i-manager Publications

i-manager's Journal on Structural Engineering (JSTE)

Current Issue Vol. 12 Issue 3

Assessment of Uncertainty of Error in Design Flood Estimates Using 2-Parameter Log Normal Distribution
The Impact of Plan Irregularity on Multistory Building Response: A Pushover Analysis
Static and Vibration Analysis of Reinforced Cement Concrete Cellular Beams: A Three-Dimensional Study
Pumice as a Coarse Aggregate Substitute for Lightweight Concrete: Strength and Density Assessment
Masonry Infill in Structural Analysis: Effects on Seismic Performance

Most Cited

Volume 3 Issue 1 March - May 2014

Research Paper

Comparative Analysis of Elevated Water Storage Structure using Different Typesof Bracing Patterns in Staging

Gaurav S. Atalkar* , 0**

* M.Tech Student, Civil Engineering Department, ShriRamdeobaba College of Engineering & Management, Nagpur, India.

** Assistant Professor, Civil Engineering Department,ShriRamdeobaba College of Engineering & Management, Nagpur, India.

Atalkar, G.S., and Gharad, A.M. (2014). Comparative Analysis of Elevated Water Storage Structure using Different Types of Bracing Patterns in Staging. i-manager’s Journal on Structural Engineering, 3(1), 1-6. https://doi.org/10.26634/jste.3.1.2978

Abstract

From the very upsetting experiences of few earthquakes, like the Bhuj earthquake in India (DurgeshC. Rai 2003), R.C.C elevated water tanks were heavily damaged or collapsed. This might be due to lack of knowledge regarding the proper behavior of supporting pattern of the tank due to dynamic effect and improper geometrical selection of staging. The main aim of this study is (i) to understand the behavior of different staging, under different loading conditions (ii) to strengthen the conventional type of staging during earthquake. Seismic coefficient method, for five different types of bracing patterns, is applied to the staging of elevated circular water tank in zone IV, and is carried out using STAAD Pro. V8i. Comparison of base / storey shear and nodal displacements of the container of circular water tank for empty, half filled and full condition is done. Alternate diagonal bracing pattern for staging yields the optimum value of base shear and displacement, compared to other patterns that are studied.

References

[1]. Durgesh C. Rai (2003). “Performance of Elevated Tanks in Mw 7.7 Bhuj Earthquak of January 26th, 2001.”Proc. Indian Acad. Sci. (Earth Planet. Sci.), 112, No. 3 pp. 421- 429

[2]. H. S. Patel and Ayazhussain M. Jabar(2012). “Seismic Behavior of RC Elevated Water Tank Under Different Staging Pattern and Earthquake Characteristics.”International Journal of Advanced Engineering Research and Studies, EISSN2249– 8974.

[3]. IITK-GSDMA (October 2007). “Guidelines for Seismic Design of Liquid Storage Tanks: Provisions with Commentary and Explanatory Examples.” Gujarat State Disaster Management Authority, Indian Institute of Technology Kanpur.

[4]. IS 11682:1985, “Criteria for Design of RCC Staging for Overhead Water Tanks.” Bureau of Indian Standards, New Delhi, India.

[5]. IS 1893:2000(Part 1), “Criteria for Earthquake Resistant Design Structures, General Provisions and Buildings.” Bureau of Indian Standards, NewDelhi, India.

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Research Paper

Modelling Annual Rainfall of Krishna and Godavari River Basins using Extreme Value Type-1 Distribution

N. Vivekanandan*

*Assistant Research Officer, Hydrometeorology Division, Central Water and Power Research Station, Pune, Maharashtra, India.

