Dynamic Axle Force and Road Profile Identification Using a Moving Vehicle

In the interaction between vehicles, pavements and bridges, it is essential to aim towards a reduction of vehicle axle forces to promote longer pavement life spans and to prevent bridges loads becoming too high. Moreover, as the road surface roughness affects the vehicle dynamic forces, an efficient monitoring of pavement condition is also necessary to achieve this aim. This paper uses a novel algorithm to identify the dynamic interaction forces and pavement roughness from vehicle accelerations in both theoretical simulations and a laboratory experiment; moving force identification theory is applied to a vehicle model for this purpose. Theoretical simulations are employed to evaluate the ability of the algorithm to predict forces over a range of bridge spans and to evaluate the influence of road roughness level on the accuracy of the results. Finally, in addressing the challenge for the real-world problem, the effects of vehicle configuration and speed on the predicted road roughness are also investigated in a laboratory experiment.

Theoretical investigation of the use of a moving vehicle to identify bridge dynamic parameters

Pavements and bridges are subject to a continuous degradation due to traffic aggressiveness, ageing and environmental factors. A rational transport policy requires the monitoring of this transport infrastructure in order to provide adequate maintenance and guarantee the required levels of transport service and safety. This paper investigates the use of an instrumented vehicle fitted with accelerometers on its axles to monitor the dynamics of bridges. A simplified quarter carbridge interaction model is used in theoretical simulations and the natural frequency of the bridge is extracted from the spectra of the vehicle accelerations. The accuracy is better at lower speeds and for smooth road profiles. The structural damping of the bridge was also monitored for smooth and rough road profiles. The magnitude of peaks in the power spectral density of the vehicle accelerations decreased with increasing bridge damping and this decrease was easier to detect the smoother the road profile.

Inhibitory Effect of Phosphonium-Based Ionic Liquids on Coal Oxidation

To develop a chemical inhibitor that can efficiently suppress coal oxidation, nine tetraalkylphosphonium-based ionic liquids (ILs) and one imidazolium-based IL [1-allyl-3-methylimidazolium chloride ([AMIm]Cl)] were examined as additives. These ILs were used to treat and investigate the inhibitory effect on the oxidation activity and the structure of lignite coal. Characterization using thermogravimetric analysis showed that phosphonium-based ILs are able to inhibit coal oxidation up to 400 degrees C with the tributylethylphosphonium diethylphosphate ([P-4,P-4,P-4,P-2][DEP]) found to be the most effective. In contrast to the tetraalkylphosphonium-based ILs, inhibition using [AMIm]Cl was only found to be effective at temperatures below 250 degrees C, indicating that the tetraallcylphosphonium-based ILs may be more suitable for the future application of suppressing coal spontaneous combustion over a wide range of temperatures. Fourier transform infrared spectroscopic data showed that the various functional groups change in the coal following IL treatment, which are a decrease in the minerals and hydrogen bonds in all treated coals, while decreased aliphatic hydrocarbon and increased carbonyl bonds only appeared in some samples. During the oxidation of coal, the decomposition of aliphatic hydrocarbon groups is inhibited and the formation of carbonyl groups is delayed, so that the evolved gas concentration decreased, as shown by the temperature-programmed oxidation-mass spectrometry results. The deployment of the [P-4,P-4,P-4,P-2][ DEP] and tributylmethylphosphonium methylsulfate Its as additives also show good inhibitory effect on coal oxidation over the temperature range studied, and a relatively stronger interaction between [P-4,P-4,P-4,P-2] [DEP] and coal is demonstrated by the additive model.

Shallow water methane-derived authigenic carbonate mounds at the Codling Fault Zone, western Irish Sea

Methane-derived authigenic carbonate (MDAC) mound features at the Codling Fault Zone (CFZ), located in shallow waters (50-120m) of the western Irish Sea were investigated and provide a comparison to deep sea MDAC settings. Carbonates consisted of aragonite as the major mineral phase, with δ¹³C depletion to -50‰ and δ¹⁸O enrichment to~2‰. These isotope signatures, together with the co-precipitation of framboidal pyrite confirm that anaerobic oxidation of methane (AOM) is an important process mediating methane release to the water column and the atmosphere in this region. ¹⁸O-enrichment could be a result of MDAC precipitation with seawater in colder than present day conditions, or precipitation with ¹⁸O-enriched water transported from deep petroleum sources. The ¹³C depletion of bulk carbonate and sampled gas (-70‰) suggests a biogenic source, but significant mixing of thermogenic gas and depletion of the original isotope signature cannot be ruled out. Active seepage was recorded from one mound and together with extensive areas of reduced sediment, confirms that seepage is ongoing. The mounds appear to be composed of stacked pavements that are largely covered by sand and extensively eroded. The CFZ mounds are colonized by abundant Sabellaria polychaetes and possible Nemertesia hydroids, which benefit indirectly from available hard substrate. In contrast to deep sea MDAC settings where seep-related macrofauna are commonly reported, seep-specialist fauna appear to be lacking at the CFZ. In addition, unlike MDAC in deep waters where organic carbon input from photosynthesis is limited, lipid biomarkers and isotope signatures related to marine planktonic production (e.g. sterols, alkanols) were most abundant. Evidence for microbes involved in AOM was limited from samples taken; possibly due to this dilution effect from organic matter derived from the photic zone, and will require further investigation. 

Dynamic axle force and road profile identification using a moving vehicle

The axle forces applied by a vehicle through its wheels are a critical part of the interaction between vehicles, pavements and bridges. Therefore, the minimisation of these forces is important in order to promote long pavement life spans and ensure that bridge loads are small. Moreover, as the road surface roughness affects the vehicle dynamic forces, the monitoring of pavements for highways and bridges is an important task. This paper presents a novel algorithm to identify these dynamic interaction forces which involves direct instrumentation of a vehicle with accelerometers. The ability of this approach to predict the pavement roughness is also presented. Moving force identification theory is applied to a vehicle model in theoretical simulations in order to obtain the interaction forces and pavement roughness from the measured accelerations. The method is tested for a range of bridge spans in simulations and the influence of road roughness level on the accuracy of the results is investigated. Finally, the challenge for the real-world problem is addressed in a laboratory experiment.