The introduction of concrete damage plasticity material models has significantly improved the accuracy with which the concrete structural elements can be predicted in terms of their structural response. Research into this method's accuracy in analyzing complex concrete forms has been limited. A damage model combined with a plasticity model, based on continuum damage mechanics, is recommended for effectively predicting and simulating concrete behaviour. The damage parameters, such as compressive and tensile damages, can be defined to simulate concrete behavior in a damaged-plasticity model accurately. This research aims to propose an analytical model for assessing concrete compressive damage based on stiffness deterioration. The proposed method can determine the damage variables at the start of the loading process, and this variable continues to increase as the load progresses until complete failure. The results obtained using this method were assessed through previous studies, whereas three case studies for concrete specimens and reinforced concrete structural elements (columns and gable beams) were considered. Additionally, finite element models were also developed and verified. The results revealed good agreement in each case. Furthermore, the results show that the proposed method outperforms other methods in terms of damage prediction, particularly when damage is calculated using the stress ratio. Doi: 10.28991/CEJ-2022-08-02-03 Full Text: PDF
HM Al-Dabbas, RA Azeez, AE Ali, IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 2023
Estimating the semantic similarity between short texts plays an increasingly prominent role in many fields related to text mining and natural language processing applications, especially with the large increase in the volume of textual data that is produced daily. Traditional approaches for calculating the degree of similarity between two texts, based on the words they share, do not perform well with short texts because two similar texts may be written in different terms by employing synonyms. As a result, short texts should be semantically compared. In this paper, a semantic similarity measurement method between texts is presented which combines knowledge-based and corpus-based semantic information to build a semantic network that repre
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