Vivekanandan, N. (2014). Modelling Annual Rainfall of Krishna and Godavari River Basins using Extreme Value Type-1 Distribution. i-manager’s Journal on Structural Engineering, 3(1), 7-12. https://doi.org/10.26634/jste.3.1.2979

Abstract

Assessment of annual rainfall for a river basin is of utmost importance for planning, design and management of water resources projects. This paper illustrates the use of six parameter estimation methods of Extreme Value Type-1 (EV1) distribution for modelling annual rainfall of Krishna and Godavari river basins. Goodness-of-Fit (GoF) tests such as Anderson-Darling and Kolmogorov-Smirnov are used for checking the adequacy of fitting of EV1 distribution to the recorded rainfall data. A diagnostic test of root mean square error is used for the selection of a suitable method for modelling annual rainfall. Based on GoF and diagnostic test results, probability weighted moments is identified as best suited method for modelling annual rainfall of Krishna and Godavari river basins.

References

[1]. Arora, K., & Singh, V.P. (1987). “On statistical intercomparison of EVI estimators by Monte Carlo simulation”. Advanced Water Resources, 10 (2), pp.87- 107.

[2]. Atomic Energy Regulatory Board (AERB, 2008). “Extreme values of meteorological parameters”, AERB Guide No. NF/SG/ S-3.

[3]. Baratti, E., Montanari, A., Castellarin, A., Salinas, J.L., Viglione, A., & Bezzi, A. (2012). “Estimating the flood frequency distribution at seasonal and annual time scales”. Hydrological Earth System Science, 16 (12), pp.4651 –4660.

[4]. Casas, M.C., Rodriguez, R., Prohom, M., Gazquez, A., & Redano, A. (2011). “Estimation of the probable maximum precipitation in Barcelona (Spain)”. Journal of Climatology, 31 (9), pp.1322–1327.

[5]. Gumbel, E.J. (1960). “Statistic of Extremes”, 2 Edition, Columbia University Press, New York, USA.

[6]. Indian Institute of Tropical Meteorology (IITM, 2007). “Characteristics of hydrological wet season over different river basins in India”. IITM Report No. RR-119.

[7]. Landwehr, J.M., Matalas, N.C., & Wallis, J.R. (1979). “Probability weighted moments compared with some traditional techniques in estimating Gumbel parameters and quantiles”. Water Resources Research, 15 (5), 1055- 1064.

[8]. Lieblein, J. (1974). “Note on simplified estimates for Type I extreme value distribution”. NBSIR 75-647, National Bureau of Standards, Washington.

[9]. Manik, D., & Datta, S.K. (1998). “A comparative study of estimation of extreme value”, Journal of River Behaviour and Control, 25 (1), pp. 41-47.

[10]. Mujere, N. (2011). “Flood frequency analysis using the Gumbel distribution”. Journal of Computer Science and Engineering, 3 (7), pp.2774-2778.

[11]. Phien, H.N. (1987). “A review of methods of parameter estimation for the extreme value type–1 distribution”. Journal of Hydraulics, 90 (3 & 4), 251-268.

[12]. Ranyal, J.A., & Salas, J.D. (1986). “Estimation procedures for the type-1 extreme value distribution”, Journal of Hydrology, 87 (3 & 4), pp.315-336.

[13]. Uboldi, F., Sulis, A.N., Lussana, C., Cislaghi, M., & Russo, M. (2013). “A spatial bootstrap technique for parameter estimation of rainfall annual maxima distribution”, Hydrology and Earth System Sciences Discussions, 10 (10), pp.11755–11794.

[14]. Vijayagopal, P., Vivekanandan, N., & Kannan, S. (2013). “Assessing adequacy of probability distribution for development of IDF relationships for Mandla and Jabalpur”, International Journal of Scientific Research and Reviews, 2 (3), pp.99-114.

[15]. Zhang, J. (2002). “Powerful goodness-of-fit tests based on the likelihood ratio”, Journal of Royal Statistical Society, 64 (2), pp.281-294.

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Research Paper

Parametric Optimization of Alkali Activated F-Type Flyash-Based Geopolymer Concrete

Kolli Ramujee* , P. Hanumantha Rao, M. Potharaju*, Radhakrishna****

* Associate Professor, Department of Civil Engineering, VNR VignanaJyothi Institute of Engineering & Technology, Hyderabad.

