Development of an algorithm of bidirectional surface strain measurements from soft elastomeric capacitors

Once a structure comes into service, the most important concern is its ability to keep providing steady and safe service. Assessment of structural health is a tool that can be used to ensure such. However, most of the available evaluation strategies in structural health monitoring are labor intensive such as visual inspection, which is highly economically inefficient, especially for large scale structures. As a result, structural health monitoring (SHM) and smart sensor techniques are of great interest that have brought the attention of scientists recently. As an economic surface sensor for large scale structural surface, a soft elastomeric capacitor (SEC) was proposed in previous studies. However, the previous application of the SEC network was only able to monitor uniaxial strain filed due to the implicit directional strain measurement. To extend the applicability of the SEC sensor network into real-time biaxial strain field monitoring, an algorithm has been developed in this thesis based on a strain mapping approach using least square estimator. With the algorithm formulation based upon the classical plate theory, the biaxial sensing ability of the SEC sensor network with the proposed algorithm has been verified on both a rectangular cantilever plate under three types of load cases and an irregular laminated cantilever plate under simulated wind pressure. The proposed algorithm showed a computing speed around 0.1 s in a specific application which enables real-time monitoring and illustrated stability under noise level up to 5%. It can be concluded that the proposed algorithm possess the potential to be applied for wind turbine monitoring

Seismic Resilience-based Design and Optimization: A Deep Learning and Cyber-Physical Approach

With the growing acceptance and better understanding of the importance of uncertainties in seismic design, traditional design approaches with deterministic analysis are being replaced with more reliable approaches within a risk-based context. Recently, resilience has been increasingly studied as a comprehensive metric to assess the ability of a system to withstand and recover from disturbances with large uncertainties. For civil infrastructure systems susceptible to natural hazards, especially earthquakes as considered herein, seismic resilience could provide a measurement integrating both earthquake and post-earthquake performance. For structural engineers, improving infrastructure disaster resilience starts with the design of more resilient structures. This requires a quantitative approach to explicitly guild the design towards better resilience. However, when attempting to quantify the seismic resilience of a structure, large uncertainties lead to large computational costs associated with risk-based approaches. Additionally, the accuracy of numerical simulations under wide range of design scenarios is unknown. To address these challenges, this dissertation investigates the role of seismic resilience in structural design. This dissertation starts with a novel seismic protective device to improve structural resilience and follows with the development of a quantitative and efficient design, evaluation, and optimization framework for seismic resilience. This framework proposes metamodeling through deep neural networks for improved efficiency and cyber-physical systems for improved accuracy. Feedforward neural networks are adopted for fragility metamodeling, while online learning long-short term memory neural networks are developed for structural component metamodeling. Real-time hybrid simulation is used for the construction of cyber-physical systems. The proposed framework is demonstrated to have both improved accuracy and significantly reduced computational/experimental cost when compared to existing approaches. The applicability of the framework is illustrated through the optimization of structural systems for improved seismic resilience

Draft genome sequence of the mulberry tree Morus notabilis

Human utilization of the mulberry–silkworm interaction started at least 5,000 years ago and greatly influenced world history through the Silk Road. Complementing the silkworm genome sequence, here we describe the genome of a mulberry species Morus notabilis. In the 330-Mb genome assembly, we identify 128 Mb of repetitive sequences and 29,338 genes, 60.8% of which are supported by transcriptome sequencing. Mulberry gene sequences appear to evolve ~3 times faster than other Rosales, perhaps facilitating the species’ spread worldwide. The mulberry tree is among a few eudicots but several Rosales that have not preserved genome duplications in more than 100 million years; however, a neopolyploid series found in the mulberry tree and several others suggest that new duplications may confer benefits. Five predicted mulberry miRNAs are found in the haemolymph and silk glands of the silkworm, suggesting interactions at molecular levels in the plant–herbivore relationship. The identification and analyses of mulberry genes involved in diversifying selection, resistance and protease inhibitor expressed in the laticifers will accelerate the improvement of mulberry plants