i-manager Publications

i-manager's Journal on Civil Engineering (JCE)

Current Issue Vol. 13 Issue 4

An Experimental Investigation to Determine the Failure Load of Optimal Hammer Head Pier Cap and Interpolate using Univariate Splines Machine Learning Algorithm
Assessment of Radius of Curvature along the Existing Road Networks
Sustainable Urban Infrastructure: A Comprehensive Review of Environmental Safety Practices in Civil Engineering
Experimental Investigation on the Influence of Recycled Aggregate on the Durability and Mechanical Properties of Concrete
A Step by Step Analysis to Solve the Equation of Motion in Space and Time using Basis Splines - A Class Room Example

Most Cited

Volume 5 Issue 2 March - May 2015

Research Paper

PCI based Maintenance with Nonlinear Deterioration Rate

Rafiqul A. Tarefder* , Md Mostaqur Rahman**

* Professor, Department of Civil Engineering, University of New Mexico, Albuquerque, New Mexico, USA.

** Graduate Research Assistant, Department of Civil Engineering, University of South Carolina,Columbia, South Carolina, USA.

Tarefder,R.A., and Rahman,M. (2015). PCI based Maintenance with Nonlinear Deterioration Rate. i-manager’s Journal on Civil Engineering, 5(2), 1-8. https://doi.org/10.26634/jce.5.2.3348

Abstract

In this study, maintenance solutions for 19 airport pavements in New Mexico are derived based on Pavement Condition Index (PCI) and nonlinear deterioration rate. In a Pavement Management System (PMS), PCI indicates the functional condition of the pavement. In this study, a specific maintenance treatment is applied when the PCI value of a pavement section reaches a minimum defined value or cutoff value. Using system dynamics modeling, modules to quantify the benefit and Life Cycle Cost (LCC) were developed and utilized to determine the relative benefit and life cycle treatment cost of a maintenance solution or treatment. This study indicates that airports with higher initial PCI have lower functional benefit and lower LCC for maintenance solutions of different PCI improvement or PCI rises. Benefit and cost are determined using two different system dynamic modules developed in Powersim and then benefit and cost are compared using developed design charts. Benefit cost ratio (BCR) design charts are capable of showing the BCR for airport pavements having initial PCI 30 to 80, cutoff-PCI 10 to 80 and rise 10 to 40. For PCI rise 30 and 40, initial PCI 60, 70 and 80 have shown almost the same BCR for a different cutoff-PCI.

References

[1]. ASTM Standard D5340 (2012). Standard test method for airport pavement condition index surveys.

[2]. ASTM Standard D6433 (2011). Standard practice for roads and parking lots pavement condition index surveys.

[3]. Friedman, S. (2003). The effect of dynamic decision making on resource allocation: The case of pavement management, Dissertation of Ph.D. Program, Worcester Polytechnic Institute.

[4]. Greene, J., Shahin, M. Y., and Alexander, D. R. (2004). “Airfield Pavement Condition Assessment”, Transp. Res. Rec., J. of the TRB, 1889, pp.63–70.

[5]. Haider, S. W., and Dwikat, M. B. (2011). “Estimating Optimum Timing for Preventive Maintenance Treatment to Mitigate Pavement Roughness”, Transp. Res. Rec., J. of the TRB, 2235, pp.43–53.

[6]. Hajj, E. Y., Loria, L., and Sebaaly, P. E. (2011). “Optimum Time for Application of Slurry Seal to Asphalt Concrete Pavement”, Transp. Res. Rec., J. of the TRB, 2235, pp.66–81.

[7]. Hass, R., Hudson, W. R., and Zaniewski, J. (1994). “Modern pavement management”, Krieger Publishing Company, Malabar, Florida.

[8]. Hicks, R. G., Seeds, S. B., and Peshkin, D. G. (2000). “Selecting a Preventive Maintenance Treatment for Flexible Pavements”, Foundation for Pavement Preservation, pp.01–43.

[9]. Linard, K. T. (2000). “Application of system dynamics to st pavement maintenance optimisation”. 1 Inter. Conf. on Sys. Thinking in Management.

[10]. Morlan, D. A. (2011). “Cost benefit analysis of including microsurfacing in pavement treatment strategies and cycle maintenance”, Commonwealth of Pennsylvania Department of Transportation.

[11]. Ningyuan, L., Kazmierowski. T., Tighe, S., and Hass, R. (2001). “Integrating dynamic performance prediction models into pavement management maintenance and th rehabilitation programs”. 5 Inter. Conf. on Managing Pavement.

[12]. Peshkin, D., Hoerner, T., and Zimmerman, K. (2004). “The optimal time for preventive maintenance: concepts and practice”. 6th International Conference on Managing Pavements.

[13]. Shahin, M. Y. (2005). “Pavement management for airports, roads, and parking lots”, Springer Publication, New York.

