Nondestructive field assessment of flexible pavement and foundation layers

Falling weight deflectometer (FWD) and ground penetrating radar (GPR) are nondestructive test devices widely used by transportation agencies to assess pavement conditions. The two papers in this thesis evaluated the uncertainties associated with interpreting data from these devices and assessed potential applications. In the first paper, FWD tests were conducted on asphalt pavements with varying supporting conditions, and individual layer modulus values were estimated using forward-and back-calculation methods. Dynamic cone penetrometer (DCP) test device was used to independently measure individual layer penetration resistance (PR) values to compare with the estimate moduli values. Results indicated that the predicted subgrade moduli values from forward- and back-calculations are strongly correlated but produce slightly different values. The predicted asphalt and base layer moduli values from forward- and back-calculations, however, showed significant scatter. Comparison between DCP-PR and the predicted base and subgrade layer modulus yielded non-linear relationships. The relationships produced lower standard errors when only data from subgrade layer is considered. The relationships developed in this study fell within the upper and lower bounds of relationships documented in the literature. In the second paper, the efficacy of using a ground-coupled GPR system and a hand-held dielectric property measurement device to determine the asphalt and pavement foundation layer thicknesses is assessed. The actual pavement thicknesses were measured from pavement cores and foundation layer thicknesses were obtained using dynamic cone penetrometer (DCP) tests. Further, the viability of using GPR to detect moisture variations in the base layers is assessed. Tests were conducted on various asphalt pavement test sections built at a test site in Iowa with different foundation support and drainage conditions, and layer thicknesses. A comparative analysis of core measurements and asphalt thickness estimated from GPR showed a 10% average error. Base layer thicknesses could not be evaluated using GPR data due to variations in moisture contents. Based on the dielectric properties calculated from GPR scans, the estimated moisture contents in the base layer varied from about 5 to 15%. The variations in moisture contents between the test sections are attributed to variations in gradation and permeability properties of the base layer

Finding Global Optimum for Truth Discovery: Entropy Based Geometric Variance

Truth Discovery is an important problem arising in data analytics related fields such as data mining, database, and big data. It concerns about finding the most trustworthy information from a dataset acquired from a number of unreliable sources. Due to its importance, the problem has been extensively studied in recent years and a number techniques have already been proposed. However, all of them are of heuristic nature and do not have any quality guarantee. In this paper, we formulate the problem as a high dimensional geometric optimization problem, called Entropy based Geometric Variance. Relying on a number of novel geometric techniques (such as Log-Partition and Modified Simplex Lemma), we further discover new insights to this problem. We show, for the first time, that the truth discovery problem can be solved with guaranteed quality of solution. Particularly, we show that it is possible to achieve a (1+eps)-approximation within nearly linear time under some reasonable assumptions. We expect that our algorithm will be useful for other data related applications

Deep Neural Newsvendor

We consider a data-driven newsvendor problem, where one has access to past demand data and the associated feature information. We solve the problem by estimating the target quantile function using a deep neural network (DNN). The remarkable representational power of DNN allows our framework to incorporate or approximate various extant data-driven models. We provide theoretical guarantees in terms of excess risk bounds for the DNN solution characterized by the network structure and sample size in a non-asymptotic manner, which justify the applicability of DNNs in the relevant contexts. Specifically, the convergence rate of the excess risk bound with respect to the sample size increases in the smoothness of the target quantile function but decreases in the dimension of feature variables. This rate can be further accelerated when the target function possesses a composite structure. Compared to other typical models, the nonparametric DNN method can effectively avoid or significantly reduce the model misspecification error. In particular, our theoretical framework can be extended to accommodate the data-dependent scenarios, where the data-generating process is time-dependent but not necessarily identical over time. Finally, we apply the DNN method to a real-world dataset obtained from a food supermarket. Our numerical experiments demonstrate that (1) the DNN method consistently outperforms other alternatives across a wide range of cost parameters, and (2) it also exhibits good performance when the sample size is either very large or relatively limited

