ORCID Profile
0000-0001-6082-5643
Current Organisation
Public Health Engineering Department, Peshawar, Government of Khyber Pakhtunkhwa
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Publisher: MDPI AG
Date: 22-08-2020
DOI: 10.3390/MA13173718
Abstract: In the current work, a novel hybrid laminate with negative Poisson’s ratio (NPR) is developed by considering auxetic laminate which is composed of carbon nanotube-reinforced composite (CNTRC) and fiber-reinforced composite (FRC) materials. The maximum magnitude of out-of-plane NPR is identified in the case of (20 F/20 C/−20 C/20 C) S laminate as well. Meanwhile, a method for the geometric non-linear analysis of hybrid laminated beam with NPR including the non-linear bending, free, and forced vibrations is proposed. The beam deformation is modeled by combining higher-order shear-deformation theory (HSDT) and large deflection theory. Based on a two-step perturbation approach, the asymptotic solutions of the governing equations are obtained to capture the linear and non-linear frequencies and load-deflection curves. Moreover, a two-step perturbation methodology in conjunction with fourth-order Runge–Kutta method is employed to solve the forced-vibration problem. Several key factors, such as CNT distribution, variations in the elastic foundation, and thermal stress, are considered in the exhaustive analysis. Theoretical results for some particular cases are given to examine the geometric non-linearity behavior of hybrid beam with NPR as well as positive Poisson’s ratio (PPR).
Publisher: MDPI AG
Date: 10-03-2022
DOI: 10.3390/SU14063274
Abstract: Foundry sand (FS) is produced as a waste material by metal casting foundries. It is being utilized as an alternative to fine aggregates for developing sustainable concrete. In this paper, an artificial intelligence technique, i.e., gene expression programming (GEP) has been implemented to empirically formulate prediction models for split tensile strength (ST) of concrete containing FS. For this purpose, an extensive experimental database has been collated from the literature and split up into training, validation, and testing sets for modeling purposes. ST is modeled as a function of water-to-cement ratio, percentage of FS, and FS-to-cement content ratio. The reliability of the proposed expression is validated by conducting several statistical and parametric analyses. The modeling results depicted that the prediction model is robust and accurate with a high generalization capability. The availability of reliable formulation to predict strength properties can promote the utilization of foundry industry waste in the construction sector, promoting green construction and saving time and cost incurred during experimental testing.
Publisher: MDPI AG
Date: 02-06-2022
DOI: 10.3390/SU14116801
Abstract: The understanding of the effects of multidirectional loadings imposed on major load bearing elements such as reinforced concrete (RC) columns by seismic actions for collapse prevention is of utmost importance, and a few simplified models are available in the literature. In this study, the distinguishing features of two machine-learning (ML) methods, namely, multi expression programming (MEP) and adaptive neuro-fuzzy inference system (ANFIS) are exploited for the first time to develop eight novel prediction models (M1-to M4-MEP and M1-to M4-ANFIS) with different combinations of input parameters to predict the biaxial shear strength of RC columns (V). The performance of the developed models was assessed using various statistical indicators and by comparing them with the experimental values. Based on the statistical analysis of the developed models, M1-ANFIS and M1-MEP performed very well and exhibited the best overall efficiency of the studied ML methods. Simple mathematical formulations were also provided by the MEP algorithm for the prediction of V, using which the M1-MEP model was finalized based on its performance, accuracy, and generalization capability. A parametric analysis was also performed for the model to show that the mathematical formulation provided by MEP accurately represents the system under consideration and is imperative for prediction purposes. Based on its performance, the model can thus be recommended to update the current code provisions and engineering practices.
Publisher: Walter de Gruyter GmbH
Date: 2020
Abstract: In this work, a hybrid laminated plate is developed by changing ply orientations and stacking sequences. The hybrid laminated plate is composed of carbon nanotube reinforced composite and fiber reinforced composite layers. Negative Poisson’s ratio (NPR) is obtained for the case of [22 F /(22 C /−22 C ) 3T /−22 F ] laminate. A theoretical laminated model considering geometric nonlinearity and shear deformation is presented. Based on a two-step perturbation method, the solutions of the motion equations are obtained to capture the nonlinear frequencies and load–deflection curves. On this basis, the fourth-order Runge–Kutta method is used to obtain the dynamic response of hybrid laminated plates. The presented model is verified by comparing the results obtained analytically and numerically. Several factors such as loading and distribution of carbon nanotubes (CNTs), and foundation type are considered in parametric study. Numerical results indicate that the thermal-mechanical behavior of hybrid laminated plates significantly improved by properly adjusting the CNT distribution. In addition, the results reveal that changes in temperature and foundation stiffness have pronounced influence on the nonlinear vibration characteristics of hybrid laminate plates with NPR as compared to those with positive Poisson’s ratio.
Publisher: Elsevier BV
Date: 12-2022
Location: Pakistan
No related grants have been discovered for Iftikhar Azim.