A Spike-shaped Anchorage For Steel Reinforced Polymer (SRP)-strengthened Concrete Structures

Steel reinforced polymer (SRP) composite has recently emerged as an effective and economical solution for strengthening of reinforced concrete (RC) structures. Premature debonding failure of unanchored SRP at low load levels generally governs the performance of RC structures strengthened with externally bonded SRP. Therefore, a novel yet simple spike-shaped anchorage system was proposed in this study to prevent the debonding failure of SRP and to improve the interfacial shear capacity. Experimental investigation through single-lap shear tests of SRP-concrete joints showed that the anchorage system changed the failure mode from composite debonding to fiber rupture. In addition, the anchorage system substantially increased the peak load and reduced the interfacial slippage of the SRP-concrete joint compared to the unanchored condition. A numerical procedure based on the finite difference method was developed to predict the full-range load response, and results matched well with the full-range experimental responses of anchored and unanchored specimens. Parametric study of the test results and numerical simulation based on finite difference method both showed that the fiber rupture failure mode could be achieved for anchors in various positions along the bonded length. The closer the anchor is to the loaded end, the less global slip was obtained when the load reached the peak value

Deep Reinforcement Learning for Approximate Policy Iteration: Convergence Analysis and a Post-Earthquake Disaster Response Case Study

Approximate Policy Iteration (API) is a Class of Reinforcement Learning (RL) Algorithms that Seek to Solve the Long-Run Discounted Reward Markov Decision Process (MDP), Via the Policy Iteration Paradigm, Without Learning the Transition Model in the Underlying Bellman Equation. Unfortunately, These Algorithms Suffer from a Defect Known as Chattering in Which the Solution (Policy) Delivered in Each Iteration of the Algorithm Oscillates between Improved and Worsened Policies, Leading to Sub-Optimal Behavior. Two Causes for This that Have Been Traced to the Crucial Policy Improvement Step Are: (I) the Inaccuracies in the Policy Improvement Function and (Ii) the Exploration/exploitation Tradeoff Integral to This Step, Which Generates Variability in Performance. Both of These Defects Are Amplified by Simulation Noise. Deep RL Belongs to a Newer Class of Algorithms in Which the Resolution of the Learning Process is Refined Via Mechanisms Such as Experience Replay And/or Deep Neural Networks for Improved Performance. in This Paper, a New Deep Learning Approach is Developed for API Which Employs a More Accurate Policy Improvement Function, Via an Enhanced Resolution Bellman Equation, Thereby Reducing Chattering and Eliminating the Need for Exploration in the Policy Improvement Step. Versions of the New Algorithm for Both the Long-Run Discounted MDP and Semi-MDP Are Presented. Convergence Properties of the New Algorithm Are Studied Mathematically, and a Post-Earthquake Disaster Response Case Study is Employed to Demonstrate Numerically the Algorithm\u27s Efficacy

What is in a pebble shape?

We propose to characterize the shapes of flat pebbles in terms of the statistical distribution of curvatures measured along the pebble contour. This is demonstrated for the erosion of clay pebbles in a controlled laboratory apparatus. Photographs at various stages of erosion are analyzed, and compared with two models. We find that the curvature distribution complements the usual measurement of aspect ratio, and connects naturally to erosion processes that are typically faster at protruding regions of high curvature.Comment: Phys. Rev. Lett. (to appear

The shape and erosion of pebbles

The shapes of flat pebbles may be characterized in terms of the statistical distribution of curvatures measured along their contours. We illustrate this new method for clay pebbles eroded in a controlled laboratory apparatus, and also for naturally-occurring rip-up clasts formed and eroded in the Mont St.-Michel bay. We find that the curvature distribution allows finer discrimination than traditional measures of aspect ratios. Furthermore, it connects to the microscopic action of erosion processes that are typically faster at protruding regions of high curvature. We discuss in detail how the curvature may be reliable deduced from digital photographs.Comment: 10 pages, 11 figure

