An accurate approach to determining the spatiotemporal vehicle load on bridges based on measured boundary slopes

TL;DR

This paper introduces a mathematical model and inverse problem approach to accurately determine spatiotemporal vehicle loads on bridges using boundary slope measurements, employing Tikhonov regularization and gradient methods.

Contribution

The paper develops a novel inverse problem framework for vehicle load identification on bridges from boundary slope data, with explicit gradient formulas and convergence guarantees.

Findings

Proves the compactness and Lipschitz continuity of the input-output maps.

Establishes the existence of a quasi-solution using Tikhonov regularization.

Derives an explicit gradient formula for the inverse problem.

Abstract

In this paper, a novel mathematical model is developed to evaluate the spatiotemporal vehicle loads on long bridges from slope measurements made at the ends of a bridge based on Euler-Bernoulli beam model with internal and external damping. The mathematical modelling of this phenomena leads to the inverse source problem of determining the spatiotemporal vehicle load $F(x,t)$ in the variable coefficient Euler-Bernoulli equation $\rho_A(x)u_{tt}+\mu(x) u_{t}+(r(x)u_{xx})_{xx}+(\kappa(x)u_{xxt})_{xx}=F(x,t)$, $(x,t)\in \Omega_T:=(0,\ell)\times (0,T)$ subject to the "simply supported" boundary conditions $u(0,t)=(r(x)u_{xx}+(\kappa(x)u_{xxt})_{x=0}=0$, $u(\ell,t)=(r(x)u_{xx}+(\kappa(x)u_{xxt})_{x=\ell}=0$, from the both measured outputs: $\theta_1(t):=u_x(0,t)$ and $\theta_2(t):=u_x(\ell,t)$, that is, the measured boundary slopes. It is shown that the input-output maps $(\Phi F)(t):=u_x(0,t;F)$, $(\Psi F)(t):=u_x(\ell,t;F)$, $F \in \mathcal{F}\subset L^2(\Omega_T)$, corresponding to the inverse problem, are compact and Lipschitz continuous. Then Tikhonov functional $J(F)=\Vert \Phi F-\theta_1 \Vert_{L^2(0,T)}^2+\Vert \Psi F-\theta_2 \Vert_{L^2(0,T)}^2$ is introduced to prove the existence of a quasi-solution to the inverse problem. An explicit gradient formula for the Fr\'{e}chet derivative of the Tikhonov functional is derived. The Lipschitz continuity of the Fr\'{e}chet gradient, which guarantees the monotonicity of iterations in gradient methods, has been proven.