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RISS 인기검색어
- 검색키워드
- 저자 : M. Nasir Noor (검색결과 3 건)
- 무료
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RC structural system control subjected to earthquakes and TMD
Jenchung Shao,M. Nasir Noor,P. Ken,Chuho Chang,R. Wang 국제구조공학회 2024 Structural Engineering and Mechanics, An Int'l Jou Vol.89 No.2
This paper proposes a composite design of fuzzy adaptive control scheme based on TMD RC structural system and the gain of two-dimensional fuzzy control is controlled by parameters. Monitoring and learning in LMI then produces performance indicators with a weighting matrix as a function of cost. It allows to control the trade-off between the two efficiencies by adjusting the appropriate weighting matrix. The two-dimensional Boost control model is equivalent to the LMI-constrained multi-objective optimization problem under dual performance criteria. By using the proposed intelligent control model, the fuzzy nonlinear criterion is satisfied. Therefore, the data connection can be further extended. Evaluation of controller performance the proposed controller is compared with other control techniques. This ensures good performance of the control routines used for position and trajectory control in the presence of model uncertainties and external influences. Quantitative verification of the effectiveness of monitoring and control. The purpose of this article is to ensure access to adequate, safe and affordable housing and basic services. Therefore, it is assumed that this goal will be achieved in the near future through the continuous development of artificial intelligence and control theory.
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Y.C. Huang,M.D. TuMuli Lulios,Chu-Ho Chang,M. Nasir Noor,Jen-Chung Shao,Chien-Liang Chiu,Tsair-Fwu Lee,Renata Wang 국제구조공학회 2024 Structural Engineering and Mechanics, An Int'l Jou Vol.90 No.3
This research explores a new finite element model for the free vibration analysis of bi-directional functionally graded (BDFG) beams. The model is based on an efficient higher-order shear deformation beam theory that incorporates a trigonometric warping function for both transverse shear deformation and stress to guarantee traction-free boundary conditions without the necessity of shear correction factors. The proposed two-node beam element has three degrees of freedom per node, and the inter-element continuity is retained using both C1 and C0 continuities for kinematics variables. In addition, the mechanical properties of the (BDFG) beam vary gradually and smoothly in both the in-plane and out-of-plane beam’s directions according to an exponential power-law distribution. The highly elevated performance of the developed model is shown by comparing it to conceptual frameworks and solution procedures. Detailed numerical investigations are also conducted to examine the impact of boundary conditions, the bi-directional gradient indices, and the slenderness ratio on the free vibration response of BDFG beams. The suggested finite element beam model is an excellent potential tool for the design and the mechanical behavior estimation of BDFG structures.
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Machine Learning Technique for the Prediction of Blended Concrete Compressive Strength
Dawood S. A. Jubori,Abu B. Nabilah,Nor A. Safiee,Aidi H. Alias,Noor A. M. Nasir 대한토목학회 2024 KSCE Journal of Civil Engineering Vol.28 No.2
Predicting concrete strength is complex due to the high non-linearity involved in strength development, especially when using supplementary cementitious materials (SCMs) such as fly ash, silica fume, and GGBS. In this paper, an artificial neural network has been used to predict the compressive strength of concrete for four cases, namely concrete without cement replacement, and binary, ternary, and quaternary cement concretes corresponding to one, two and three different SCMs in the mix. To predict the strength accurately, a total of 1013 data were collected from 37 literature and trained using two training algorithms namely Levenberg-Marquardt (LM) and Bayesian Regularization (BR). The best predictions were achieved using one hidden layer with 14 and 15 neurons for LM and BR algorithms respectively. A high accuracy has been achieved with a correlation factor of 0.97 and 0.966 using the BR and LM algorithms respectively, with a20-index of 83%. Generally, the BR algorithm gives a better overall performance, while underestimating the compressive strength compared to LM. Sensitivity analysis has also been investigated using linear and quadratic regressions. The findings showed that the highest contributors to concrete strength were cement and water, while the lowest contributor was coarse aggregate.
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