Moving Load Identification of Small and Medium-Sized Bridges Based on Distributed Optical Fiber Sensing

Abstract

A novel method was proposed for the moving load identi¯cation of bridges based on the in°uence line theory and distributed optical¯ber sensing technique. The method of load and vehicle speed identi¯cation was¯rstly theoretically studied, and then numerical simulation was also performed to study its accuracy and robustness. The numerical results showed that this method was characterized by high accuracy and excellent resistance to noise. Finally, the load identi¯cation of an actual continuous pre-stressed concrete beam bridge was carried out with the proposed method. The bridge consists of four pre-stressed box beams. At the same time, a weigh-in-motion system was also installed about 200 m in front of the bridge to measure the speed and moving loads with a purpose of comparing the load identi¯cation of the proposed method. Long gaugē ber Bragg grating (FBG) sensors with a gauge length of 1.0 m were adhered to the bottom of the beams. The individual loaded vehicles and the corresponding structure response were mainly monitored as standard samples, and the speed and weight of the sample vehicles were monitored and identi¯ed with the proposed method. The results revealed that the distributed long gauge FBG sensors were capable of sensing the structure response precisely and identifying the tra±c load. On the basis of the design information and ambient vibration testing results, a re¯ned model was established and the response under unit moving load was acquired for load identi¯cation. It was also shown that the sensors in di®erent positions can achieve accurate vehicle speed and weight, the relative error of which are within 10% and 15%, respectively.

Moving Load Identification with Long Gauge Fiber Optic Strain Sensing

“…Within the method of identifying vehicle parameters using the glowworm swarm optimization algorithm (GSO), the vehicle system of 2-DOF with 5 parameters and 4-DOF with 12 parameters were used to solve the objective function of the optimization problem (Li et al, 2016). Based on the influence line theory and distributed optical fiber sensing technique, a method to obtain the load of a single truck has been developed (Yang et al, 2016). A local linear embedding algorithm is used to identify continuous or discontinuous loads from the response data (Zhang et al, 2019).…”

Advances of Area-Wise Distributed Monitoring Using Long Gauge Sensing Techniques

“…The loading identification method using long gauge strains are studied to measure both load and vehicle speed in a practical and robust manner [72]. Under multi-axle load, the identified vehicle load is expressed as normalP=∫0l1+L2F(x)dx∫0L1f(x)dx where P is the vehicle load, L1 is the bridge length, and l1 is the vehicle length, F(x) is the influence line of bending moment at mid-span section under multiple axles, x is the distance between the first axle to the bridge end, f(x) is the influence line of bending moment at the mid-span section under a single axle.…”

“… Load application to a continuous beam bridge: ( a ) Sensor installation; ( b ) Typical long gauge strains; ( c ) Relative error for speed; ( d ) Relative error for load [ 72 ]. …”

Structural deformation reconstruction by the Penrose–Moore pseudo-inverse and singular value decomposition–estimated equivalent force

“…Therefore, the combination of the FEM and in-field measurements has attracted extensive attention. [3][4][5] Based on the concept of combining the FEM with the in-field measurements, the bridge weight in-motion (BWIM) algorithm, 6,7 which utilizes the influence line obtained from the FEM or a loading test, 8 is studied to identify the vehicle load information including vehicle weight, speed, axial space, and so on.…”