Peptoids (N-substituted oligoglycines) are biomimetic polymers that can fold into a variety of unique structural scaffolds. Peptoid helices, which result from the incorporation of bulky chiral side chains, are a key peptoid structural motif whose formation has not yet been accurately modeled in molecular simulations. Here, we report that a simple modification of the backbone φ-angle potential in GAFF is able to produce well-folded cis-amide helices of (S)-N-(1-phenylethyl)glycine (Nspe), consistent with experiment. We validate our results against both QM calculations and NMR experiments. For this latter task, we make quantitative comparisons to sparse NOE data using the Bayesian Inference of Conformational Populations (BICePs) algorithm, a method we have recently developed for this purpose. We then performed extensive REMD simulations of Nspe oligomers as a function of chain length and temperature to probe the molecular forces driving cooperative helix formation. Analysis of simulation data by Lifson-Roig helix-coil theory show that the modified potential predicts much more cooperative folding for Nspe helices. Unlike peptides, per-residue entropy changes for helix nucleation and extension are mostly positive, suggesting that steric bulk provides the main driving force for folding. We expect these results to inform future work aimed at predicting and designing peptoid peptidomimetics and tertiary assemblies of peptoid helices.

In this study, we further develop the processing of ground-based interferometric radar measurements for the application of bridge monitoring. Applying ground-based radar in such complex setups or long measurement durations requires advanced processing steps to receive accurate measurements. These steps involve removing external influences from the measurement and evaluating the measurement uncertainty during processing. External influences include disturbances caused by objects moving through the signal, static clutter from additional scatterers, and changes in atmospheric properties. After removing these influences, the line-of-sight displacement vectors, measured by multiple ground-based radars, are decomposed into three-dimensional displacement components. The advanced processing steps are applied exemplarily on measurements with two sensors at a prestressed concrete bridge near Coburg (Germany). The external influences are successfully removed, and two components of the three-dimensional displacement vector are determined. A measurement uncertainty of less than 0.1mm is achieved for the discussed application.

In this paper, we introduce a non-invasive approach for monitoring bridge infrastructure with ground-based interferometric radar. This approach is called the mirror mode, since it utilises the flat surface of the bridge underside as a mirror to reflect the signal to a corner reflector on the ground placed opposite of the radar sensor. For proving the feasibility of this approach, a measurement campaign has been carried out at an exemplary bridge in Karlsruhe (Germany) including a radar sensor in mirror mode, a second radar sensor in the default mode and a laser profile scanner. We investigate the potential of this approach to monitor the bridge displacement in vertical direction and compare the results with the two other sensors. The derived results reveal the potential for monitoring bridge infrastructure. Finally, we propose further research aspects of this approach to analyse its capabilities and limitation in the context of non-invasive infrastructure monitoring.

Assessing the condition of bridge infrastructure requires estimating damage-sensitive features from reliable sensor data. This study proposes to estimate natural frequencies from displacement measurements of a ground-based interferometric radar (GBR). These frequencies are determined from the damped vibration after each vehicle crossing with least squares and compared to a Frequency Domain Decomposition result. We successfully applied the approach in an exemplary measurement campaign at a bridge near Coburg (Germany) with an additional comparison to commonly used strain sensors. Since temperature greatly influences natural frequencies, linear regression is used to correct this influence. A simulation shows that GBR, combined with the least squares approach, achieves the lowest uncertainty and variation in the linear regression, indicating better damage detection results. However, the success of the damage detection highly depends on correctly determining the temperature influence, which might vary throughout the structure. Future work should further investigate the biases and variability of this influence.

Detection of surface deformation can be measured using InSar, a geodetic technique that calculates the interference pattern which results from a difference in phase between images acquired by synthetic aperture radar (SAR). Ground surface deformation, in particular land subsidence is caused by numerous factors, such as: tectonic motion, sediment compaction, thawing permafrost, increased surface loading, Glacial Isostatic Adjustment (GIA), hydro-chemical erosion of karst, decomposition of organic material in soils, mining, anthropogenic fluid withdrawal and surface water/drainage management. Groundwater applications have commonly been completed in areas of extreme subsidence due to dehydration and collapse of fine grained sediment textures; such as in the central valley of California, Nevada, and the Mexico city area. This study examines whether there is potential application of this technique to measure changes in surface elevation in southern Ontario and whether it can be related to changes in groundwater storage. For the southern Ontario study five datasets were used to assess the ground surface deformation and the hydrogeological/hydrologic conditions within the imagery extent. Datasets included: a set of 40 Radarsat-2 images spanning five years, GPS weekly solutions, Real-time kinematic (RTK) gps data, groundwater levels, terrestrial water storage data derived from GRACE satellites and hydrologic data from the Provincial Groundwater Monitoring Network (PGMN). Differential synthetic aperture radar interferometry (D-InSAR) has sub-centimetre precision and high spatial resolution over a large area. To eliminate some of the noise and to reduce geometrical distortions (multilook) images were averaged to a 50 m resolution. A stable reference site RTK TWOO was used a reference point. The TWOO GPS measurements were then added to the InSAR time series deformation maps. Obtaining a coherent signal was difficult and resulted in clustering of signal return from urbanized areas. The roofs and corners of the buildings in urbanized areas can form permanent scatterers, resulting in a more coherent signal. Across the study area, an annual rate of 1 mm to 10 mm of subsidence is observed. The greatest amount of subsidence (> 8 mm/year) is observed along the shore of Lake Ontario. In the absence of a field campaign to support validation the study has concluded that any viable signal attributed to specific geological - hydrogeological controls is within the signal-noise ratio of the study.