Search arXiv⌕ Search

arXiv · 2508.17109

The non-contact assessment of the bridge expansion joint

Abstract

The aim of the paper is to provide information on a newly developed design methodology for the evaluation of bridge expansion joints with respect to their noise emission and overall technical condition. The methodology also gives recommendations for operational non-destructive measurements of the condition of bridge gates using a crossing laser sensors, CPX sensors or the necessary technical equipment and the method of collecting, processing and evaluating the measured data. The method can easily scan and evaluate geometry and/or noise emission of extensive number of joints by passing them without traffic interruption. The aim is to establish a methodology for comparing the noise and condition of bridge expansion joints in the road network, both over time (monitoring long-term trends in noise emission and degradation) and comparing different states and different types of bridge closures between each other.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Pavel Ryjáček, Kirill Golubiatnikov, Ondřej Simon, Libor Ládyš, Martin Ládyš, Ondřej Červenka, Petr Klokočník. 2025-08-23. The non-contact assessment of the bridge expansion joint. https://arxiv.org/abs/2508.17109

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

A Physics-Driven Framework for Parametric Periodic-Flow Modeling and Finite-Amplitude Aeroelastic Response Analysis

Periodic unsteady flows are common in forced-motion and fluid-structure interaction problems. Their parametric analysis typically requires repeated high-fidelity simulations, whereas existing reduced-order and surrogate models generally rely on pre-generated flow-field or aerodynamic data. This study proposes a purely physics-driven framework for solving parametric periodic flows and finite-amplitude aeroelastic responses. First, a Periodic Physics-Informed Neural Network (P-PINN) is developed to directly solve periodic flows by imposing temporal periodicity over a single motion cycle, thereby avoiding the need to resolve the long transient evolution preceding the establishment of the periodic state. The flow conditions and motion parameters are further incorporated as network inputs to construct continuous parametric representations of the periodic flow field and aerodynamic forces. On this basis, the parametric aerodynamic model is coupled with the structural dynamic equation through first-order harmonic balance to solve the response amplitude and frequency of a single-degree-of-freedom aeroelastic system. This framework is validated using forced-motion cases of a circular cylinder and an airfoil, demonstrating accurate reproduction of periodic aerodynamic forces, surface load distributions, and instantaneous flow fields under different flow and motion parameters compared with time-marching results. Furthermore, aeroelastic analysis is conducted for an elastically mounted circular cylinder at subcritical Reynolds numbers, and the resulting aeroelastic response agrees well with fully coupled CFD/CSD results. Once trained offline, the parametric model can be repeatedly evaluated for different structural parameter states, enabling the complete aeroelastic response curve to be obtained online within seconds, without repeated long-time fluid-structure interaction time marching.

physics.comp-ph↗

Monte Carlo sampling of first-order QED processes in laser and pulsar plasmas

Monte Carlo sampling of strong-field quantum electrodynamics processes underpins simulations of high-intensity laser experiments and of astrophysical compact-object magnetospheres. Sampling an event requires the total rate of the process together with the cumulative probability that determines how energy is partitioned between the produced particles. Simulations typically tabulate both in advance and invert the tabulated probability numerically. Here we replace this procedure with elementary-function approximations for synchrotron radiation and the nonlinear Breit--Wheeler process. For each process, we approximate the auxiliary function that sets the total rate, as well as the cumulative probability, with Padé approximants chosen so that the inversion reduces to a quartic equation. This yields the sampled quantum parameter---electron $χ_e$ or photon $χ_γ$---in closed form. The approximations and the particle spectra sampled from them agree with the exact results to within $1\%$. The procedure requires no lookup tables, no interpolation, and no numerical root finding, and can be inserted directly into radiative particle-in-cell codes.

physics.comp-ph↗

CTrex: A Research-Oriented Framework for Kernel- and Projection-Level Algorithm Development in CT Reconstruction

Micro-computed tomography (micro-CT) is increasingly applied to imaging scenarios in which object motion, deformation, truncated fields of view, continuous rotation, or unconventional scanning protocols violate assumptions underlying classical reconstruction pipelines. Existing reconstruction frameworks efficiently support standard geometries and workflows but offer limited control over how projection operators are discretized and evaluated. This restricts methodological exploration when accuracy depends on ray sampling, interpolation, or operator coupling. We introduce CTrex, a research-oriented, GPU-accelerated iterative reconstruction framework that exposes the projection-correction-backprojection pipeline down to the kernel level. Rather than treating these operators as fixed black boxes, CTrex represents them as editable computational building blocks within a unified iterative structure. This allows non-ideal acquisition effects to be incorporated directly into the numerical operators rather than treated as external corrections. CTrex combines GPU-level accessibility with an extensible geometry formulation based on homogeneous-coordinate view matrices, allowing rigid and affine transformations, detector misalignments, and time-varying geometries to be expressed consistently. It supports circular, helical, offset, and conveyor-belt trajectories while keeping GPU kernels agnostic to the trajectory definition. Applications in motion- and deformation-aware reconstruction, event-based 4D imaging, cylindrical-coordinate reconstruction, and extended-field-of-view CT illustrate how operator-level adaptations enable strategies difficult to realize in conventional frameworks. CTrex provides a flexible platform for CT reconstruction research under realistic and unconventional imaging conditions.

physics.comp-ph↗