Process Monitoring and Terminal Verification of Cable-Stayed Bridges with Corrugated Steel Webs under Contruction

In this paper,the construction process of a cable-stayed bridge with corrugated steel webs was monitored.Moreover,the end performance of the bridge was verified by load test.Owing to the consideration of the bridge structure safety,it is necessary to monitor the main girder deflection,stress,construction error and safety state during construction.Furthermore,to verify whether the bridge can meet the design requirements,the static and dynamic load tests are carried out after the completion of the bridge.The results of construction monitoring show that the stress state of the structure during construction is basically consistent with the theoretical calculation and design requirements,and both meet the design and specification requirements.The final measured stress state of the structure is within the allowable range of the cable-stayed bridge,and the stress state of the structure is normal and meets the specification requirements.The results of load tests show that the measured deflection values of the mid-span section of the main girder are less than the theoretical calculation values.The maximum deflection of the girder is−20.90 mm,which is less than−22.00 mm of the theoretical value,indicating that the girder has sufficient structural stiffness.The maximum impact coefficient under dynamic load test is 1.08,which is greater than 1.05 of theoretical value,indicating that the impact effect of heavy-duty truck on this type of bridge is larger.This study can provide important reference value for construction and maintenance of similar corrugated steel web cable-stayed bridges.

Research on the Dynamic Performance of Low-Yield-Strength Shear Panel Damper for Bridge Application

This study seeks to develop a scientific understanding of the effects of dynamic loading history on the low-yield-strength shear panel damper （LYSPD） mechanical properties and fatigue performance. The LYSPD model is supposed as perfect elastic-plastic without strength deterioration. By adjusting the damper strength with the above supposition in the DYMO software, the serial earthquake response waves of the LYSPD under different soil conditions are obtained firstly. Subsequently, several waves, the most prone to damage at each soil condition. are selected as dynamic loading waves to verify the LYSPD dynamic performances experimentally. The test re- sults suggest that the dynamic loading histories have no influence on the LYSPD strength or the supposed model while they affect the fatigue performance. The applicability and accuracy of three fatigue evaluation metbods, cumulative plastic shear strain, cumulative energy absorption and Miner rule, are also compared according t,o the test results.

Machine learning based fault detection and state of health estimation of proton exchange membrane fuel cells

In fuel cells, chemical energy is directly converted into heat and electricity without any emissions which makes them an attractive substitute for various energy needs. Fuel cells have high energy conversion ratio and highpower densities which make them suitable for automotive applications. However, these fuel cell systems suffer with low reliability and durability as system components develop faults during operation resulting in degradation and diminished system performance. In this context, fault detection and fault mitigation strategies are being extensively developed. Diagnostic approaches like electrochemical impedance spectroscopy, cyclic voltammetry, and galvanostatic analysis offer a truthful representation of the State of Health (SOH) of the fuel cell. However, these approaches are intrusive and require pausing the operation of the fuel cell effecting its integrity. Machine learning based fault detection and SOH estimation is a non-intrusive approach where a mapping function is established between the indicators and SOH. The SOH of a fuel cell can be correlated to the patterns in sensor signals or indicators. Indicators that influence SOH are cell voltages, current density distribution, impedance spectra, acoustic emission and magnetic fields. Developing an accurate fault detection and state estimation technique through data driven machine learning approaches will allow corrective measures to avoid irreversible faults and improve the reliability and durability of fuel cells.