Normalized Weighting Schemes for Image Interpolation Algorithms

This paper presents and evaluates four weighting schemes for image interpolation algorithms. The first scheme is based on the normalized area of the circle, whose diameter is equal to the minimum side of a tetragon. The second scheme is based on the normalized area of the circle, whose radius is equal to the hypotenuse. The third scheme is based on the normalized area of the triangle, whose base and height are equal to the hypotenuse and virtual pixel length, respectively. The fourth weighting scheme is based on the normalized area of the circle, whose radius is equal to the virtual pixel length-based hypotenuse. Experiments demonstrated debatable algorithm performances and the need for further research.Comment: 8 pages, 14 figure

A fuzzy-based Bayesian Belief Network approach for railway bridge condition monitoring and fault detection

More than 35% of the European railway bridges are over 100 years old and the increasing traffic loads are pushing the railway infrastructure to its limits. Bridge condition-monitoring strategies can help the railway industry to improve safety, availability and reliability of the network. In this paper, a Bayesian Belief Network method for condition monitoring and fault detection of a truss steel railway bridge is proposed by relying on a fuzzy analytical hierarchy process of expert knowledge. The BBN method is proposed for obtaining the bridge health state and identifying the most degraded bridge elements. A Finite Element model is developed for simulating the bridge behaviour and studying a degradation mechanism. The proposed approach originally captures the interactions existing between the health state of different bridge elements and, furthermore, when the evidence about the displacement is introduced in the BBN, the health state of the bridge is updated

Neutrosophic Completion Technique for Incomplete Higher-Order AHP Comparison Matrices

[EN] After the recent establishment of the Sustainable Development Goals and the Agenda 2030, the sustainable design of products in general and infrastructures in particular emerge as a challenging field for the development and application of multicriteria decision-making tools. Sustainability-related decision problems usually involve, by definition, a wide variety in number and nature of conflicting criteria, thus pushing the limits of conventional multicriteria decision-making tools practices. The greater the number of criteria and the more complex the relations existing between them in a decisional problem, the less accurate and certain are the judgments required by usual methods, such as the analytic hierarchy process (AHP). The present paper proposes a neutrosophic AHP completion methodology to reduce the number of judgments required to be emitted by the decision maker. This increases the consistency of their responses, while accounting for uncertainties associated to the fuzziness of human thinking. The method is applied to a sustainable-design problem, resulting in weight estimations that allow for a reduction of up to 22% of the conventionally required comparisons, with an average accuracy below 10% between estimates and the weights resulting from a conventionally completed AHP matrix, and a root mean standard error below 15%.The authors acknowledge the financial support of the Spanish Ministry of Economy and Business, along with FEDER funding (DIMALIFE Project: BIA2017-85098-R).Navarro, IJ.; Martí Albiñana, JV.; Yepes, V. (2021). Neutrosophic Completion Technique for Incomplete Higher-Order AHP Comparison Matrices. Mathematics. 9(5):1-19. https://doi.org/10.3390/math905049611995Worrell, E., Price, L., Martin, N., Hendriks, C., & Meida, L. O. (2001). CARBON DIOXIDE EMISSIONS FROM THE GLOBAL CEMENT INDUSTRY. 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Sustainability assessment of concrete bridge deck designs in coastal environments using neutrosophic criteria weights

"This is an Accepted Manuscript of an article published by Taylor & Francis in Structure and Infrastructure Engineering on 02/07/2020, available online: https://doi.org/10.1080/15732479.2019.1676791."[EN] Essential infrastructures such as bridges are designed to provide a long-lasting and intergenerational functionality. In those cases, sustainability becomes of paramount importance when the infrastructure is exposed to aggressive environments, which can jeopardise their durability and lead to significant maintenance demands. The assessment of sustainability is however often complex and uncertain. The present study assesses the sustainability performance of 16 alternative designs of a concrete bridge deck in a coastal environment on the basis of a neutrosophic group analytic hierarchy process (AHP). The use of neutrosophic logic in the field of multi-criteria decision-making, as a generalisation of the widely used fuzzy logic, allows for a proper capture of the vagueness and uncertainties of the judgements emitted by the decision-makers. TOPSIS technique is then used to aggregate the different sustainability criteria. From the results, it is derived that only the simultaneous consideration of the economic, environmental and social life cycle impacts of a design shall lead to adequate sustainable designs. Choices made based on the optimality of a design in only some of the sustainability pillars will lead to erroneous conclusions. The use of concrete with silica fume has resulted in a sustainability performance of 46.3% better than conventional concrete designs.The authors acknowledge the financial support of the Spanish Ministry of Economy and Competitiveness, along with FEDER funding (Project: BIA2017-85098-R).Navarro, I.; Yepes, V.; Martí, J. (2020). 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A Bayesian Belief Network method for bridge deterioration detection

