• Advances in nano research
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• v.16 no.6
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• pp.623-638
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• 2024

This study investigates the thermal post-buckling behavior of concrete eccentric annular sector plates reinforced with graphene oxide powders (GOPs). Employing the minimum total potential energy principle, the plates' stability and response under thermal loads are analyzed. The Haber-Schaim foundation model is utilized to account for the support conditions, while the transform differential quadrature method (TDQM) is applied to solve the governing differential equations efficiently. The integration of GOPs significantly enhances the mechanical properties and stability of the plates, making them suitable for advanced engineering applications. Numerical results demonstrate the critical thermal loads and post-buckling paths, providing valuable insights into the design and optimization of such reinforced structures. This study presents a machine learning algorithm designed to predict complex engineering phenomena using datasets derived from presented mathematical modeling. By leveraging advanced data analytics and machine learning techniques, the algorithm effectively captures and learns intricate patterns from the mathematical models, providing accurate and efficient predictions. The methodology involves generating comprehensive datasets from mathematical simulations, which are then used to train the machine learning model. The trained model is capable of predicting various engineering outcomes, such as stress, strain, and thermal responses, with high precision. This approach significantly reduces the computational time and resources required for traditional simulations, enabling rapid and reliable analysis. This comprehensive approach offers a robust framework for predicting the thermal post-buckling behavior of reinforced concrete plates, contributing to the development of resilient and efficient structural components in civil engineering.

• Journal of Information Technology Services
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• v.14 no.1
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• pp.53-68
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• 2015

This article aims to identify the quality factors of public data which have increased as a public issue; analyze the impact of users satisfaction in the perspective of Technology Acceptance Model (TAM), and investigate the effect of service satisfaction on the government's open policy of public data. This study is consistent with Total Data Quality Management (TDQM) of MIT, it focuses on three main qualities except Contextual Data Quality (CDQ) and includes seven independent variables : accuracy, reliability, fairness for Intrinsic Data Quality (IDQ), accessibility, security for Accessibility Data Quality (ADQ), Consistent representation and understandability for Representational Data Quality (RDQ). Basing on TAM, the research model was conducted to examine which factors affect to perceived usefulness, perceived ease of use, service satisfaction and how service satisfaction affects to the government's open policy of public data. The results showed that accuracy, fairness, understandability affect both perceived ease of use and perceived usefulness; while reliability, consistent representation, security, and accessibility affect only perceived ease of use. This article found that the influence of perceived ease of use on perceived usefulness and the influence of these two causes on service satisfaction in the perspective of TAM were significant and it was consistent with prior studies. The service satisfaction when using public data leads to the reliability of public data open policy. As an initial study on unstructured public data open policy, this article offered quality factors that pubic data providers should consider and also present the operation plan of public data open policy in the future.