P.G student, Department of Civil Engineering, VNR VignanaJyothi Institute of Engineering & Technology, Hyderabad

* Professor of Civil Engineering, GITAM University, Visakhapatnam.

**** Dean P.G studies, R V College of Engineering, Bangalore.

Ramujee, K., Hanumantharao, R., Potharaju, M., and Radhakrishna (2014). Parametric Optimization of Alkali Activated F-Type Flyash-Based Geopolymer Concretee. i-manager’s Journal on Structural Engineering, 3(1), 13-19. https://doi.org/10.26634/jste.3.1.2980

Abstract

This paper describes the effects of various optimized parameters on the properties of fly ash based geopolymer concrete, especially the compressive strength, density and workability have been investigated. The effect of various optimized parameters related to alkaline activator such as molar concentration of sodium hydroxide solution in the range of 8M-16M, mass ratio of alkaline liquid to fly-ash in the range of 0.4 and 0.33, the rest period in the range of 1- 5days, the curing time and curing temperature in the range of 24-72 hours and 60-100°C respectively were considered. The geopolymer concrete test specimens of cube size 150 x 150 x 150 mm were tested for their compressive strength at the age of 3,7 ,14 and 28 days. The compressive strength of geopolymer concrete ranging from 24 to 63MPa have been developed. Test results show that the compressive strength increases with increase in molar concentration of sodium hydroxide solution duration of oven curing, curing temperature and rest period .The compressive strength of geopolymer concrete decreases with increase in Alkaline liquid to Fly ash ratio. For alkaline liquid, the ratio of Sodium Silicate solution to Sodium hydroxide solution was fixed as 2.5 and the dosage of Super plasticizer maintained constant as 2 % by weight of fly ash. The experimental investigation concludes that some parameters are in disagreement with the results reported in past literature and some other parameters are in agreement with the available literature.

References

[1]. Davidovits, J., (1988). “Soft Mineralogy and Geopolymers”, Proceedings of the Geopolymer 88 International Conference, The Université de Technologie, Compiègne, France.

[2]. Davidovits J., (2008). “Geopolymer Chemistry and Applications”. Institute Géopolymère, Saint-Quentin, France.

[3]. B. VijayaRangan., (2008). “Mix design and production of fly ash based geopolymer concrete”. The Indian concrete Journal.

[4]. J.G.S. Van Jaarsveld, J. S. J. Van Deventer and G. C. Lukey (2002), “The Effect of Composition and Temperature on the Properties of Fly Ashand Kaolinite-based Geopolymers”. Chemical Engineering Journal, 89(1-3) pp. 63-73 .

[5]. A. Palomo, M. W. Grutzeck, M. T. Blanco,(1999). “Alkaliactivated fly ashes– A cement for the future”, Cement and Concrete Research, 29 (8): pp. 1323–1329.

[6]. B. VijayaRangan., (2008). “Studies on Fly Ash- Based Geopolymer Concrete”, Malaysian Construction Research Journal, Vol. 03, No. 2.

[7]. Ahmed, M., F., FadhilNuruddin, M., Demie, S., Shafiq, N.,(2011). “Effect of Curing Conditions on Strength of Fly Ash-based Self-Compacting Geopolymer Concrete”. International Journal of Civil & Environmental Engineering, 3(3): p. 183-186.

[8]. KolliRamujee ,M.Potharaju (2013). “Development of mix design for Low calcium based Geopolymer concrete in Ordinary, Standard and high strength grades”. Indian Concrete Institute Journal, Vol. 14, No 2, pp. 29-34.

[9]. IS:2386 (Part-IV)-1963, “Methods of test for aggregates for concrete-mechanical properties”, Bureau of Indian standards, New Delhi.