Bridges are one of the most important assets of transportation networks. A closure of a bridge can increase the vulnerability of the geographic area served by such networks, as it reduces the number of available routes. Condition monitoring and deterioration detection methods can be used to monitor the health state of a bridge and enable detection of early signs of deterioration. In this paper, a novel Bayesian Belief Network (BBN) methodology for bridge deterioration detection is proposed. A method to build a BBN structure and to define the Conditional Probability Tables (CPTs) is presented first. Then evidence of the bridge behaviour (such as bridge displacement or acceleration due to traffic) is used as an input to the BBN model, the probability of the health state of whole bridge and its elements is updated and the levels of deterioration are detected. The methodology is illustrated using a Finite Element Model (FEM) of a steel truss bridge, and for an in-field post-tensioned concrete bridge

Fuzzy analytic hierarchy process: a comparison of the existing algorithms with new proposals

In a multiple-criteria decision analysis, prioritizing and assigning weights to each criteria with reference to set of available alternatives is key to effective decision making. Analytic Hierarchy Process (AHP) is one such technique through which experts provide pairwise comparisons and this information is processed in a comparison matrix to calculate priority vector which ranks the available alternatives. Original AHP as proposed by Thomas L. Saaty used crisp numbers to represent pairwise comparisons. However, human judgments are often vague and traditional 1-9 scale is not capable to incorporate the inherent human uncertainty into pairwise comparisons. In order to address this issue, fuzzy set theory is being used along side original AHP where human judgments are recorded in the form of fuzzy numbers and thus comparison matrices are formed in such a way that its elements are fuzzy numbers. Various algorithms have been proposed over the past three decades through which priority vector can be calculated from fuzzy comparison matrices. This study performs an extensive review of the most common algorithms proposed in fuzzy AHP (FAHP) and conducts a performance analysis of nine algorithms, out of which ve are existing FAHP algorithms namely Logarithmic Least Square Method (LLSM), Modified LLSM, Fuzzy Extent Analysis (FEA), modified FEA and Buckley's Geometric Mean method, while four models are introduced in this study which includes Geometric Mean method, Arithmetic Mean method, Row Sum method and Inverse of Column Sum method. A separate algorithm is also proposed to construct fuzzy comparison matrices of varying sizes, level of fuzziness and inconsistency, so as to carry out performance analysis of the selected nine FAHP algorithms. We found that Geometric Mean method discussed in this study performs significantly better than other algorithms, while FEA is the worst performing algorithm. Although at high inconsistency levels, performance of FEA method improves however, even at high inconsistency levels, Geometric Mean method performs significantly better. Modi cation to FEA method (Row Sum method) proposed in this study significantly improves its performance and this modified FEA method is the second best performing algorithm among the selected nine FAHP models. In addition, we also conducted a comparative analysis based on popularity, computational time, applicability of fuzzy numbers, ease of understanding and ease of implementation. Through this study, we attempt to consolidate the existing literature on FAHP algorithms and identify the best performing methodologies to calculate priority vector from fuzzy comparison matrices

Barrier analysis approach in metal additive manufacturing implementation with environment consideration

Notwithstanding the developments in additive manufacturing technology have been set to overcome human limitations and improve efficiency in manual restoration activities, their widespread implementation as a disruptive production technology has brought various impacts on the environment, and the environmental assessment is limited in this regard. The Malaysian automotive industry has not seen widespread adoption of Life Cycle Assessment for additive manufacturing implementation. Based on the current literature review, there is a gap as the barriers for implementing Life Cycle Assessment in additive manufacturing technology within the Malaysian automotive manufacturing industry are not critically discussed. There is a need for developing appropriate approaches to weight and determine the interrelationships between these obstacles and the most prevalent ones in order to devise mitigation strategies for them. The purposes of this study are to identify various barriers of implementing Life Cycle Assessment in metal additive manufacturing within Malaysian automotive manufacturing industry and, secondly, to develop an approach to prioritize the barriers and recognize the most critical barriers. In this regard, the extant literature has critically reviewed the barriers of implementing Life Cycle Assessment in metal additive manufacturing within Malaysian automotive manufacturing industry. Fuzzy preference programming, as one of the newest and most accurate fuzzy modifications of the Analytical Hierarchy Process, was used to achieve the research purposes. Suitable Triangular Fuzzy Number has been defined and the selected data collection method was expert opinion. A total of eight industry experts from one company were involved in this research study to give their opinion on the Fuzzy Analytical Hierarchy Process pairwise comparison table. The expert opinions indicated that the main concern of industry is financial-related topic. The data collected have been analyzed using Fuzzy Analytical Hierarchy Process calculations and confirmed by the consistency check. Following the results, dominant barriers were accordingly identified and ranked in each category as well as overall. According to the results from expert opinions, the highest-ranking barrier is lack of financial resources, followed by lack of Life Cycle Assessment expertise in the additive manufacturing context, and the third rank is the lack of laws and directives for Life Cycle Assessment application in additive manufacturing. The findings may be useful to managers to develop suitable mitigation strategies and make more informed decisions with individual focus, level focus, or cluster focus. It may also contribute to the additive manufacturing literature by the weighted presentation of the barriers to implementing Life Cycle Assessment in additive manufacturing within the Malaysian automotive manufacturing industry. This study will contribute to a framework of roadmaps and strategies for sound and environmentally friendly additive manufacturing implementation in Malaysian automotive industry