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Mohammadhassani, Mohammad,Nezamabadi-pour, Hossein,Suhatril, Meldi,Shariati, Mahdi Techno-Press 2013 Structural Engineering and Mechanics, An Int'l Jou Vol.46 No.6
The comparison of the effectiveness of artificial neural network (ANN) and linear regression (LR) in the prediction of strain in tie section using experimental data from eight high-strength-self-compact-concrete (HSSCC) deep beams are presented here. Prior to the aforementioned, a suitable ANN architecture was identified. The format of the network architecture was ten input parameters, two hidden layers, and one output. The feed forward back propagation neural network of eleven and ten neurons in first and second TRAINLM training function was highly accurate and generated more precise tie strain diagrams compared to classical LR. The ANN's MSE values are 90 times smaller than the LR's. The correlation coefficient value from ANN is 0.9995 which is indicative of a high level of confidence.
Application of the ANFIS model in deflection prediction of concrete deep beam
Mohammadhassani, Mohammad,Nezamabadi-Pour, Hossein,Jumaat, MohdZamin,Jameel, Mohammed,Hakim, S.J.S.,Zargar, Majid Techno-Press 2013 Structural Engineering and Mechanics, An Int'l Jou Vol.45 No.3
With the ongoing development in the computer science areas of artificial intelligence and computational intelligence, researchers are able to apply them successfully in the construction industry. Given the complexities indeep beam behaviour and the difficulties in accurate evaluation of its deflection, the current study has employed the Adaptive Network-based Fuzzy Inference System (ANFIS) as one of the modelling tools to predict deflection for high strength self compacting concrete (HSSCC) deep beams. In this study, about 3668measured data on eight HSSCC deep beams are considered. Effective input data and the corresponding deflection as output data were recorded at all loading stages up to failure load for all tested deep beams. The results of ANFIS modelling and the classical linear regression were compared and concluded that the ANFIS results are highly accurate, precise and satisfactory.
Mohammadhassani, Mohammad,Nezamabadi-pour, Hossein,Suhatril, Meldi,shariati, Mahdi Techno-Press 2014 Smart Structures and Systems, An International Jou Vol.14 No.5
In this paper, an Adaptive nerou-based inference system (ANFIS) is being used for the prediction of shear strength of high strength concrete (HSC) beams without stirrups. The input parameters comprise of tensile reinforcement ratio, concrete compressive strength and shear span to depth ratio. Additionally, 122 experimental datasets were extracted from the literature review on the HSC beams with some comparable cross sectional dimensions and loading conditions. A comparative analysis has been carried out on the predicted shear strength of HSC beams without stirrups via the ANFIS method with those from the CEB-FIP Model Code (1990), AASHTO LRFD 1994 and CSA A23.3 - 94 codes of design. The shear strength prediction with ANFIS is discovered to be superior to CEB-FIP Model Code (1990), AASHTO LRFD 1994 and CSA A23.3 - 94. The predictions obtained from the ANFIS are harmonious with the test results not accounting for the shear span to depth ratio, tensile reinforcement ratio and concrete compressive strength; the data of the average, variance, correlation coefficient and coefficient of variation (CV) of the ratio between the shear strength predicted using the ANFIS method and the real shear strength are 0.995, 0.014, 0.969 and 11.97%, respectively. Taking a look at the CV index, the shear strength prediction shows better in nonlinear iterations such as the ANFIS for shear strength prediction of HSC beams without stirrups.
Mohammad Mohammadhassani,Hossein Nezamabadi-pour,Meldi Suhatril,Mahdi Shariati 국제구조공학회 2013 Structural Engineering and Mechanics, An Int'l Jou Vol.46 No.6
The comparison of the effectiveness of artificial neural network (ANN) and linear regression (LR) in the prediction of strain in tie section using experimental data from eight high-strength-self-compactconcrete (HSSCC) deep beams are presented here. Prior to the aforementioned, a suitable ANN architecture was identified. The format of the network architecture was ten input parameters, two hidden layers, and one output. The feed forward back propagation neural network of eleven and ten neurons in first and second TRAINLM training function was highly accurate and generated more precise tie strain diagrams compared to classical LR. The ANN’s MSE values are 90 times smaller than the LR’s. The correlation coefficient value from ANN is 0.9995 which is indicative of a high level of confidence.
Repairing cracked aluminum plates by aluminum patch using diffusion method
Sobhan Dehghanpour,Alireza Nezamabadi,Mohammad Mahdi Attar,Farzan Barati,Mehdi Tajdari 대한기계학회 2019 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.33 No.10
The aim of this work is to scrutinize the importance of diffusion bonding temperature, pressure and time on the repair of a cracked aluminum plate while the maximum tensile strength under a quasi-static loading has been investigated. The diffusion method was used to connect aluminum patches to repair the central and mode I cracks in thin aluminum plates. Here, the process of repairing cracked pieces made of aluminum or aluminum alloys involved a chemical and mechanical step to remove aluminum oxide and press the patch and piece under the temperature difference at a certain time. The repaired pieces underwent a quasi-static tensile loading while the maximum tolerable amount of force was obtained in different situations. Having assigned five temperatures, 300, 400, 500, 550 and 600 ° C, as well as four test times 30, 60, 120 and 180 min and five different pressures 10, 20, 50, 80 and 110 bar, we utilized fractional factorial design approach to perform 40 tests while there was seen a 10 mm initial crack in repaired samples . Experimental results showed that the optimum connection was resulted at 550 °C and the pressure of 80 bars within 120 min where the maximum force tolerated by the repaired vs. the unrepaired piece increased by 92 %. Therefore, having weighed the repair method using the diffusion bonding, we suggest that this type of the repair method can be a proper and cheap alternative to composite and adhesive patches.
