A Most-Unfavorable-Condition Method for Bridge-Damage Detection and Analysis Using PSP-InSAR

The main contribution of this study is to provide a new idea to detect bridge damage by using PSP-InSAR technology. A most-unfavorable-condition method is proposed for bridge-damage detection and analysis. The method can determine the specific damaged location and occurrence time by using the differential deformation values of persistent scatterer (PS) points on bridge piers. Taking Beijing Suzhou Bridge as an experimental area, 96 COSMO-SkyMed time-series SAR images were used from September 2011 to November 2017. Deformation values of PS points around Suzhou Bridge were acquired and analyzed. Experimental results show that in July 2017, the unusual maximum differential deformation value was 25.73 mm. It occurred between piers D3 and D4 of Suzhou Bridge, and it was deduced that the main girder between piers D3 and D4 may have been damaged in July 2017. As a validation, taking the differential deformation value between piers D3 and D4 as an input, the maximum tensile stress, and the maximum compressive stress were calculated as 2.1 MPa and 8.4 MPa, respectively, through a finite element model. The tensile stress exceeded the design value of the concrete, further confirming the damage of the girder between piers D3 and D4. Moreover, all results are consistent with the Suzhou Bridge damage information shown in existing records, which verify the accuracy and reliability of the proposed method.

Case Study of Deep Learning Model of Temperature-Induced Deflection of a Cable-Stayed Bridge Driven by Data Knowledge

A cable-stayed bridge is a typical symmetrical structure, and symmetry affects the deformation characteristics of such bridges. The main girder of a cable-stayed bridge will produce obvious deflection under the inducement of temperature. The regression model of temperature-induced deflection is hoped to provide a comparison value for bridge evaluation. Based on the temperature and deflection data obtained by the health monitoring system of a bridge, establishing the correlation model between temperature and temperature-induced deflection is meaningful. It is difficult to complete a high-quality model only by the girder temperature. The temperature features based on prior knowledge from the mechanical mechanism are used as the input information in this paper. At the same time, to strengthen the nonlinear ability of the model, this paper selects an independent recurrent neural network (IndRNN) for modeling. The deep learning neural network is compared with machine learning neural networks to prove the advancement of deep learning. When only the average temperature of the main girder is input, the calculation accuracy is not high regardless of whether the deep learning network or the machine learning network is used. When the temperature information extracted by the prior knowledge is input, the average error of IndRNN model is only 2.53%, less than those of BPNN model and traditional RNN. Combining knowledge with deep learning is undoubtedly the best modeling scheme. The deep learning model can provide a comparison value of bridge deformation for bridge management.

Segmental Intelligent Model Test of Box Girder Bridge Strengthened By a Cable-Stayed System by Mathematical Calculation

Dongming Yellow River Highway Bridge is an important super large bridge across the Yellow River on national highway 106. In order to effectively improve the cracking and deflection of the bridge, the cable-stayed system is used to reinforce the main girder of the project. In order to analyze the stress state of joist, bracket and box girder, the static loading of cable is carried out through scale model test to study the mechanical properties of concrete box girder segment, joist and bracket, verify the reliability of bracket and concrete box girder anchorage, and optimize the reinforcement method of cable-stayed system according to the test results.

Comparative Analysis of Three Typical Cantilever Scaffold Supporting Systems

In order to effectively guide the selection of scaffold in designing the cantilever scaffold, comprehensive analyses of three typical scaffold supporting systems (including fully cantilever, bottom-supporting cantilever and pull-up cantilever) are carried out. The calculation formulas of the internal force for the three scaffold supporting systems are proposed based on the theoretical analysis, which are effectively verified by the finite element method (FEM). In addition, the force mechanism and benefits of the three scaffold supporting systems are compared and analyzed combined with actual engineering. The results indicate that there is high calculating accuracy for the proposed internal force and deflection calculation formulas about the scaffold supporting systems. According to the distribution uniformity of the internal force and controlling of the deformation of the main girder, the bottom-supporting cantilever system is undoubtedly the best choice. While the pull-up cantilever supporting system is the best choice when considering the aspects of cost, construction period and social benefits, which ought to be popularized in engineering practice.

Force Analysis of Self-Anchored Suspension Bridges after Cable Clamp Slippage

The slippage of cable clamps during the long-term operation of suspension bridges is a common and detrimental phenomenon. From an experimental point of view, the cable clamp slippage of a suspension bridge was investigated to reveal the effect of this sliding on the force acting on the full bridge. The forces acting on the bridge before and after the slippage were analyzed using a finite element model. The calculation results showed that the cable clamp slippage directly affects the cable forces of the hangers. The hanger cable force decreased by 19.2% when the slippage reached 10.2 cm, while the maximum increase in the cable force of adjacent hangers was 147.7 kN, an increase of 7.25%. The variation of forces in the hanger cable disrupted the force balance of the main girder, thereby producing a torque effect at the corresponding position in the girder, i.e., increased torque. Meanwhile, the slippage affected the axial tension in the main cable and the main girder. The impact of the tower internal force was less than 1%. Hence, the study concluded that the effect of cable clamp slippage is better understood, ensuring the safety of the suspension bridge.

Analysis of Vehicle Collision on an Assembled Anti-Collision Guardrail

Traffic accidents such as vehicle collisions with bridge guardrails occur frequently. These accidents cause damage to the driver and the vehicle as well as the bridge. A new type of assembled anti-collision guardrail is proposed in this study. LS-DYNA is a nonlinear display dynamic analysis software used to evaluate the safety of a new type of assembled anti-collision guardrail. A specific, numerically analyzed model of vehicle–guardrail collision is established using LS-DYNA. The energy distribution–time curve of the vehicle collision process is obtained. After comparison with measured data from the vehicle collision test, the model of vehicle–guardrail collision is verified as being correct. Based on this, we analyze the process of a vehicle collision on the assembled anti-collision guardrail. The result shows that the assembled anti-collision guardrail proposed in this paper can better change the trajectory of a moving vehicle and can prevent the vehicle from falling off the bridge. From the car body collision results, the assembled anti-collision guardrail for bridges proposed in this paper can reduce vehicle damage and can protect the driver effectively. From the analysis of the main girder stress on the bridge, an anti-collision guardrail installed on an existing bridge will not cause damage to the main girder during a collision. In order to study the influence of the four parameters on the anti-collision effect, we carried out a comparative calculation of multiple working conditions. The results show that the new type of assembled anti-collision guardrail has good protective performance under different working conditions.