[10]. IS:383-1970, “Specification for coarse and fine aggregates from natural sources for concrete”, Bureau of Indian standards, New Delhi.

[11]. IS:516-1959, “Methods of test for strength of concrete”, Bureau of Indian standards, New Delhi.

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Research Paper

Acoustic Emission Health Assessment with Artificial Intelligence on the Composite Cross Ply Laminates.

T. Sasikumar* , Nanda Kumar. S**

*-** Centre for Research, Department of Mechanical Engineering, Lord Jegannath College of Engineering and Technology, Tamilnadu, India.

Sasikumar, T., and Kumar, S.N. (2014). Acoustic Emission Health Assessment with Artificial Intelligence on the Composite Cross Ply Laminates. i-manager’s Journal on Structural Engineering, 3(1), 20-26. https://doi.org/10.26634/jste.3.1.2981

Abstract

Three point bending test on cross ply laminates leads to the activation of distinct damage mechanism, such as matrix cracking, de-lamination between the adjutant plies and the fibre breakage at various stages of loading. This study deals with the investigation of the failure of the cross ply laminates using acoustic emission (AE). Broad band AE sensors monitor the elastic waves originating from different sources of failure in coupons while loading. AE signals for different failure modes have been analysed with respect to their parameters. Assessing the health condition of the composite laminate in advance to its ultimate failure while loading is the complimentary method and much useful to the composite industry. AE data acquired until 50% of the ultimate failure load was utilised for both graphical and artificial intelligence prediction of the approximate failure load of composite specimen within the safer limit, so that the structural integrity degradation during the health assessment test is significantly reduced. Both graphical and Artificial Neural Network (ANN) method of prediction have been successfully carried out and good correlation was found between them. Feed forward back propagation network with 22 middle layer neuron was able to map the pattern of failure present in the acoustic emission data and predict the ultimate failure load well in advance.

References

[1]. Nondestructive testing hand book, Vol.5.Ed 2. American society for non-destructive testing (ASNT).

[2]. Mix.P.E: Introduction to non-destructive testing John wiley&Sons.Inc, pp.1-13. (2005)

[3]. Yun.H.D. Choi. W.C,Seo.S.Y: “Acoustic emission activities and damage evaluation of reinforced concrete beams strengthened with CFRP sheets.” NDTE International, 43 (7), pp.615-628, 2010.

[4]. Grosse. C.U. Ohtsu.M. Acoustic emission testing. Springer, 2008.

[5].Kostopoulos.V.Loutos,T.H..Kontsos,A.,Sotiriadis,G. Pappas,Y.Z.. “On the identification of the failure mechanism in Oxide/Oxide composites using acoustic emission.” NDT&E International 36 (8), pp.571-580 (2003).

[6]. Loutas,T.H., Kostopaulos,V., “Health monitoring of carbon/carbon woven reinforced composites damage assessment by using advanced signal processing techniques” Part I: Acoustic emission monitoring and damage mechanism evaluation, Composite Science and Technology, 69 (2), 265-272, 2009.

[7]. Vavouliotis.A. Karapappas.P., Loutas.T.,Votatzi,T. Paipetis,A., Kostopoulos,V. “Multi stage fatigue life monitoring on carbon fibre reinforced polymer enhanced with multiwall carbon nano tubes” Plastics and Rubber Composites 38 (2-4), 124-130(2009).

[8]. Aggelis.D.G.,Shiotani,T., Mononki,S., Hirama,A, “Combined stress wave techniques for damage characterization of composite concrete elements”, American.Concr.Intn.Mater. Journal, 105 (5), pp.469-473 (2009).

[9]. Overgaard, L.C.T., Lund,E., Camanho, P.P, “A methodology for the structural analysis of composite wind turbine blades under geometric and materials induced instabilities”, Composite structures 88(19-20), pp.1092- 1109, 2010.

[10]. Schechinger, B., Vogel,T, “Acoustic emission for monitoring a reinforced concrete beam subjected to four point bending”. Construction and Building Marerials, 21 (3), 483-490 (2009).