Applications of the ANFIS and LR in the prediction of strain in tie section of concrete deep beams
Mohammad Mohammadhassani,Hossein Nezamabadi-Pour,Mohammed Jameel,Karim Garmasiri 사단법인 한국계산역학회 2013 Computers and Concrete, An International Journal Vol.12 No.3
Recent developments in Artificial Intelligence (AI) and computational intelligence have made it viable in the construction industry and structural analysis. This study usesthe Adaptive Network-based Fuzzy Inference System (ANFIS) as a modelling tool to predict the strain in tie section for High Strength Self Compacting Concrete (HSSCC) deep beams. 3773 experimental data were collected. The input data andits corresponding strains in tie section as output data were recorded at all loading stages. Results from ANFIS are compared with the classical linear regression (LR). The comparison shows that the ANFIS‘s results are highly accurate, precise and satisfactory.
Mohammad Mohammadhassani,Hossein Nezamabadi-pour,Meldi Suhatril,Mahdi shariati 국제구조공학회 2014 Smart Structures and Systems, An International Jou Vol.14 No.5
In this paper, an Adaptive nerou-based inference system (ANFIS) is being used for the prediction of shear strength of high strength concrete (HSC) beams without stirrups. The input parameters comprise of tensile reinforcement ratio, concrete compressive strength and shear span to depth ratio. Additionally, 122 experimental datasets were extracted from the literature review on the HSC beams with some comparable cross sectional dimensions and loading conditions. A comparative analysis has been carried out on the predicted shear strength of HSC beams without stirrups via the ANFIS method with those from the CEB-FIP Model Code (1990), AASHTO LRFD 1994 and CSA A23.3 – 94 codes of design. The shear strength prediction with ANFIS is discovered to be superior to CEB-FIP Model Code (1990), AASHTO LRFD 1994 and CSA A23.3 – 94. The predictions obtained from the ANFIS are harmonious with the test results not accounting for the shear span to depth ratio, tensile reinforcement ratio and concrete compressive strength; the data of the average, variance, correlation coefficient and coefficient of variation (CV) of the ratio between the shear strength predicted using the ANFIS method and the real shear strength are 0.995, 0.014, 0.969 and 11.97%, respectively. Taking a look at the CV index, the shear strength prediction shows better in nonlinear iterations such as the ANFIS for shear strength prediction of HSC beams without stirrups.
Mohammad Mohammadhassani,Hossein Nezamabadi-Pour,Mohd. Zamin Jumaat,Mohammed Jameel,Arul M S Arumugam 사단법인 한국계산역학회 2013 Computers and Concrete, An International Journal Vol.11 No.3
This paper presents the application of artificial neural network (ANN) to predict deep beam deflection using experimental data from eight high-strength-self-compacting-concrete (HSSCC) deep beams. The optimized network architecture was ten input parameters, two hidden layers, and one output. The feed forward back propagation neural network of ten and four neurons in first and second hidden layers using TRAINLM training function predicted highly accurate and more precise load-deflection diagrams compared to classical linear regression (LR). The ANN’s MSE values are 40 times smaller than the LR’s. The test data R value from ANN is 0.9931; thus indicating a high confidence level.
J. Jenabi,A.R. Nezamabadi,M. Karami Khorramabadi 국제구조공학회 2024 Structural Engineering and Mechanics, An Int'l Jou Vol.90 No.3
In current study, for the first time, Nonlinear Bending of a skew microplate made of a laminated composite strengthened with graphene nanosheets is investigated. A mixture of mechanical and thermal stresses is applied to the plate, and the reaction is analyzed using the First Shear Deformation Theory (FSDT). Since different percentages of graphene sheets are included in the multilayer structure of the composite, the characteristics of the composite are functionally graded throughout its thickness. Halpin-Tsai models are used to characterize mechanical qualities, whereas Schapery models are used to characterize thermal properties. The microplate’s non-linear strain is first calculated by calculating the plate shear deformation and using the Green-Lagrange tensor and von Karman assumptions. Then the elements of the Couple and Cauchy stress tensors using the Modified Coupled Stress Theory (MCST) are derived. Next, using the Hamilton Principle, the microplate’s governing equations and associated boundary conditions are calculated. The nonlinear differential equations are linearized by utilizing auxiliary variables in the nonlinear solution by applying the Frechet approach. The linearized equations are rectified via an iterative loop to precisely solve the problem. For this, the Differential Quadrature Method (DQM) is utilized, and the outcomes are shown for the basic support boundary condition. To ascertain the maximum values of microplate deflection for a range of circumstances— such as skew angles, volume fractions, configurations, temperatures, and length scales-a parametric analysis is carried out. To shed light on how the microplate behaves in these various circumstances, the resulting results are analyzed.
Application of the ANFIS model in deflection prediction of concrete deep beam
Mohammad Mohammadhassani,Hossein Nezamabadi-Pour,MohdZamin Jumaat,Mohammed Jameel,S.J.S.Hakim,Majid Zargar 국제구조공학회 2013 Structural Engineering and Mechanics, An Int'l Jou Vol.45 No.3
With the ongoing development in the computer science areas of artificial intelligence and computational intelligence, researchers are able to apply them successfully in the construction industry. Given the complexities indeep beam behaviour and the difficulties in accurate evaluation of its deflection,the current study has employed the Adaptive Network-based Fuzzy Inference System (ANFIS) as one of the modelling tools to predict deflection for high strength self compacting concrete (HSSCC) deep beams. In this study, about 3668measured data on eight HSSCC deep beams are considered. Effective input data and the corresponding deflection as output data were recorded at all loading stages up to failure load for all tested deep beams. The results of ANFIS modelling and the classical linear regression were compared and concluded that the ANFIS results are highly accurate, precise and satisfactory.