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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.
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.
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.
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.
Exploring the Potential of Tannin Based Colorants Towards Functional Value Addition of Wool Textiles
Mohd Shabbir,Luqman Jameel Rather,Faqeer Mohammad 한국섬유공학회 2019 Fibers and polymers Vol.20 No.9
Nature has been a first inspiration for the evolution of mankind. Natural colorants of plants origin highlyinfluenced the textile industry and target a wide range of textile functionalities. Tannins from Terminalia chebula fruits havebeen explored to develop colored, UV protective and antioxidant functional wool textiles. Metallic mordants (Alum, ferroussulfate, and stannous chloride) under ecological permissible limits are utilized to establish the correlation with the functionalproperties achieved on textiles. Shades of various colors on wool are characterized in terms of CIEL*a*b* and color strength(K/S) values on Hunter-Lab spectrophotometer. Ultra-violet (UV) protection ability was measured in terms of UVtransmittance and UPF (UV protection factor) values. UV transmittance is consistently below 5 % in all dyed wool fabrics,while it was upto more than 30 % in undyed wool. Antioxidant results of both mordanted and unmordanted (control) dyedwool fabrics are found highly appreciable up to 99 % of activity. Multifunctional colored wool textiles are developed withecofriendly and economically viable process and resources.
Assessment of Rheological and Piezoresistive Properties of Graphene based Cement Composites
Sardar Kashif Ur Rehman,Zainah Ibrahim,Mohammad Jameel,Shazim Ali Memon,Muhammad Faisal Javed,Muhammad Aslam,Kashif Mehmood,Sohaib Nazar 한국콘크리트학회 2018 International Journal of Concrete Structures and M Vol.12 No.6
The concrete production processes including materials mixing, pumping, transportation, injection, pouring, moulding and compaction, are dependent on the rheological properties. Hence, in this research, the rheological properties of fresh cement paste with different content of graphene (0.03, 0.05 and 0.10% by weight of cement) were investigated. The parameters considered were test geometries (concentric cylinders and parallel plates), shear rate range (300–0.6, 200–0.6 and 100–0.6 s<SUP>−1</SUP>), resting time (0, 30 and 60 min) and superplasticizer dosage (0 and 0.1% by weight of cement). Four rheological prediction models such as Modified Bingham, Herschel–Bulkley, Bingham model and Casson model were chosen for the estimation of the yield stress, plastic viscosity and trend of the flow curves. The effectiveness of these rheological models in predicting the flow properties of cement paste was verified by considering the standard error method. Test results showed that the yield stress and the plastic viscosity increased with the increase in graphene content and resting time while the yield stress and the plastic viscosity decreased with the increase in the dosage of superplasticizer. At higher shear rate range, the yield stress increased while the plastic viscosities decreased. The Herschel–Bulkley model with the lowest average standard error and standard deviation value was found to best fit the experimental data, whereas, Casson model was found to be the most unfitted model. Graphene reduces the flow diameter and electrical resistivity up to 9.3 and 67.8% and enhances load carrying capacity and strain up to 16.7 and 70.1% of the composite specimen as compared with plain cement specimen. Moreover, it opened a new dimension for graphene-cement composite as smart sensing building construction material.
Muhammad Bilal,Tasneem G. Kazi,Hassan I. Afridi,Mohammad Balal Arain,Jameel A. Baig,Mustafa Khan,Naeemullah Khan 한국공업화학회 2016 Journal of Industrial and Engineering Chemistry Vol.40 No.-
A new method based on modification of cloud point extraction for simultaneous enrichment ofcadmium, lead and copper in water and fish muscle samples was proposed. The extraction efficiency bymodified cloud point extraction (m-CPE) was compared with conventional cloud point extraction (c-CPE)method. The procedure for both CPE methods was comprised of formation of metal complexes with ahydrophobic chelating agent, dithizone, followed by entrapment of the chelates in a nonionic surfactant,Triton X-114. For c-CPE, the surfactant rich phase was treated with ethanolic solution of nitric acid andanalyzed by flame atomic absorption spectrometer (FAAS). Whereas for m-CPE, aqueous nitric acid wasused to back extract the metal ions from the surfactant rich phase and finally determined. The efficiencyof the methods was tested by analyzing certified reference material and standard addition to a realsample. All the experimental parameters were optimized. At optimized experimental conditions,preconcentration and enhancement factors were 62.5 and 78.0 to 83.0 respectively for m-CPE, 25.0 and56.0% higher than that of c-CPE. This improvement might be due to elimination of the effects ofsurfactant on the signal of analytes by FAAS. The developed method of m-CPE was applied successfullyfor analysis of the selected heavy metals in water and muscle tissues samples of fish of different lakes inSindh, Pakistan.