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.

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.

Influence of porosity and cement grade on concrete mechanical properties

Huang, Jiandong,Alyousef, Rayed,Suhatril, Meldi,Baharom, Shahrizan,Alabduljabbar, Hisham,Alaskar, Abdulaziz,Assilzadeh, Hamid Techno-Press 2020 Advances in concrete construction Vol.10 No.5

The given research focuses on examining the effect of relatively humidity (RH) and curing temperature on the hydrates as well as the porosity of calcium sulfoaluminate (CSA) cement pastes. Numerous tests, which consist of mercury intrusion porosimetry (MIP), thermosgravi metric (TG) and X-ray diffraction (XRD) were conducted. Various characterization techniques which include, scanning electron microscopy, Fourier transform microscopy along with X-ray diffraction evaluations were conducted on the samples to examine phase formation and crystallinity, morphology and microstructure along with bond formations and functional groups, respectively. During long-term study, the performance of concrete which consisted of limestone and flash-calcined was close to those from standard Portland cement concrete. Traditional classifications and methods of corrosion were widely used for the assessment of steel in concrete which may get employed to concrete which contains LC3 to recalibrate the range of polarization resistance for passitivity condition. For example, there is up to 79.5% and 146% respective flexural and compressive strengths. Moreover, they developed more advance electrical and thermo-mechanical performance with a substantial reduction in absorption of water of close to 400%. These advantages allow this research crucial to evaluate how these methods can be applied. Additionally, the research evaluates developed and more advanced cement preservation and repair techniques. The conclusion suggests concerted efforts by various stakeholders such as policy makers to enable low-carbon rates.

Application of adaptive neuro-fuzzy system in prediction of nanoscale and grain size effects on formability

Nan Yang,Meldi Suhatril,Khidhair Jasim Mohammed,H. Elhosiny Ali Techno-Press 2023 Advances in nano research Vol.14 No.2

Grain size in sheet metals in one of the main parameters in determining formability. Grain size control in industry requires delicate process control and equipment. In the present study, effects of grain size on the formability of steel sheets is investigated. Experimental investigation of effect of grain size is a cumbersome method which due to existence of many other effective parameters are not conclusive in some cases. On the other hand, since the average grain size of a crystalline material is a statistical parameter, using traditional methods are not sufficient for find the optimum grain size to maximize formability. Therefore, design of experiment (DoE) and artificial intelligence (AI) methods are coupled together in this study to find the optimum conditions for formability in terms of grain size and to predict forming limits of sheet metals under bi-stretch loading conditions. In this regard, a set of experiment is conducted to provide initial data for training and testing DoE and AI. Afterwards, the using response surface method (RSM) optimum grain size is calculated. Moreover, trained neural network is used to predict formability in the calculated optimum condition and the results compared to the experimental results. The findings of the present study show that DoE and AI could be a great aid in the design, determination and prediction of optimum grain size for maximizing sheet formability.

Prediction of shear capacity of channel shear connectors using the ANFIS model

Ali Toghroli,Mohammad Mohammadhassani,Meldi Suhatril,Mahdi shariati,Zainah Ibrahim 국제구조공학회 2014 Steel and Composite Structures, An International J Vol.17 No.5

Due to recent advancements in the area of Artificial Intelligence (AI) and computational intelligence, the application of these technologies in the construction industry and structural analysis has been made feasible. With the use of the Adaptive-Network-based Fuzzy Inference System (ANFIS) as a modelling tool, this study aims at predicting the shear strength of channel shear connectors in steel concrete composite beam. A total of 1200 experimental data was collected, with the input data being achieved based on the results of the push-out test and the output data being the corresponding shear strength which were recorded at all loading stages. The results derived from the use of ANFIS and the classical linear regressions (LR) were then compared. The outcome shows that the use of ANFIS produces highly accurate, precise and satisfactory results as opposed to the LR.

Identification of a suitable ANN architecture in predicting strain in tie section of concrete deep beams

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.

An evolutionary fuzzy modelling approach and comparison of different methods for shear strength prediction of high-strength concrete 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.

An evolutionary fuzzy modelling approach and comparison of different methods for shear strength prediction of high-strength concrete beams without stirrups

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.

Identification of a suitable ANN architecture in predicting strain in tie section of concrete deep beams

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.

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.