Integral seismic risk assessment through fuzzy models

The usage of indicators as constituent parts of composite indices is an extended practice in many fields of knowledge. Even if rigorous statistical analyses are implemented, many of the methodologies follow simple arithmetic assumptions to aggregate indicators to build an index. One of the consequences of such assumptions can be the concealment of the influence of some of the composite index’s components. We developed a fuzzy method that aggregates indicators using non-linear methods and, in this paper, compare it to a well-known example in the field of risk assessment, called Moncho’s equation, which combines physical and social components and uses a linear aggregation method to estimate a level of seismic risk. By comparing the spatial pattern of the risk level obtained from these two methodologies, we were able to evaluate to what extent a fuzzy approach allows a more realistic representation of how social vulnerability levels might shape the seismic risk panorama in an urban environment. We found that, in some cases, this approach can lead to risk level values that are up to 80% greater than those obtained using a linear aggregation method for the same areas.Peer ReviewedPostprint (published version

Shared Nearest-Neighbor Quantum Game-Based Attribute Reduction with Hierarchical Coevolutionary Spark and Its Application in Consistent Segmentation of Neonatal Cerebral Cortical Surfaces

© 2012 IEEE. The unprecedented increase in data volume has become a severe challenge for conventional patterns of data mining and learning systems tasked with handling big data. The recently introduced Spark platform is a new processing method for big data analysis and related learning systems, which has attracted increasing attention from both the scientific community and industry. In this paper, we propose a shared nearest-neighbor quantum game-based attribute reduction (SNNQGAR) algorithm that incorporates the hierarchical coevolutionary Spark model. We first present a shared coevolutionary nearest-neighbor hierarchy with self-evolving compensation that considers the features of nearest-neighborhood attribute subsets and calculates the similarity between attribute subsets according to the shared neighbor information of attribute sample points. We then present a novel attribute weight tensor model to generate ranking vectors of attributes and apply them to balance the relative contributions of different neighborhood attribute subsets. To optimize the model, we propose an embedded quantum equilibrium game paradigm (QEGP) to ensure that noisy attributes do not degrade the big data reduction results. A combination of the hierarchical coevolutionary Spark model and an improved MapReduce framework is then constructed that it can better parallelize the SNNQGAR to efficiently determine the preferred reduction solutions of the distributed attribute subsets. The experimental comparisons demonstrate the superior performance of the SNNQGAR, which outperforms most of the state-of-the-art attribute reduction algorithms. Moreover, the results indicate that the SNNQGAR can be successfully applied to segment overlapping and interdependent fuzzy cerebral tissues, and it exhibits a stable and consistent segmentation performance for neonatal cerebral cortical surfaces

Water Absorption Process Parametric Selection For Natural Composites Using The PROMETHEE Method And Analytical Hierarchy Process For Objective Weights For Ship’s Hull Application

The purpose of this study is to establish the importance of parameters in a water absorption process for natural particulate composite for ship’s hull applications. To attain useful and reliable outcomes, the subjective evaluation of the assessor and weights of inputs are combined in a PROMETHEE and analytical hierarchy process (AHP) approach. The PROMETHEE serves the goal of ranking while the AHP is deployed to establish the objective weighing. It was found that time is the heading parameter for the natural particulate thermoset composite solutions, compared with thickness and length. By integrating PROMETHEE and AHP, it was proved that this approach offers a higher level of confidence to composite developers than initiative practices that currently dominate choices of parameters. It is particularly useful for natural particulate water absorption parametric selection since it is an innovative and scientific choice approach involving multicriteria analysis

Assessment of mechanical and tribological performance of hybrid Al/MoS2/Al2O3 composite by GFRA

This work emphasizes the mechanical and tribological performance of Al-Si/Al2O3/MoS2 hybrid matrix composites. The composites are reinforced by varying weight percentages of Al2O3 (8%, 12%, and 16%) and MoS2 (0%, 2%, and 4%), and were prepared by stir casting. As the weight percentage of Al2O3 in a composite grows, so does its hardness and tensile strength. The addition of 2% wt. MoS2 enhances the specific strength and tribological properties, according to the research. However, when compared to other composites studied, the Al/16% Al2O3 composite had improved mechanical properties. MoS2 also aids the hybrid composite in achieving higher tribological characteristics while marginally lowering the specific strength. Taguchi orthogonal array (L27) is used to design tribological performances with process parameters viz. applied load, sliding speed and weight % of Al2O3 as well the percentage of MoS2 whereas wear rate (mm3/m), wear (µm) and coefficient of friction were considered as the responses. A hybrid Grey–Fuzzy Reasoning Approach (GFRA) is used to optimize a multi-response for avoiding vagueness in decision making. The statistical analysis revealed that Al/2%MoS2/16%Al2O3 composite has exhibited better wear resistance than other composites. The confirmation test is also conducted to validate the optimal condition obtained by ANOVA.