[14]. Smadi, O. (2004). “Quantifying the benefit of th pavement management”, Proc., 6 Inter. Conf. of Managing Pavement.

[15]. Walls, J., and Smith, M. (1998). “Life-cycle cost analysis in pavement design–In search of better investment decisions”, Interim Tech. Bulletin, Publication No. FHWA-SA-98-079, FHWA, Washington, D.C.

[16]. Zaniewski, J. (1991). “Unified methodology for airport pavement analysis and design”, State of the Art Tech. Report, FAA, Research and Development Service, Washington, D.C., 1, pp.137–150.

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Research Paper

Design and Analysis of Roller Compacted Concrete Pavements for Low Volume Roads in India

S. Krishna Rao* , P. Sravana, 0*

Research Scholar, Jawaharlal Nehru Technological University, Hyderabad, Telangana, India

Professor, Jawaharlal Nehru Technological University, Hyderabad, Telangana, India

Professor, Daita Madhusudana Sastry Sri Venkateswara Hindu College of Engineering, Machilipatnam. AP, India

Rao,K.S., Sravana.P., and Rao,C.T. (2015). Design and Analysis of Roller Compacted Concrete Pavements for Low Volume Roads in India. i-manager’s Journal on Civil Engineering, 5(2), 9-15. https://doi.org/10.26634/jce.5.2.3349

Abstract

Roller Compacted Concrete (RCC) is an innovative pavement material for the construction of low volume rural roads. RCC can easily overcome the problems usually observed in the construction of flexible bituminous pavements. RCC is the commercial name used for concrete placed with conventional hot mix bituminous paving equipment compacted with vibratory rollers.RCC pavements are highly rigid and hence eliminates the high deformation problems such as rutting and corrugations generally encountered in flexible pavements. For rural development in India, connectivity of rural roads is an important aspect; but many rural roads such as ODR (Other District Roads) and VR (Village Roads) are of poor quality, potholed, and unable to withstand the loads of heavy farm equipment. Two construction techniques are available i.e., rigid and flexible. Of these, selection of type of construction depends on the sub-grade soil types, rainfall, traffic pattern and availability of construction materials. In the present paper, the design and analysis of RCC Pavements have been considered in place of conventional Cement Concrete Pavements and Bituminous pavements. The flexural strengths of Roller compacted concrete of 4.5MPa, 5.0MPa and 5.5MPa are considered for design and analysis. Design curves for low volume roads are presented. Proposed RCC pavement is suitable for sub-grade having low modulus of reaction.

References

[1]. IRC SP:62-2014 (2014). “Guidelines for the Design and Construction of Cement Concrete Pavements for LowVolume Roads”, The Indian Roads Congress, 2004..

[2]. IRC: SP-20, (2002). Rural Roads Manual, Indian Roads Congress, New Delhi, India.

[3]. R.K. Srivastava, S.K. Duggal, K.K. Shukla, (2012). “Reinforced Cement Concrete Pavement For Village Roads In Alluvial Region: A Sustainable Option”, Highway Research Journal. Vol.5, No.2, pp.19-26..

[4]. IRC: 58-2002 (2002). “Guidelines for Design of Plain Jointed Rigid Pavements for Highways”, The Indian Road Congress 2002..

[5]. Pandey, B.B., (2006). “Low Cost Concrete Roads for Villages”, 3. Grameensampark, National Rural Road Development Agency, Ministry of Rural Development, Government of India, Vol. II, No. 1 &2, pp.14-15..

[6]. IRC: 37-2001 (2001). “Guidelines for the design of flexible pavement (second Revision)”, The Indian Road Congress 2001.

[7]. Palmer, William D., Jr., (2005). “Paving with roller compacted concrete: RCC topped with asphalt quickly provides a durable street”, Concrete Construction – World of Concrete, Vol. 50, No. 2, pp. 45-50..

[8]. IRC: SP: 72-2007. “Guidelines for the Design of Flexible Pavements for Low Volume Rural Roads”, Indian Road Congress, New Delhi

[9]. IRC: 101-1988. “Guidelines for Design of Continuously Reinforced Cement Concrete Pavements with elastic joints”, Indian Roads Congress, New Delhi

[10]. ACI 325.10R-95 “Report on Roller-Compacted Concrete Pavements”, Reported by ACI Committee 325

[11]. National Concrete Pavement Technology Center, (2010). “Guide for Roller Compacted Concrete Pavements”, August 2010. Iowa State University's Institute for Transportation, with funding from the Portland Cement Association

[12]. Prusinski, J., (1997). “Roller-Compacted Concrete Carries a Heavy Load”, Roads & Bridges, Scranton Gillette Communications Inc., Des Plaines, IL, USA, July, pp. 68-69.

