Fiber-reinforced concrete (FRC) is one of the efficient innovation in concrete industry that has the ability to enhance the mechanical properties significantly. To cope up with the increase in infrastructural activiti...
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ISBN:
(纸本)9781450376785
Fiber-reinforced concrete (FRC) is one of the efficient innovation in concrete industry that has the ability to enhance the mechanical properties significantly. To cope up with the increase in infrastructural activities which resulted in greater demand in production of different construction materials have a negative impact on the environment, this study aims to determine the mechanical performance of the optimum compressive and flexural strength of buntal fiber-reinforced concrete with surkhi as partial replacement for sand (BFRC-SS). Using 28th-day compressive and flexural strength, several mixtures were experimentally tested to derive a mix proportion that gave the best mechanical properties of BFRC-SS. From the results, best hybrid models of compressive and flexural strength were formulated using Artificial Neural Network (ANN). Results showed that ANN was able to establish the effects of surkhi and buntal (Corypha utan Lam) fiber to the mechanical properties of BFRC-SS. Furthermore, the multi-objective Genetic Algorithm (GA) model generated the optimum proportion for the best compressive and flexural strength. fuzzyinferencesystem (FIS) and Multi-Linear Regression Analysis (MLRA) were also utilized to assess and validate the hybrid model through surface imaging Utilizing least percent error, ANN hybrid model showed the most significant predictive model compared to other models generated by MLRA and FIS. This study adoptied the fusion of 4.0 Industrial Revolution and favoring creativity and integrity through artificial intelligence.
This paper presents an evolutionary algorithm that allows the adaptation of neuro-fuzzysystems according to conflicting objectives. The adaptation includes both the structures and the parameters of the evolved soluti...
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ISBN:
(纸本)9781728190488
This paper presents an evolutionary algorithm that allows the adaptation of neuro-fuzzysystems according to conflicting objectives. The adaptation includes both the structures and the parameters of the evolved solutions. The suggested algorithm, which is termed fuzzy-Evolution of Membership and Structures by Decomposition (FEMS/D). As suggested by its name, the algorithm is based on the decomposition approach, which is a divide-and-conquer technique that transforms the original multi-objective problem into a set of single-objective sub-problems. In addition, this paper provides initial demonstrations of the applicability of FEMS/D to the development of non-dominated robot motion controllers, which exhibit a range of behaviors between the safest and the fastest ones. Finally, the adaptation of the controllers to an abrupt change in the environment is also demonstrated.
The growing number of people with cognitive impairment will significantly increase healthcare demand. Screening tools are crucial for detecting cognitive impairment due to a shortage of mental health experts aiming to...
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The growing number of people with cognitive impairment will significantly increase healthcare demand. Screening tools are crucial for detecting cognitive impairment due to a shortage of mental health experts aiming to improve the quality of life for those living with this condition. Eye tracking is a powerful tool that can provide deeper insights into human behavior and inner cognitive processes. The proposed Eye-Tracking-Based Trail-Making Test, ETMT, is a screening tool for monitoring a person's cognitive function. The proposed system utilizes a fuzzy-inference system as an integral part of its framework to calculate comprehensive scores assessing visual search speed and focused attention. By employing an adaptive neuro-fuzzy-inference system, the tool provides an overall cognitive-impairment score, allowing psychologists to assess and quantify the extent of cognitive decline or impairment in their patients. The ETMT model offers a comprehensive understanding of cognitive abilities and identifies potential deficits in various domains. The results indicate that the ETMT model is a potential tool for evaluating cognitive impairment and can capture significant changes in eye movement behavior associated with cognitive impairment. It provides a convenient and affordable diagnosis, prioritizing healthcare resources for severe conditions while enhancing feedback to practitioners.
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