptic device.