[11]. Tobias.A. “Acoustic emission source location in two dimensions by an array of three sensors. Non Destructive Testing, 9(1), pp.9-12, (1976), doi: 10.1016/0029-1021 (76) 90027-x.

[12]. Caprino. G., Lopresto.V.,Leone,C., Papa,I. “Acoustic emission source location in unidirectional carbon fibrereinforced plastic plates with virtually trained artificial neural networks”, J. Applied Poly. Science, 122 (6), pp.3506-3515 (2011).

[13]. Jeony,H., Jang.Y.S., “Wavelet analysis of plate wave propagation in composite laminates”, Composite Structures, 49 (10),443-450, 2000.

[14]. Kundu,T., Das. S., Martin, S.A., Jata,K.V, “Locating point of impact in anisotropic fibre reinforced composite plates”, Ultrasonics, 48(3), pp.193-201 (2008).

[15]. Ativitavas,N, Pothisiri,T., Flower,T.J., (2006), “Identification of fibre reinforced plastic failure mechanism from acoustic emission data using neural networks”. Journal of composite materials, 40 (3), pp.193-226.

[16]. Ely. T.M., Hill,E.V.K, “Longitudinal splitting and fibre breakage characterization in graphite epoxy using acoustic emission data. Material Evaluation, 1995,

[17]. Eric,V.K.Hill, J.L. Walker and G.H, Rowell, “Burst pressure prediction in Graphite/Epoxy pressure vessels using neural networks and acoustic emission amplitude data”. Materials Evacuation, 1995, pp.744-754.

[18]. Marcus. E.Asher, E.V.K.Hill, “Neural network burst pressure prediction in fibre glass epoxy pressure vessels using acoustic emission”, Material Evaluation, 1996, pp.1395-1401.

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Research Paper

GIS Based Bridge Management System (BMS) for Urban Agglomerate

Sandeep Gupta* , Garg P.K., Ghosh S.K*

* Department of Civil Engineering, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India.

-* Department of Civil Engineering, Indian Institute of Technology, Roorkee, Uttarakhand, India.

Gupta, S., Garg, P.K., and Ghosh, S.K. (2014). GIS Based Bridge Management System (BMS) for Urban Agglomerate. i-manager’s Journal on Structural Engineering, 3(1), 27-31. https://doi.org/10.26634/jste.3.1.2982

Abstract

A Bridge Management System (BMS) has been developed as a component of Road Information and Management System (RIMS) for Dehradun urban agglomerate with the main objective to obtain accurate, up-to-date bridge information which may result in proper management of the bridges. It provides bridge information, like geographical location, structure, width, railing type, footpath and bridge maintenance requirements on visual inspection basis etc. The geospatial techniques, like GIS and GPS make the process of data collection and analyses easy, faster and economical. This paper also outlines the data requirements for the development of a BMS.

References

[1]. Hallberg, D., Racutanu, G., (2007). Development of the Swedish bridge management system by introducing a LMS concept. Materials and Structures, Vol 40, pp. 627- 639.

[2]. Orndoff, C. J. W., Vasudevan, R. (2003). Web-Based Bridge Management System. 9th International Bridge Management Conference. Orlando, Florida, 239-243,

[3]. Miyamoto, A., Kawamura, K., Nakamura, H., (2001). Development of Bridge Management System for Existing Bridges. Advances in Engineering Software. Vol 32, pp.821- 833. ed Major Repair

[4]. http://uppwd.up.nic.in/ids/6%20IT%20Unit/Report% 20No.%202%20(Final) .pdf.

[5]. http://www.prbdb.gov.in/files/Presentations/TS2/ Asset%20Management%20d%an20Computerization . pps #292,23,Data Collection.

