Water, Climate, and Social Change in a Fragile Landscape

We present here and in the companion papers an analysis of sustainability in the Middle Rio Grande region of the U.S.-Mexico border and propose an interdisciplinary research agenda focused on the coupled human and natural dimensions of water resources sustainability in the face of climate and social change in an international border region. Key threats to water sustainability in the Middle Rio Grande River region include: (1) increasing salinization of surface and ground water, (2) increasing water demand from a growing population in the El Paso/Ciudad Juarez area on top of an already high base demand from irrigated agriculture, (3) water quality impacts from agricultural, municipal, and industrial discharges to the river, (4) changing regional climate that portends increased frequency and intensity of droughts interspersed with more intensive rainfall and flooding events, and (5) disparate water planning and management systems between different states in the U.S. and between the U.S. and Mexico. In addition to these challenges, there is an increasing demand from a significant regional population who is (and has been historically) underserved in terms of access to affordable potable water. To address these challenges to water resources sustainability, we have focused on: (1) the determinants of resilience and transformability in an ecological/social setting on an international border and how they can be measured and predicted; and (2) the drivers of change ... what are they (climate, social, etc.) and how are they impacting the coupled human and natural dimensions of water sustainability on the border? To tackle these challenges, we propose a research agenda based on a complex systems approach that focuses on the linkages and feedbacks of the natural, built/managed, and social dimensions of the surface and groundwater budget of the region. The approach that we propose incorporates elements of systems analysis, complexity science, and the use of modeling tools such as scenario planning and back-casting to link the quantitative with the qualitative. This approach is unique for our region, as are our bi-national focus and our conceptualization of water capital . In particular, the concept of water capital provides the basis for a new interdisciplinary paradigm that integrates social, economic, and natural sectors within a systems framework in order to understand and characterize water resources sustainability. This proposed approach would not only provide a framework for water sustainability decision making for our bi-national region at the local, state, and federal levels, but could serve as a model for similar border regions and/or international rivers in arid and semi-arid regions in the Middle East, Africa, Asia, and Latin America

Statistical validation of physical system models

It is common practice in applied mechanics to develop mathematical models for mechanical system behavior. Frequently, the actual physical system being modeled is also available for testing, and sometimes the test data are used to help identify the parameters of the mathematical model. However, no general-purpose technique exists for formally, statistically judging the quality of a model. This paper suggests a formal statistical procedure for the validation of mathematical models of physical systems when data taken during operation of the physical system are available. The statistical validation procedure is based on the bootstrap, and it seeks to build a framework where a statistical test of hypothesis can be run to determine whether or not a mathematical model is an acceptable model of a physical system with regard to user-specified measures of system behavior. The approach to model validation developed in this study uses experimental data to estimate the marginal and joint confidence intervals of statistics of interest of the physical system. These same measures of behavior are estimated for the mathematical model. The statistics of interest from the mathematical model are located relative to the confidence intervals for the statistics obtained from the experimental data. These relative locations are used to judge the accuracy of the mathematical model. A numerical example is presented to demonstrate the application of the technique

Reducing Over-Conservative Expert Failure Rate Estimates in the Presence of Limited Data: A New Probabilistic/Fuzzy Approach

Abstract—Unique highly reliable components are typical for aerospace industry. For such components, due to their high reliability and uniqueness, we do not have enough empirical data to make statistically reliable estimates about their failure rate. To overcome this limitation, the empirical data is usually supplemented with expert estimates for the failure rate. The problem is that experts tend to be – especially in aerospace industry – over-cautious, over-conservative; their estimates for the failure rate are usually much higher than the actual observed failure rate. In this paper, we provide a new fuzzy-related statistically justified approach for reducing this over-estimation. I. FORMULATION OF THE PROBLEM Reliability: how it is usually described and evaluated. Failures are ubiquitous. As a result, reliability analysis is an important part of engineering design

Artificial Neural Networks for Structural Damage Detection and Classification

An analysis of artificial neural networks on damage assessment of an aluminum cantilever beam was conducted. The neural networks were trained and tested with deterministic data of resonant frequency information to test their ability in determining the magnitude, location and type of damage on the beam. Being a preliminary study, no experimental data has been included, since no information was found in the literature where neural networks were used in determining the type of damage on a structure. This paper includes a discussion on the theory of neural network and the process involved in developing the architecture for three layer backpropagation neural networks for damage assessment. The neural networks were tested for three types of damage using four damage magnitudes

