Multiscale Regional Liquefaction Hazard Assessment and Mapping

Soil liquefaction is a major cause of damage during earthquakes that could trigger many kinds of ground failures such as ground settlement, lateral spreading, land slides, etc. These ground failures could cause damage to infrastructures such as buildings, bridges, and lifelines resulting in significant economic losses. Therefore it is of significant importance to assess liquefaction hazard. The triggering and consequencing ground failure of liquefaction have been well investigated in the past decades. Nowadays, the dominant approach that correlates the observed field behavior with various in-situ œindex tests is able to achieve considerably precise assessments for free field conditions at site-specific scale. Regional scale assessments of liquefaction hazard, however, are still underdeveloped. Issues such as cross-geologic units correlations are still not systematically investigated in regional liquefaction assessment. Therefore, the main objective of this dissertation is to develop a solution framework for reliable regional assessment of earthquake-induced liquefaction hazard. Another objective is to validate this framework by applying it to several earthquake-prone regions so that liquefaction hazard maps of these regions could be added to the literature and guide designers, engineers and researchers. Moreover, the dominant method of estimating liquefaction damages via empirical correlations are not capable for complex site conditions. Therefore another objective of this dissertation is to study alternative approaches for general estimation of liquefaction damages. To achieve these objectives, a multiscale modeling framework for better estimate of regional liquefaction hazard with material randomness and heterogeneity is developed. One advantage the developed methodology is the extension of conventional random field models to account for soil spatial variability at multiple scales and resolutions. The method allows selectively and adaptively generating random fields at smaller scales around critical areas or around areas where soil properties are known to a great detail from lab or field tests. The process is defined such that spatial correlation is consistent across length scales. Illustrative examples (Marina District in San Francisco, Alameda County in California, and Christchurch in New Zealand) are presented. Liquefaction hazard is evaluated at multi-scale. Compared with single scale analyses, multi-scale random fields provide more detailed information and higher-resolution soil properties around critical areas. This framework provides a new way to consistently incorporating small-scale local liquefaction analysis into large-scale liquefaction assessment mapping. Furthermore, finite element method is identified as a prominent alternative to traditional approach for liquefaction estimation via empirical correlations. A dynamic FEM model is built upon which an effective stress analysis is performed to estimate liquefaction-induced soil deformation at site-specific scale. It is shown the developed finite element model as a numerical tool can be used in predicting cyclic liquefaction in soils. This research is expected to shed light on the complete understanding of soil liquefaction during earthquakes in hoping of saving economic losses in the future

Intelligent and Secure Underwater Acoustic Communication Networks

Underwater acoustic (UWA) communication networks are promising techniques for medium- to long-range wireless information transfer in aquatic applications. The harsh and dynamic water environment poses grand challenges to the design of UWA networks. This dissertation leverages the advances in machine learning and signal processing to develop intelligent and secure UWA communication networks. Three research topics are studied: 1) reinforcement learning (RL)-based adaptive transmission in UWA channels; 2) reinforcement learning-based adaptive trajectory planning for autonomous underwater vehicles (AUVs) in under-ice environments; 3) signal alignment to secure underwater coordinated multipoint (CoMP) transmissions. First, a RL-based algorithm is developed for adaptive transmission in long-term operating UWA point-to-point communication systems. The UWA channel dynamics are learned and exploited to trade off energy consumption with information delivery latency. The adaptive transmission problem is formulated as a partially observable Markov decision process (POMDP) which is solved by a Monte Carlo sampling-based approach, and an expectation-maximization-type of algorithm is developed to recursively estimate the channel model parameters. The experimental data processing reveals that the proposed algorithm achieves a good balance between energy efficiency and information delivery latency. Secondly, an online learning-based algorithm is developed for adaptive trajectory planning of multiple AUVs in under-ice environments to reconstruct a water parameter field of interest. The field knowledge is learned online to guide the trajectories of AUVs for collection of informative water parameter samples in the near future. The trajectory planning problem is formulated as a Markov decision process (MDP) which is solved by an actor-critic algorithm, where the field knowledge is estimated online using the Gaussian process regression. The simulation results show that the proposed algorithm achieves the performance close to a benchmark method that assumes perfect field knowledge. Thirdly, the dissertation presents a signal alignment method to secure underwater CoMP transmissions of geographically distributed antenna elements (DAEs) against eavesdropping. Exploiting the low sound speed in water and the spatial diversity of DAEs, the signal alignment method is developed such that useful signals will collide at the eavesdropper while stay collision-free at the legitimate user. The signal alignment mechanism is formulated as a mixed integer and nonlinear optimization problem which is solved through a combination of the simulated annealing method and the linear programming. Taking the orthogonal frequency-division multiplexing (OFDM) as the modulation technique, simulation and emulated experimental results demonstrate that the proposed method significantly degrades the eavesdropper\u27s interception capability