Prox-DBRO-VR: A Unified Analysis on Decentralized Byzantine-Resilient Composite Stochastic Optimization with Variance Reduction and Non-Asymptotic Convergence Rates

Decentralized Byzantine-resilient stochastic gradient algorithms resolve efficiently large-scale optimization problems in adverse conditions, such as malfunctioning agents, software bugs, and cyber attacks. This paper targets on handling a class of generic composite optimization problems over multi-agent cyberphysical systems (CPSs), with the existence of an unknown number of Byzantine agents. Based on the proximal mapping method, two variance-reduced (VR) techniques, and a norm-penalized approximation strategy, we propose a decentralized Byzantine-resilient and proximal-gradient algorithmic framework, dubbed Prox-DBRO-VR, which achieves an optimization and control goal using only local computations and communications. To reduce asymptotically the variance generated by evaluating the noisy stochastic gradients, we incorporate two localized variance-reduced techniques (SAGA and LSVRG) into Prox-DBRO-VR, to design Prox-DBRO-SAGA and Prox-DBRO-LSVRG. Via analyzing the contraction relationships among the gradient-learning error, robust consensus condition, and optimal gap, the theoretical result demonstrates that both Prox-DBRO-SAGA and Prox-DBRO-LSVRG, with a well-designed constant (resp., decaying) step-size, converge linearly (resp., sub-linearly) inside an error ball around the optimal solution to the optimization problem under standard assumptions. The trade-offs between the convergence accuracy and the number of Byzantine agents in both linear and sub-linear cases are characterized. In simulation, the effectiveness and practicability of the proposed algorithms are manifested via resolving a sparse machine-learning problem over multi-agent CPSs under various Byzantine attacks.Comment: 14 pages, 0 figure

Distributed and Robust Support Vector Machine

In this paper, we consider the distributed version of Support Vector Machine (SVM) under the coordinator model, where all input data (i.e., points in R^d space) of SVM are arbitrarily distributed among k nodes in some network with a coordinator which can communicate with all nodes. We investigate two variants of this problem, with and without outliers. For distributed SVM without outliers, we prove a lower bound on the communication complexity and give a distributed (1-epsilon)-approximation algorithm to reach this lower bound, where epsilon is a user specified small constant. For distributed SVM with outliers, we present a (1-epsilon)-approximation algorithm to explicitly remove the influence of outliers. Our algorithm is based on a deterministic distributed top t selection algorithm with communication complexity of O(k log (t)) in the coordinator model. Experimental results on benchmark datasets confirm the theoretical guarantees of our algorithms

The Small-Molecule TrkB Agonist 7, 8-Dihydroxyflavone Decreases Hippocampal Newborn Neuron Death After Traumatic Brain Injury

Previous studies in rodents have shown that after a moderate traumatic brain injury (TBI) with a controlled cortical impact (CCI) device, the adult-born immature granular neurons in the dentate gyrus are the most vulnerable cell type in the hippocampus. There is no effective approach for preventing immature neuron death after TBI. We found that tyrosine-related kinase B (TrkB), a receptor of brain-derived neurotrophic factor (BDNF), is highly expressed in adult-born immature neurons. We determined that the small molecule imitating BDNF, 7, 8-dihydroxyflavone (DHF), increased phosphorylation of TrkB in immature neurons both in vitro and in vivo. Pretreatment with DHF protected immature neurons from excitotoxicity-mediated death in vitro, and systemic administration of DHF before moderate CCI injury reduced the death of adult-born immature neurons in the hippocampus 24 hours after injury. By contrast, inhibiting BDNF signaling using the TrkB antagonist ANA12 attenuated the neuroprotective effects of DHF. These data indicate that DHF may be a promising chemical compound that promotes immature neuron survival after TBI through activation of the BDNF signaling pathway