Bridge Monitoring Strategies for Sustainable Development with Microwave Radar Interferometry

The potential of a coherent microwave radar for infrastructure health monitoring has been investigated over the past decade. Microwave radar measuring based on interferometry processing is a non-invasive technique that can measure the line-of-sight (LOS) displacements of large infrastructure with sub-millimeter precision and provide the corresponding frequency spectrum. It has the capability to estimate infrastructure vibration simultaneously and remotely with high accuracy and repeatability, which serves the long-term serviceability of bridge structures within the context of the long-term sustainability of civil engineering infrastructure management. In this paper, we present three types of microwave radar systems employed to monitor the displacement of bridges in Japan and Italy. A technique that fuses polarimetric analysis and the interferometry technique for bridge monitoring is proposed. Monitoring results achieved with full polarimetric real aperture radar (RAR), step-frequency continuous-wave (SFCW)-based linear synthetic aperture, and multi-input multi-output (MIMO) array sensors are also presented. The results reveal bridge dynamic responses under different loading conditions, including wind, vehicular traffic, and passing trains, and show that microwave sensor interferometry can be utilized to monitor the dynamics of bridge structures with unprecedented spatial and temporal resolution. This paper demonstrates that microwave sensor interferometry with efficient, cost-effective, and non-destructive properties is a serious contender to employment as a sustainable infrastructure monitoring technology serving the sustainable development agenda

Double Networks of Liquid-Crystalline Elastomers with Enhanced Mechanical Strength.

Liquid-crystalline elastomers (LCEs) are frequently used in soft actuator development. However, applications are limited because LCEs are prone to mechanical failure when subjected to heavy loads and high temperatures during the working cycle. A mechanically tough LCE system offers larger work capacity and lower failure rate for the actuators. Herein, we adopt the double-network strategy, starting with a siloxane-based exchangeable LCE and developing a series of double-network liquid-crystalline elastomers (DN-LCEs) that are mechanically tougher than the initial elastomer. We incorporate diacrylate reacting monomers to fabricate DN-LCEs, some of which have the breaking stress of 40 MPa. We incorporate thermoplastic polyurethane to fabricate a DN-LCE, achieving an enormous ductility of 90 MJ/m3. We have also attempted to utilize the aza-Michael chemistry to make a DN-LCE that retains high plasticity because of several bond-exchange mechanisms; however, it failed to produce a stable reprocessable LCE system using conventional ester-based reactive mesogens. Each of these DN-LCEs exhibits unique features and characteristics, which are compared and discussed.ERC H202

Amine‐Acrylate Liquid Single Crystal Elastomers Reinforced by Hydrogen Bonding

Abstract: Liquid crystalline elastomers (LCEs) have been considered one of the most promising material concepts for artificial muscles. However, accomplishing actuation of LCEs requires macroscopic alignment of the liquid‐crystalline orientation in the rubbery network, which imposes challenges in the materials chemistry and processing. A two‐stage curing strategy has been the dominating approach during last three decades. Despite its many successes, the method is difficult in practice and requires delicate experiential skills, dealing with intrinsic fragility of intermediate gels after the first crosslinking stage. Here, a robust fabrication method for monodomain LCE based on the amine‐acrylate aza‐Michael addition is developed, involving two readily commercially available components with no catalyst. The method is based on the large kinetic difference of hydrogen addition in primary amines to acrylates, which offers a sufficient gap separating two stages of curing and enabling versatile mechanical alignment techniques for manufacturing monodomain LCE in both liquid and gel states. Importantly, the mechanically robust network, helping processability at a partial‐crosslinking stage, is facilitated by the chemically generated hydrogen bonding all through the process, as a by‐product of hydrogen addition. Such a facile two‐component kit‐like fabrication should aid researchers from various fields in the search for a practical and reliable process of making soft actuators

Amine‐Acrylate Liquid Single Crystal Elastomers Reinforced by Hydrogen Bonding

Abstract: Liquid crystalline elastomers (LCEs) have been considered one of the most promising material concepts for artificial muscles. However, accomplishing actuation of LCEs requires macroscopic alignment of the liquid‐crystalline orientation in the rubbery network, which imposes challenges in the materials chemistry and processing. A two‐stage curing strategy has been the dominating approach during last three decades. Despite its many successes, the method is difficult in practice and requires delicate experiential skills, dealing with intrinsic fragility of intermediate gels after the first crosslinking stage. Here, a robust fabrication method for monodomain LCE based on the amine‐acrylate aza‐Michael addition is developed, involving two readily commercially available components with no catalyst. The method is based on the large kinetic difference of hydrogen addition in primary amines to acrylates, which offers a sufficient gap separating two stages of curing and enabling versatile mechanical alignment techniques for manufacturing monodomain LCE in both liquid and gel states. Importantly, the mechanically robust network, helping processability at a partial‐crosslinking stage, is facilitated by the chemically generated hydrogen bonding all through the process, as a by‐product of hydrogen addition. Such a facile two‐component kit‐like fabrication should aid researchers from various fields in the search for a practical and reliable process of making soft actuators