Economics of Engineering Design under Interval (and Fuzzy) Uncertainty: Case Study of Building Design

One of the main objectives of engineering design is to find a design that is the cheapest among all designs that satisfy given constraints. Most of the constraints must be satisfied under all possible values within certain ranges. Checking all possible combinations of values is often very time-consuming. In this paper, we propose a faster algorithm for checking such constraints

Fuzzy (Granular) Levels of Quality, With Applications to Data Mining and to Structural Integrity of Aerospace Structures

Experts usually describe quality by using words from natural language such as "perfect", "good", etc. In this paper, we deduce natural numerical values corresponding to these words, and show that these values explain empirical dependencies uncovered in data mining and in the analysis of structural integrity of aerospace structures. 1. Formulation of the Problem In mathematical descriptions, quality is characterized by a numerical value of an appropriately chosen objective function. In real-life, however, to describe quality, we use words such as "perfect", "good", etc. We therefore need to relate numerical values with words describing quality. ffl If we already have a numerical value, then fuzzy logic provides us with a reasonable technique for translating this numerical value into words. ffl Often, we face the opposite problem: we have an expert's estimate of quality in terms of words, and we must translate this estimate into numbers so that we will be able to combine this qualit..

Damage Localization in a Space Truss Model Using Modal Strain Energy Distribution

This paper summarizes the experimental program, the modal test results, the extracted modal shapes and an analysis implemented to localize the damage via modal strain energy distribution. The modal test results consist of the vibrational signatures due to different damages on the structure. The extracted modal results are implemented in a global damage detection/evaluation theory bared on modal swain energy distribution. The strain energy distributions due to the static shapes of the modes are computed before and after the inflicted damage. The differences from the normalized strain energy distributions are multiplied timer element weight factors proportional to the strain energy distribution of the damaged structure. The weighted modal strain energy differences are lumped into the connecting nodes of the elements to provide indications of the locution of the inflicted damage. The technique is able to locate the complete damage and the partial damage, but was no, able to detect the GUI damage

With Applications to Data Mining and to Structural Integrity of Aerospace Structures

Experts usually describe quality by using words from natural language such as “perfect”, “good”, etc. In this paper, we deduce natural numerical values corresponding to these words, and show that these values explain empirical dependencies uncovered in data mining and in the analysis of structural integrity of aerospace structures. 1. Formulation of the Problem In mathematical descriptions, quality is characterized by a numerical value of an appropriately chosen objective function. In real-life, however, to describe quality, we use words such as “perfect”, “good”, etc. We therefore need to relate numerical values with words describing quality. If we already have a numerical value, then fuzzy logic provides us with a reasonable technique for translating this numerical value into words. Often, we face the opposite problem: we have an expert’s estimate of quality in terms of words, and we must translate this estimate into numbers so that we will be able to combine this quality information with other knowledge which is already given in numerical form

CHAPTER 10 Aircraft Integrity and Reliability

Abstract. In his recent paper “Probability theory needs an infusion of fuzzy logic to enhance its ability to deal with real-world problems”, L. A. Zadeh explains that probability theory needs an infusion of fuzzy logic to enhance its ability to deal with real-world problems. In this chapter, we give an example of a real-world problem for which such an infusion is indeed successful: the problems of aircraft integrity and reliability

Aircraft Integrity and Reliability

. In his recent paper "Probability theory needs an infusion of fuzzy logic to enhance its ability to deal with real-world problems", L. A. Zadeh explains that probability theory needs an infusion of fuzzy logic to enhance its ability to deal with real-world problems. In this chapter, we give an example of a real-world problem for which such an infusion is indeed successful: the problems of aircraft integrity and reliability. 1 Case Study: Aircraft Structural Integrity. Formulation of the Problem 1.1 Aerospace Testing: Why One of the most important characteristics of the plane is its weight: every pound shaved off the plane means a pound added to the carrying ability of this plane. As a result, planes are made as light as possible, with their "skin" as thin as possible. However, the thinner the layer, the more vulnerable is the resulting 1 structure to stresses and faults, and a flight is a very stressful experience. Therefore, even minor faults in the plane's structure, if undetect..