[6]. http://publications.piarc.org/ressources/documents /actes-seminaires08/C41-inde0308/TS1-Sahu.pdf

[7]. http://evenfitconsult.com/PDF/8.towards%20The%20 development%20of%20a%20gis%20based%20bridge %20management%20 System %20BMS.pdf

i-manager's Journal on Structural Engineering (JSTE)

Current Issue Vol. 12 Issue 3

Most Read

Most Cited

Volume 3 Issue 1 March - May 2014

Comparative Analysis of Elevated Water Storage Structure using Different Typesof Bracing Patterns in Staging

Gaurav S. Atalkar* , 0**

* M.Tech Student, Civil Engineering Department, ShriRamdeobaba College of Engineering & Management, Nagpur, India. ** Assistant Professor, Civil Engineering Department,ShriRamdeobaba College of Engineering & Management, Nagpur, India.

Atalkar, G.S., and Gharad, A.M. (2014). Comparative Analysis of Elevated Water Storage Structure using Different Types of Bracing Patterns in Staging. i-manager’s Journal on Structural Engineering, 3(1), 1-6. https://doi.org/10.26634/jste.3.1.2978

Abstract

References

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Modelling Annual Rainfall of Krishna and Godavari River Basins using Extreme Value Type-1 Distribution

N. Vivekanandan*

*Assistant Research Officer, Hydrometeorology Division, Central Water and Power Research Station, Pune, Maharashtra, India.

Vivekanandan, N. (2014). Modelling Annual Rainfall of Krishna and Godavari River Basins using Extreme Value Type-1 Distribution. i-manager’s Journal on Structural Engineering, 3(1), 7-12. https://doi.org/10.26634/jste.3.1.2979

Abstract

References

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Parametric Optimization of Alkali Activated F-Type Flyash-Based Geopolymer Concrete

Kolli Ramujee* , P. Hanumantha Rao**, M. Potharaju***, Radhakrishna****

Ramujee, K., Hanumantharao, R., Potharaju, M., and Radhakrishna (2014). Parametric Optimization of Alkali Activated F-Type Flyash-Based Geopolymer Concretee. i-manager’s Journal on Structural Engineering, 3(1), 13-19. https://doi.org/10.26634/jste.3.1.2980

Abstract

References

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Acoustic Emission Health Assessment with Artificial Intelligence on the Composite Cross Ply Laminates.

T. Sasikumar* , Nanda Kumar. S**

*-** Centre for Research, Department of Mechanical Engineering, Lord Jegannath College of Engineering and Technology, Tamilnadu, India.

Sasikumar, T., and Kumar, S.N. (2014). Acoustic Emission Health Assessment with Artificial Intelligence on the Composite Cross Ply Laminates. i-manager’s Journal on Structural Engineering, 3(1), 20-26. https://doi.org/10.26634/jste.3.1.2981

Abstract

References

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GIS Based Bridge Management System (BMS) for Urban Agglomerate

Sandeep Gupta* , Garg P.K.**, Ghosh S.K***

* Department of Civil Engineering, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India. **-*** Department of Civil Engineering, Indian Institute of Technology, Roorkee, Uttarakhand, India.

Gupta, S., Garg, P.K., and Ghosh, S.K. (2014). GIS Based Bridge Management System (BMS) for Urban Agglomerate. i-manager’s Journal on Structural Engineering, 3(1), 27-31. https://doi.org/10.26634/jste.3.1.2982

Abstract

References

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* M.Tech Student, Civil Engineering Department, ShriRamdeobaba College of Engineering & Management, Nagpur, India.

** Assistant Professor, Civil Engineering Department,ShriRamdeobaba College of Engineering & Management, Nagpur, India.

Kolli Ramujee* , P. Hanumantha Rao, M. Potharaju*, Radhakrishna****

Sandeep Gupta* , Garg P.K., Ghosh S.K*

* Department of Civil Engineering, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India.

-* Department of Civil Engineering, Indian Institute of Technology, Roorkee, Uttarakhand, India.