An integrated solution for tension monitoring in bridge hangers and stay cable by means of vibration measurement

<p>The determination of tension in bridge cables by means of vibration measurements has been used by the Direction of civil engineering expertise for decades to monitor the bridges managed by the Walloon Region (SPW). Over time, it has appeared that the traditional methodology, based on sparse on-site measurement and the taut string theory, suffers from certain limitations: the two most important are the delay between measurements (at best once a year) and the difficulties in solving the equation linking the vibration response to the tension in the stay, in the case of short or geometrically complex cable.</p><p>An agreement was concluded between the SPW, the University of Liège and V2i, a company specialised in vibrations, to develop a solution including a software allowing the tension determination in any kind of bridge cable and the hardware to monitor the cable tension remotely for a long time.</p><p><br clear="none"/></p>

Tube-Based Taut String Algorithms for Total Variation Regularization

Removing noise from signals using total variation regularization is a challenging signal processing problem arising in many practical applications. The taut string method is one of the most efficient approaches for solving the 1D TV regularization problem. In this paper we propose a geometric description of the linearized taut string method. This geometric description leads to the notion of the “tube”. We propose three tube-based taut string algorithms for total variation regularization. Different weight functionals can be used in the 1D TV regularization that lead to different types of tubes. We consider uniform, vertically nonuniform, vertically and horizontally nonuniform tubes. The proposed geometric approach is used to speed-up TV regularization processing by dividing the tubes into subtubes and using parallel processing. We introduce the concept of a relatively convex tube and describe the relationship between the geometric characteristics of tubes and exact solutions to the TV regularization. The properties of exact solutions can also be used to design efficient algorithms for solving the TV regularization problem. The performance of the proposed algorithms is discussed and illustrated by computer simulation.

Estimasi Gaya Tarik pada Kabel Jembatan Cable Stayed dengan Pengujian Dinamik

Kabel merupakan salah satu elemen utama pada jembatan cable stayed yang fungsinya memikul dan meneruskan beban pada balok dan lantai jembatan ke struktur pilon. Elemen kabel perlu selalu dilakukan monitoring dan pemeliharaan selama umur jembatan, sehingga gaya kabel yang bekerja tidak melampaui kapasitasnya. Salah satu metode dilakukan dengan uji dinamik, dimana nilai parameter dinamik (frekuensi dan mode getar) berkorelasi dengan gaya tarik kabel. Selain parameter dinamik, gaya tarik kabel juga dipengaruhi oleh parameter fisiknya, yaitu parameter tak-berdimensi l2 akibat efek kelengkungan (sag effect) dan parameter tak-berdimensi x akibat efek kekakuan lentur (bending stiffness effect). Berbagai rumusan pendekatan telah dikembangkan oleh para ahli, diantaranya: taut string, beam string, wei-xin, dan zui et.al. theory. Studi ini akan membahas perbandingan rumusan tersebut dengan mengambil studi kasus Jembatan Pedamaran 1, Riau. Hasil studi menunjukkan bahwa parameter tak berdimensi l2 dan x “cukup berpengaruh” dan perlu ditinjau dalam perhitungan, dimana kombinasi dari kedua parameter tersebut memberikan pengaruh sebesar 1.77% - 6.00% terhadap estimasi gaya tarik kabel. Penggunaan rumusan empiris taut string theory dan beam string theory memberikan estimasi gaya tarik kabel dengan tingkat kesalahan di bawah 7,0%, sedangkan rumusan empiris wei-xin dan zui et.al. memberikan hasil dengan akurasi paling baik dengan kesalahan di bawah 3,0%

On the Taut String Interpretation and Other Properties of the Rudin–Osher–Fatemi Model in One Dimension

We study the one-dimensional version of the Rudin–Osher–Fatemi (ROF) denoising model and some related TV-minimization problems. A new proof of the equivalence between the ROF model and the so-called taut string algorithm is presented, and a fundamental estimate on the denoised signal in terms of the corrupted signal is derived. Based on duality and the projection theorem in Hilbert space, the proof of the taut string interpretation is strictly elementary with the existence and uniqueness of solutions (in the continuous setting) to both models following as by-products. The standard convergence properties of the denoised signal, as the regularizing parameter tends to zero, are recalled and efficient proofs provided. The taut string interpretation plays an essential role in the proof of the fundamental estimate. This estimate implies, among other things, the strong convergence (in the space of functions of bounded variation) of the denoised signal to the corrupted signal as the regularization parameter vanishes. It can also be used to prove semi-group properties of the denoising model. Finally, it is indicated how the methods developed can be applied to related problems such as the fused lasso model, isotonic regression and signal restoration with higher-order total variation regularization.

Solution to the Problem of a Mass Traveling on a Taut String via Integral Equation

The problem of a massive taut string, traveled by a heavy point mass, moving with an assigned law, is formulated in a linear context. Displacements are assumed to be transverse, and the dynamic tension is neglected. The equations governing the moving boundary problem are derived via a variational principle, in which the geometric compatibility between the point mass and the string is enforced via a Lagrange multiplier, having the meaning of transverse reactive force. The equations are rearranged in the form of a unique Volterra integral equation in the reactive force, which is solved numerically. A classical Galerkin solution is implemented for comparison. Numerical results throw light on the physics of the phenomenon and confirm the effectiveness of the algorithm.