[13]. ACI 211 3R-02, Guide for Selecting Proportions for No- Slump Concrete, 2002

[14]. IS 2720-8 (1983). Methods of test for soils, Part 8: Determination of water content-dry density relation using heavy compaction.

[15]. IS 516 (1959). Method of Tests for Strength of Concrete

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Research Paper

Prediction of Compressive Strength of Concrete by Data-Driven Models

Faezehossadat Khademi* , Mahmoud Akbari, Sayed Mohammadmehdi Jamal*

* Graduate Student, Civil, Architectural, and Environmental Engineering Department, Illinois Institute of Technology, Chicago, USA.

Assistant Professor, Civil Engineering Department, University of Kashan, Kashan, Iran.

* Graduate Student, Civil Engineering Department, University of Hormozgan, Hormozgan, Iran.

Khademi,F., Akbari,M., and Jamal,S,M. (2015). Prediction of Compressive Strength of Concrete by Data-Driven Models. i-manager’s Journal on Civil Engineering, 5(2), 16-23. https://doi.org/10.26634/jce.5.2.3350

Abstract

The aim of this study is prediction of 28-day compressive strength of concrete by data-driven models. Hence, by considering concrete constituents as input variables, two data-driven models namely Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models are constructed for the purpose of predicting the 28-days compressive strength of different concrete mix designs. Comparing the two models illustrates that MLR model is not a suitable model for predicting the compressive strength; however, ANN can be used to efficiently predict the compressive strength of concrete.

References

[1]. Alshihri, M. M., Azmy, A. M., & El-Bisy, M. S. (2009). “Neural networks for predicting compressive strength of structural light weight concrete”. Construction and Building Materials, Vol.23, No.6,pp. 2214-2219.

[2]. Atici, U. (2011). “Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network”. Expert Systems with applications, Vol. 38, No. 8, pp.9609-9618.

[3]. Bilim, C., Atis, C. D., Tanyildizi, H., & Karahan, O. (2009). “Predicting the compressive strength of ground granulated blast furnace slag concrete using artificial neural network”. Advances in Engineering Software, Vol.40, No.5, pp.334-340.

[4]. Deepa, C., SathiyaKumari, K., & Sudha, V. P. (2010). “Prediction of the compressive strength of high performance concrete mix using tree based modeling”. International Journal of Computer Applications, Vol.6, No.5, pp.18-24.

[5]. Duan, Z. H., Kou, S. C., & Poon, C. S. (2013). “Using artificial neural networks for predicting the elastic modulus of recycled aggregate concrete”. Construction and Building Materials, Vol.44, pp.524-532.

[6]. Hola, J., & Schabowicz, K. (2005). “Application of artificial neural networks to determine concrete compressive strength based on non-destructive tests”. Journal of Civil Engineering and Management, Vol.11, No.1, pp. 23-32.

[7]. Jang, J. S. R., & Sun, C. T. (1996). Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Prentice-Hall, Inc.

[8]. Jeong,.D. I. and Kim, Y.O. (2005). “Rainfall- Runoff Models Using Artificial Neural Networks for Ensemble Streamflow Prediction”, Hydrol. Process. Vol.19, pp.3819–3835.

[9]. Kewalramani, M. A., & Gupta, R. (2006). “Concrete compressive strength prediction using ultrasonic pulse velocity through artificial neural networks”. Automation in Construction, Vol.15, No.3, pp.374-379.

[10]. Kosmatka, S. H., & Panarese, W. C. (2002). “Design and control of concrete mixtures”. Portland Cement Association.

[11]. Mansour, M. Y., Dicleli, M., Lee, J. Y., & Zhang, J. (2004). “Predicting the shear strength of reinforced concrete beams using artificial neural networks”, Engineering Structures, Vol.26, No.6, pp.781-799.

[12]. Naderpour, H., Kheyroddin, A., & Amiri, G. G. (2010). “Prediction of FRP-confined compressive strength of concrete using artificial neural networks”. Composite Structures, Vol.92, No.12, pp.2817-2829.

[13]. Ni, H. G., & Wang, J. Z. (2000). “Prediction of compressive strength of concrete by neural networks”. Cement and Concrete Research, Vol.30, No.8, pp.1245- 1250.

[14]. Özcan, F., Atis, C. D., Karahan, O., Uncuoglu, E., & Tanyildizi, H. (2009). “Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete”. Advances in Engineering Software, Vol.40, No.9, pp.856-863.

[15]. Öztas, A., Pala, M., Özbay, E., Kanca, E., Çag?lar, N., & Bhatti, M. A. (2006). “Predicting the compressive strength and slump of high strength concrete using neural network”. Construction and Building materials, Vol.20, No.9, pp.769-775.

