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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.
Ductility and strength assessment of HSC beams with varying of tensile reinforcement ratios
Mohammadhassani, Mohammad,Suhatril, Meldi,Shariati, Mahdi,Ghanbari, Farhad Techno-Press 2013 Structural Engineering and Mechanics, An Int'l Jou Vol.48 No.6
Nine rectangular-section of High Strength Concrete(HSC) beams were designed and casted based on the American Concrete Institute (ACI) code provisons with varying of tensile reinforcement ratio as (${\rho}_{min}$, $0.2_{{\rho}b}$, $0.3_{{\rho}b}$, $0.4_{{\rho}b}$, $0.5_{{\rho}b}$, $0.75_{{\rho}b}$, $0.85_{{\rho}b}$, $_{{\rho}b}$, $1.2_{{\rho}b}$). Steel and concrete strains and deflections were measured at different points of the beam's length for every incremental load up to failure. The ductility ratios were calculated and the moment-curvature and load-deflection curves were drawn. The results showed that the ductility ratio reduced to less than 2 when the tensile reinforcement ratio increased to $0.5_{{\rho}b}$. Comparison of the theoretical ductility coefficient from CSA94, NZS95 and ACI with the experimental ones shows that the three mentioned codes exhibit conservative values for low reinforced HSC beams. For over-reinforced HSC beams, only the CSA94 provision is more valid. ACI bending provision is 10 percent conservative for assessing of ultimate bending moment in low-reinforced HSC section while its results are valid for over-reinforced HSC sections. The ACI code provision is non-conservative for the modulus of rupture and needs to be reviewed.
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.
Ductility and strength assessment of HSC beams with varying of tensile reinforcement ratios
Mohammad Mohammadhassani,Meldi Suhatril,Mahdi Shariati,Farhad Ghanbari 국제구조공학회 2013 Structural Engineering and Mechanics, An Int'l Jou Vol.48 No.6
Nine rectangular-section of High Strength Concrete(HSC) beams were designed and casted based on the American Concrete Institute (ACI) code provisons with varying of tensile reinforcement ratio as (ρmin, 0.2ρb, 0.3ρb, 0.4ρb, 0.5ρb, 0.75ρb, 0.85ρb, ρb, 1.2ρb). Steel and concrete strains and deflections were measured at different points of the beam’s length for every incremental load up to failure. The ductility ratios were calculated and the moment-curvature and load-deflection curves were drawn. The results showed that the ductility ratio reduced to less than 2 when the tensile reinforcement ratio increased to 0.5ρb. Comparison of the theoretical ductility coefficient from CSA94, NZS95 and ACI with the experimental ones shows that the three mentioned codes exhibit conservative values for low reinforced HSC beams. For over-reinforced HSC beams, only the CSA94 provision is more valid. ACI bending provision is 10 percent conservative for assessing of ultimate bending moment in low-reinforced HSC section while its results are valid for overreinforced HSC sections. The ACI code provision is non-conservative for the modulus of rupture and needs to be reviewed.
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.
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 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.
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.
Fuzzy modelling approach for shear strength prediction of RC deep beams
Mohammad Mohammadhassani,Aidi MD. Saleh,M Suhatril,M. Safa 국제구조공학회 2015 Smart Structures and Systems, An International Jou Vol.16 No.3
This study discusses the use of Adaptive-Network-Based-Fuzzy-Inference-System (ANFIS) in predicting the shear strength of reinforced-concrete deep beams. 139 experimental data have been collected from renowned publications on simply supported high strength concrete deep beams. The results show that the ANFIS has strong potential as a feasible tool for predicting the shear strength of deep beams within the range of the considered input parameters. ANFIS‟s results are highly accurate, precise and therefore, more satisfactory. Based on the Sensitivity analysis, the shear span to depth ratio (a/d) and concrete cylinder strength ( c f′) have major influence on the shear strength prediction of deep beams. The parametric study confirms the increase in shear strength of deep beams with an equal increase in the concrete strength and decrease in the shear span to-depth-ratio.
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.