Economic and Environmental Sustainability for Aircrafts Service Life

Aircrafts are responsible for a significant environmental impact mainly due to the air pollution caused by their motors. The use of composite materials for their production is a way to significantly reduce the weight of the structures and to maximise the ratio between the payload weight and the gasoline consumption. Moreover, the design phase has to consider the cost of different operations performed during the aircraft service life. During the entire life cycle, one of the main costs is the maintenance one. In the current literature, there is a lack of knowledge of methods for maintenance cost estimation in the aircraft industry; moreover, very few environmental assessment methods have been developed. Thus, the aim of this paper is to define a new method to support the aircraft design process; both the environmental and the economic dimensions have been included with the purpose of assessing the aircraft sustainability during its service life. A green index has been identified mixing the maintenance cost and an environmental parameter with the aim of identifying the greenest solution. A final practical application shows the feasibility and the simple application of the proposed approach

Recommendations for KYTC’s Railway/Highway At-Grade Crossing Surface Management Practices

An ideal Railway/Highway At-Grade Crossing Management program involves selecting costeffective practices when designing new crossings and rehabilitating existing crossings. This report outlines two strategies to enhance KYTC’s existing program. First, it describes a process that uses decision-option diagrams to optimize the assessment and implementation of engineering solutions in order to restore desired smoothness, minimize settlement in the postconstruction phase, and foster acceptable long-term performance of crossings following their rehabilitation. Decision-option diagrams rely on assessments that are site-specific and based on historical performance, the present observed performance and condition, and the measureable parameters specific to particular crossings. To supplement this process, the second strategy that this report proposes is the establishment of an effective managerial structure at KYTC that streamlines decision-making to ensure that the selected design is properly applied and implemented. Taken together, these proposals will significantly improve the state’s ability to systematically and cost-effectively repair deteriorated crossings

NASA SBIR abstracts of 1991 phase 1 projects

The objectives of 301 projects placed under contract by the Small Business Innovation Research (SBIR) program of the National Aeronautics and Space Administration (NASA) are described. These projects were selected competitively from among proposals submitted to NASA in response to the 1991 SBIR Program Solicitation. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 301, in order of its appearance in the body of the report. Appendixes to provide additional information about the SBIR program and permit cross-reference of the 1991 Phase 1 projects by company name, location by state, principal investigator, NASA Field Center responsible for management of each project, and NASA contract number are included

Robust Estimation of Reliability in the Presence of Multiple Failure Modes

In structural design, every component or system needs to be tested to ascertain that it satisfies the desired safety levels. Due to the uncertainties associated with the operating conditions, design parameters, and material systems, this task becomes complex and expensive. Typically these uncertainties are defined using random, interval or fuzzy variables, depending on the information available. Analyzing components or systems in the presence of these different forms of uncertainty increases the computational cost considerably due to the iterative nature of these algorithms. Therefore, one of the objectives of this research was to develop methodologies that can efficiently handle multiple forms of uncertainty. Most of the work available in the literature about uncertainty analysis deals with the estimation of the safety of a structural component based on a particular performance criterion. Often an engineering system has multiple failure criteria, all of which are to be taken into consideration for estimating its safety. These failure criteria are often correlated, because they depend on the same uncertain variables and the accuracy of the estimations highly depend on the ability to model the joint failure surface. The evaluation of the failure criteria often requires computationally expensive finite element analysis or computational fluid dynamics simulations. Therefore, this work also focuses on using high fidelity models to efficiently estimate the safety levels based on multiple failure criteria. The use of high fidelity models to represent the limit-state functions (failure criteria) and the joint failure surface facilitates reduction in the computational cost involved, without significant loss of accuracy. The methodologies developed in this work can be used to propagate various types of uncertainties through systems with multiple nonlinear failure modes and can be used to reduce prototype testing during the early design process. In this research, fast Fourier transforms-based reliability estimation technique has been developed to estimate system reliability. The algorithm developed solves the convolution integral in parts over several disjoint regions spanning the entire design space to estimate the system reliability accurately. Moreover, transformation techniques for non-probabilistic variables are introduced and used to efficiently deal with mixed variable problems. The methodologies, developed in this research, to estimate the bounds of reliability are the first of their kind for a system subject to multiple forms of uncertainty

Intelligent Data Storage and Retrieval for Design Optimisation – an Overview

This paper documents the findings of a literature review conducted by the Sir Lawrence Wackett Centre for Aerospace Design Technology at RMIT University. The review investigates aspects of a proposed system for intelligent design optimisation. Such a system would be capable of efficiently storing (and compressing if required) a range of types of design data into an intelligent database. This database would be accessed by the system during subsequent design processes, allowing for search of relevant design data for re-use in later designs, allowing it to become very efficient in reducing the time for later designs as the database grows in size. Extensive research has been performed, in both theoretical aspects of the project, and practical examples of current similar systems. This research covers the areas of database systems, database queries, representation and compression of design data, geometric representation and heuristic methods for design applications.

Coastal Deposits: Environmental Implications, Mathematical Modeling and Technological Development

This Special Issue on "Coastal Deposits: Environmental Implications, Mathematical Modeling, and Technological Development" includes seven high-quality, innovative research papers dealing with many scientific aspects regarding the coast, through mathematical modelling and innovative techniques in the study and preservation of the coastline from erosion, such as coastal watch camera installations, remote sensing, the use of biocementation, or analytical techniques, to assess incompatibilities in the sustainable use of the coast, including worrying issues as pollution of the marine environment and ecosystem deterioration