Stand-alone NDT system for tensile force estimation in cables and tie rods

2014 ◽  
Author(s):  
Carlo Rainieri ◽  
Danilo Gargaro ◽  
Luigi Cieri ◽  
Giovanni Fabbrocino
Keyword(s):  
Tensile Force ◽  
Force Estimation ◽  
Tie Rods

scholarly journals Dynamic Identification of Tensile Force in Tie-Rods by Interferometric Radar Measurements

2021 ◽  
Vol 11 (8) ◽  
pp. 3687
Author(s):  
Domenico Camassa ◽  
Anna Castellano ◽  
Aguinaldo Fraddosio ◽  
Giuseppe Miglionico ◽  
Mario Daniele Piccioni
Keyword(s):  
Tensile Force ◽  
Dynamic Tests ◽  
Force Estimation ◽  
Dynamic Identification ◽  
Historical Masonry ◽  
Radar Measurements ◽  
Tie Rods ◽  
Non Destructive ◽  
Identification Model

An experimental investigation on the accuracy of dynamically determined tensile force in tie-rods by applying the interferometric radar technique was performed. Tie-rods were used in historical masonry constructions for absorbing thrusts of arches and vaults, and the radar interferometry may represent a fast and easy non-destructive approach for the tensile force identification in the occasion of structural assessments. Laboratory dynamic tests on a cable under a known tensile force show that, provided that a suitable dynamic identification model is used, tensile force evaluations made stating from interferometric radar measurements were characterized by a very good accuracy (mean error in the tensile force estimation less than 2%), comparable with evaluations made starting from accelerometric measurements. In particular, the dynamic identification model considered is a modified version of a model proposed in the literature. The influence on the accuracy in the determination of the tensile force of some features of the experimental setup, like, e.g., the employ of corner reflectors, is discussed.

Field applicability of a machine learning–based tensile force estimation for pre-stressed concrete bridges using an embedded elasto-magnetic sensor

2019 ◽  
Vol 19 (1) ◽  
pp. 281-292
Author(s):  
Junkyeong Kim ◽  
Seunghee Park
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Tensile Force ◽  
Radial Basis Function Network ◽  
Feedforward Neural Network ◽  
Concrete Bridges ◽  
Force Estimation ◽  
Radial Basis ◽  
Function Network

It has been proposed that pre-stressed concrete bridges improve load performance by inducing axial pre-stress using pre-stress tendons. However, the tensile force of the pre-stress tendons could not be managed after construction, although it directly supports the load of the structure. Thus, the tensile force of the pre-stress tendon should be checked for structural health monitoring of pre-stressed concrete bridges. In this study, a machine learning–based tensile force estimation method for a pre-stressed concrete girder is proposed using an embedded elasto-magnetic sensor and machine learning method. The feedforward neural network and radial basis function network were applied to estimate the tensile force of the pre-stress tendon using the area ratio of the magnetic hysteresis curve measured by the embedded elasto-magnetic sensor. The feedforward neural network and radial basis function network were trained using 213 datasets obtained in laboratory experiments, and trained feedforward neural network and radial basis function network were applied to a 50-m real-scale pre-stressed concrete girder test for estimating tensile force. Nine embedded elasto-magnetic sensors were installed on the sheath, and the magnetic hysteresis curves of the pre-stress tendons were measured during tensioning. The area ratio was extracted and inputted to the trained feedforward neural network and radial basis function network to estimate the tensile force. The estimated tensile force was compared with the reference tensile force measured by the load cell. According to the result, the estimated tensile force can represent the actual tensile force of the pre-stress tendon without calibrating tensile force estimation algorithms at the site. In addition, it can measure the actual friction loss by estimating the tensile force at the maximum eccentric part. Based on the results, the proposed method might be a solution for the structural health monitoring of pre-stressed concrete bridges with field applicability.

scholarly journals Embedded EM Sensor for Tensile Force Estimation of PS tendon of PSC Girder

2015 ◽  
Vol 28 (6) ◽  
pp. 691-697
Author(s):  
Jooyoung Park ◽  
Junkyeong Kim ◽  
Aoqi Zhang ◽  
Hwanwoo Lee ◽  
Seunghee Park
Keyword(s):  
Tensile Force ◽  
Force Estimation

The dynamical identification of the tensile force in ancient tie-rods

2005 ◽  
Vol 27 (6) ◽  
pp. 846-856 ◽  
Author(s):  
Sergio Lagomarsino ◽  
Chiara Calderini
Keyword(s):  
Tensile Force ◽  
Tie Rods

Vibration-Based Assessment of the Tensile Force in the Tie-Rods of the Milan Cathedral

2019 ◽  
Vol 13 (3) ◽  
pp. 411-424 ◽  
Author(s):  
Carmelo Gentile ◽  
Carlo Poggi ◽  
Antonello Ruccolo ◽  
Mira Vasic
Keyword(s):  
Tensile Force ◽  
Tie Rods

Estimation of tensile force in tie-rods using a frequency-based identification method

2010 ◽  
Vol 329 (11) ◽  
pp. 2057-2067 ◽  
Author(s):  
M. Amabili ◽  
S. Carra ◽  
L. Collini ◽  
R. Garziera ◽  
A. Panno
Keyword(s):  
Tensile Force ◽  
Identification Method ◽  
Tie Rods

scholarly journals Vision-Based Suture Tensile Force Estimation in Robotic Surgery

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 110
Author(s):  
Won-Jo Jung ◽  
Kyung-Soo Kwak ◽  
Soo-Chul Lim
Keyword(s):  
Force Feedback ◽  
Short Term Memory ◽  
Tensile Force ◽  
Force Sensor ◽  
Interaction Force ◽  
Force Estimation ◽  
Training Experience ◽  
Pulling Force

Compared to laparoscopy, robotics-assisted minimally invasive surgery has the problem of an absence of force feedback, which is important to prevent a breakage of the suture. To overcome this problem, surgeons infer the suture force from their proprioception and 2D image by comparing them to the training experience. Based on this idea, a deep-learning-based method using a single image and robot position to estimate the tensile force of the sutures without a force sensor is proposed. A neural network structure with a modified Inception Resnet-V2 and Long Short Term Memory (LSTM) networks is used to estimate the suture pulling force. The feasibility of proposed network is verified using the generated DB, recording the interaction under the condition of two different artificial skins and two different situations (in vivo and in vitro) at 13 viewing angles of the images by changing the tool positions collected from the master-slave robotic system. From the evaluation conducted to show the feasibility of the interaction force estimation, the proposed learning models successfully estimated the tensile force at 10 unseen viewing angles during training.

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