The Health Education Research Experience (HERE) program metadata dataset

Undergraduate subject pools are prevalent across disciplines in the United States. The Health Education Research Experience (HERE) Program was the first known course-based subject pool entirely managed and conducted online for online students enrolled in an introductory health education/health promotion course. The program was conducted within five semesters from Spring 2012 through Summer 2013. The HERE Program encompassed 13 studies embedded in two sections of an undergraduate online course at the University of Florida. The studies were all related to course topics and current research topics in health education/promotion (as identified through the Healthy People 2020 Framework). The topics ranged from the relatively less sensitive health aspects of college life (i.e., technology use) to studies assessing more sensitive health topics (i.e., intimate partner violence and sexual assault). In alignment with a best practice in survey design, the HERE Program's survey instruments included one metadata item embedded in each survey to identify which devices students used to complete the surveys. Understanding which devices students used for survey completion has ramifications for survey designers and survey researchers. In contrast to the relative uniformity of pen and paper surveys and control of the survey completion environment, online surveys may not look identical across personal devices and may be completed in increasingly varied environments. All studies, study procedures and protocols, and metadata collection procedures were approved by the university's Institutional Review Board. The data presented here were extracted from each survey's data files and aggregated. The aggregated metadata are available through Mendeley Data in a.csv file for widespread access. Descriptive statistics are presented in tables. The data provided in this article will benefit researchers interested in survey methodology, questionnaire design, modes of survey collection, and survey metadata. The data are hosted in the following Mendeley Data repository: https://data.mendeley.com/datasets/ht9jmd3cdt/2

The rural almshouse population in Missouri

Cooperative rural research."June, 1938."This is a study of the rural almshouse in Missouri. The various types of almshouse administration and methods of inmate care are defined and described, together with the social characteristics of the inmate population. As a matter of considerable contemporary interest an analysis was made of the eligibility of inmates for old age assistance in order to determine the extent to which counties might be relieved of their inmate population through this type of public assistance. Examination of data on nearly 3,000 inmates indicated that more than one-half (52.4 per cent) were under 70 years of age which is the present minimum age limit for old age assistance. It was further determined that at least one-half of the ones eligible due to age were not likely to be approved for old age assistance since they were either physically or mentally disabled. It was concluded that less than one-fourth of the total inmate population might qualify for old age assistance and that additional provisions would be necessary if the rural counties are to be relieved of the care of their almshouse population.By C.T. Pihlblad, Arthur W. Nebel, and Joseph H. Stokes, in collaboration with Melvin W. Sneed and Cecil L. Gregory.Cooperative Rural Research ... The Agricultural Experiment Station, University of Missouri; The Rural Section, Division of Social Research, Federal Works Progress Administration; and the State Social Security Commission of Missouri Cooperating.Introduction -- Administration and control -- Personnel and management -- Almshouses and almshouse inmates -- Eligibility of inmates for old age assistance -- Consolidation of almshouses -- Suggested recommendations -- Appendix

Money Minute: Using short informational videos during COVID-19

The COVID-19 pandemic has created a money crunch for some families. To help families struggling financially while capitalizing on at-home time, The University of Tennessee (UT) Extension consumer economics leadership team developed a series of money management videos called Money Minute. The primary purpose of the videos was to provide research-based financial education during this time of financial hardships. Filmed using Zoom, each video offers a piece of research-based information, additional resources, and a call to action. The video series proved to be effective in reaching clientele with financial information in the midst of a pandemic

Condition monitoring of helical gears using automated selection of features and sensors

The selection of most sensitive sensors and signal processing methods is essential process for the design of condition monitoring and intelligent fault diagnosis and prognostic systems. Normally, sensory data includes high level of noise and irrelevant or red undant information which makes the selection of the most sensitive sensor and signal processing method a difficult task. This paper introduces a new application of the Automated Sensor and Signal Processing Approach (ASPS), for the design of condition monitoring systems for developing an effective monitoring system for gearbox fault diagnosis. The approach is based on using Taguchi's orthogonal arrays, combined with automated selection of sensory characteristic features, to provide economically effective and optimal selection of sensors and signal processing methods with reduced experimental work. Multi-sensory signals such as acoustic emission, vibration, speed and torque are collected from the gearbox test rig under different health and operating conditions. Time and frequency domain signal processing methods are utilised to assess the suggested approach. The experiments investigate a single stage gearbox system with three level of damage in a helical gear to evaluate the proposed approach. Two different classification models are employed using neural networks to evaluate the methodology. The results have shown that the suggested approach can be applied to the design of condition monitoring systems of gearbox monitoring without the need for implementing pattern recognition tools during the design phase; where the pattern recognition can be implemented as part of decision making for diagnostics. The suggested system has a wide range of applications including industrial machinery as well as wind turbines for renewable energy applications