Inside Cover: Thermoset Shape-Memory Polyurethane with Intrinsic Plasticity Enabled by Transcarbamoylation (Angew. Chem. Int. Ed. 38/2016)

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137394/1/anie201606585.pd

Deep Unfolded Gridless DOA Estimation Networks Based on Atomic Norm Minimization

Deep unfolded networks have recently been regarded as an essential way to direction of arrival (DOA) estimation due to the fast convergence speed and high interpretability. However, few consider gridless DOA estimation. This paper proposes two deep unfolded gridless DOA estimation networks to resolve the above problem. We first consider the atomic norm-based 1D and decoupled atomic norm-based 2D gridless DOA models solved by the alternating iterative minimization of variables, respectively. Then, the corresponding deep networks are trained offline after constructing the corresponding complete training datasets. At last, the trained networks are applied to realize the 1D DOA and 2D estimation, respectively. Simulation results reveal that the proposed networks can secure higher 1D and 2D DOA estimation performances while maintaining a lower computational expenditure than typical methods

DU-CG-STAP Method Based on Sparse Recovery and Unsupervised Learning for Airborne Radar Clutter Suppression

With a small number of training range cells, sparse recovery (SR)-based space–time adaptive processing (STAP) methods can help to suppress clutter and detect targets effectively for airborne radar. However, SR algorithms usually have problems of high computational complexity and parameter-setting difficulties. More importantly, non-ideal factors in practice will lead to the degraded clutter suppression performance of SR-STAP methods. Based on the idea of deep unfolding (DU), a space–time two-dimensional (2D)-decoupled SR network, namely 2DMA-Net, is constructed in this paper to achieve a fast clutter spectrum estimation without complicated parameter tuning. For 2DMA-Net, without using labeled data, a self-supervised training method based on raw radar data is implemented. Then, to filter out the interferences caused by non-ideal factors, a cycle-consistent adversarial network (CycleGAN) is used as the image enhancement process for the clutter spectrum obtained using 2DMA-Net. For CycleGAN, an unsupervised training method based on unpaired data is implemented. Finally, 2DMA-Net and CycleGAN are cascaded to achieve a fast and accurate estimation of the clutter spectrum, resulting in the DU-CG-STAP method with unsupervised learning, as demonstrated in this paper. The simulation results show that, compared to existing typical SR-STAP methods, the proposed method can simultaneously improve clutter suppression performance and reduce computational complexity

A First-Principles Study of Mechanical and Electronic Properties of Cr_0.5-xAl_0.5TM_xN Hard Coatings (TM = Ti, V, Y, Zr, Hf, and Ta)

The structural, mechanical, and electronic properties of cubic Cr0.5-xAl0.5TMxN, doped with TM (transition metal) elements (TM = Ti, V, Y, Zr, Hf, and Ta) at low concentrations (x = 0.03 and 0.06), was investigated by first-principles calculations. The results of the structural properties calculations reveal that the addition of Ti, Y, Hf, Zr, and Ta expand the volume, while V has the opposite effect. All doped compounds are thermodynamically stable, and Cr0.5-xAl0.5TMxN with TM = Ti is energetically more favorable than other doped compounds. At the same doping concentration, Cr0.5-xAl0.5VxN possesses the highest stiffness, hardness, and resistance to external forces due to its greatest mechanical properties, and Cr0.5-xAl0.5TaxN possesses the highest elastic anisotropy and the lowest Young’s modulus. Substituting Cr atoms with TM atoms in a stepwise manner results in a decrease in the bulk modulus, shear modulus, Young’s modulus, and theoretical hardness of Cr0.5-xAl0.5TMxN, while increasing its toughness. Based on the calculation results of the total and partial density of states of Cr0.5Al0.5N and Cr0.47Al0.5TM0.03N, all compounds exhibit metallic behavior as indicated by the finite density of states at the Fermi level. The contribution of Ti-3d, V-3d, and Ta-3d orbitals at Fermi level is significantly higher than that of other TM atoms, resulting in a more pronounced metallic character for Cr0.47Al0.5Ti0.03N, Cr0.47Al0.5V0.03N, and Cr0.47Al0.5Ta0.03N

Inside Cover: Thermoset Shape‐Memory Polyurethane with Intrinsic Plasticity Enabled by Transcarbamoylation (Angew. Chem. Int. Ed. 38/2016)

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137394/1/anie201606585.pd

On demand shape memory polymer via light regulated topological defects in a dynamic covalent network

Stimuli-responsive soft materials are of interest for a range of applications. Here, the authors report on the use of light triggered catalysts to control topological defect driven isomerization of polymer networks and demonstrate application in shape memory polymers