Interactions among Carbon Dioxide, Heat, and Chemical Lures in Attracting the Bed Bug, Cimex lectularius L. (Hemiptera: Cimicidae)

Commercial bed bug (Cimex lectularius L.) monitors incorporating carbon dioxide (CO2), heat, and chemical lures are being used for detecting bed bugs; however, there are few reported studies on the effectiveness of chemical lures in bed bug monitors and the interactions among chemical lure, CO2, and heat. We screened 12 chemicals for their attraction to bed bugs and evaluated interactions among chemical lures, CO2, and heat. The chemical lure mixture consisting of nonanal, 1-octen-3-ol, spearmint oil, and coriander Egyptian oil was found to be most attractive to bed bugs and significantly increased the trap catches in laboratory bioassays. Adding this chemical lure mixture when CO2 was present increased the trap catches compared with traps baited with CO2 alone, whereas adding heat did not significantly increase trap catches when CO2 was present. Results suggest a combination of chemical lure and CO2 is essential for designing effective bed bug monitors

Reinforcement learning-based multi-AUV adaptive trajectory planning for under-ice field estimation

This work studies online learning-based trajectory planning for multiple autonomous underwater vehicles (AUVs) to estimate a water parameter field of interest in the under-ice environment. A centralized system is considered, where several fixed access points on the ice layer are introduced as gateways for communications between the AUVs and a remote data fusion center. We model the water parameter field of interest as a Gaussian process with unknown hyper-parameters. The AUV trajectories for sampling are determined on an epoch-by-epoch basis. At the end of each epoch, the access points relay the observed field samples from all the AUVs to the fusion center, which computes the posterior distribution of the field based on the Gaussian process regression and estimates the field hyper-parameters. The optimal trajectories of all the AUVs in the next epoch are determined to maximize a long-term reward that is defined based on the field uncertainty reduction and the AUV mobility cost, subject to the kinematics constraint, the communication constraint and the sensing area constraint. We formulate the adaptive trajectory planning problem as a Markov decision process (MDP). A reinforcement learning-based online learning algorithm is designed to determine the optimal AUV trajectories in a constrained continuous space. Simulation results show that the proposed learning-based trajectory planning algorithm has performance similar to a benchmark method that assumes perfect knowledge of the field hyper-parameters

Synthesis and electrochemical properties of Sn-SnO2/C nanocomposite

A Sn-Sn02/C nanocomposite was synthesized using the electrospinning method. Thermal analysis was used to determine the content range of Sn and Sn02 in the composite. The composite was characterized by X-ray diffraction, and the particle size and shape in the Sn-SnOiC composite were determined by scanning and transmission electron microscopy. The results show that the Sn-Sn02/C composite takes on a nanofiber morphology, with the diameters of the nanofibers distributed from 50 to 200 nm. The electrOChemical properties of the Sn-SnOiC composite were also investigated. The Sn-SnOiC composite as an electrode material has both higher reversible capacity (887 mAh· g-I). and good cycling performance in lithium-anode ceUs working at room temperature in a 3.0 V to O.Ot V potential window. The Sn-Sn02/C composite could relain a discharge capacity of 546 mAWg aller 30 cycles. The outstanding electrochemical properties of the Sn-SnOiC composite oblained by this method make it possible for Ihis composite to be used as a promising anode material

Peec-Based On-Chip Pdn Impedance Modeling using Layered Green\u27s Function

This paper presents an impedance model of on-chip power distribution network (PDN), which is an efficient criterion for estimating simultaneous switching noises (SSNs) on 3-D integrated circuit (IC). The impedance of on-chip PDN, including the effect of silicon substrate, is accurately modeled based on partial element equivalent circuit (PEEC) and layered Green\u27s function (LGF). The equivalent circuit model of PDN is extracted based on the physical dimensions and electrical material characteristic of PDN at first. And then the LGF is used to consider the effect of silicon substrate for improving the accuracy of on-chip PDN impedance model. The effectiveness of proposed model has been validated by full wave simulation. The high order resonance of PDN impedance can also be accurately predicted