[16]. Pala, M., Özbay, E., Öztas, A., & Yuce, M. I. (2007). “Appraisal of long-term effects of fly ash and silica fume on compressive strength of concrete by neural networks”. Construction and Building Materials, Vol.21, No.2, pp.384-394.

[17]. Popovics, S. (1998). “History of a mathematical model for strength development of Portland cement concrete”. ACI Materials Journal, Vol.95, No.5.

[18]. Sadrmomtazi, A., Sobhani, J., & Mirgozar, M. A. (2013). “Modeling compressive strength of EPS lightweight concrete using regression, neural network and ANFIS”. Construction and Building Materials, Vol.42, pp.205-216.

[19]. K. Samrajyam, B. Sobha, T. D. Gunneswara Rao and R.L.N. Sai Prasad (2014). “Plastic Optic Fiber (POF) Based Phase Difference Measurement Method for Estimation Of Crack Mouth Opening Displacement (CMOD) in Concrete”. i-manager's Journal on Civil Engineering, 4(2), Mar-May 2014, Print ISSN 2231- 1068, E-ISSN 2249-0779, pp. 13-19.

[20]. S. Seshaphani, Seshadri Sekhar.T, Srinivasa Rao, Sravana and Sarika. P (2013). “Studies on Strength, Acid Attack and Sulphate Attack of High Strength Self Compacting Concrete Using Mineral Admixtures”, imanager's Journal on Civil Engineering, 3(1) Dec-Feb, 2013, Print ISSN 2231- 1068, E-ISSN 2249-0779, pp.30-34 .

[21]. Sobhani, J., Najimi, M., Pourkhorshidi, A. R., & Parhizkar, T. (2010). “Prediction of the compressive strength of no-slump concrete: a comparative study of regression, neural network and ANFIS models”. Construction and Building Materials, Vol. 24, No.5, pp.709-718.

[22]. Tsivilis, S., & Parissakis, G. (1995). “A mathematical model for the prediction of cement strength”. Cement and Concrete research, Vol.25, No.1, pp.9-14.

[23]. T. Senthil Vadivel, R. Thenmozhi and M. Doddurani (2013). “Analyzing the Behaviour of Concrete with Waste Ceramic as Fine Aggregate”. i-manager's Journal on Civil Engineering, 3(1) Dec-Feb, 2013, Print ISSN 2231- 1068, EISSN 2249-0779, pp.25-29.

[24]. Yeh, I. C. (2007). “Modeling slump flow of concrete using second-order regressions and artificial neural networks”. Cement and Concrete Composites, Vol.29, No.6, pp.474-480.

[25]. Zain, M. F. M., & Abd, S. M. (2009). Multiple regression model for compressive strength prediction of high performance concrete. Journal of Applied Sciences, Vol.9, No.1, pp.155-160.

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Research Paper

Water Requirements of Selected Crops in Kunigal Command Area

Nithya B.K* , Shreedhar R, A.V.Shivapur*

P.G. Student, Water and Land Management, Visvesvaraya Technological University, Belagavi, Karnataka, India.

Associate Professor, Civil Engineering Department, KLS Gogte Institute of Technology, Belagavi, Karnataka, India.

Professor, Water and Land Management, Visvesvaraya Technological University, Belagavi, Karnataka, India.

Nithya.B.K., Shreedhar .R., and Shivapur.A.V. (2015). Water Requirements of Selected Crops in Kunigal Command Area.i-manager’s Journal on Civil Engineering, 5(2), 24-30. https://doi.org/10.26634/jce.5.2.3351

Abstract

A study is carried out to determine the crop water requirement of some selected crops for the command area in Kunigal taluk. These crops include rice, pulses, groundnut, sugarcane and millet (ragi). Crop water requirement for each of the crops is determined by using 30-year climatic data in CROPWAT. Reference crop evapotranspiration (ETo) is determined using the FAO Penman Monteith method. For all the crops considered, three decades: decades I, II, and III and seven crop growth stages: nursery, nursery / land preparation, land preparation, initial stage, development stage, mid-season and late season stage are considered. The study shows that for the area under study, reference evapotranspiration (ETo) varied from 3.11 to 5.29 mm/day. Crop evapotranspiration (ETc) and crop water requirement for sugarcane varied from 1.61mm/day to 3.46mm/day and 0.0 mm/dec to 51.9 mm/dec, for ragi (millet) from 1.44 mm/day to 3.46 mm/day and 0.0 mm/dec to 2.6 mm/dec, for groundnut( rabi) from 0.66 mm/day to 4.45 mm/day and 0.0 mm/dec to 43.9 mm/dec, for groundnut (kharif) from 1.78 mm/day to 4.34 mm/day and 0.0 mm/dec to 14.6 mm/dec, for rice from 0.39 mm/day to 4.82 mm/day and 1.5 mm/dec to 184.9 mm/dec, and for pulses from 2.41 mm/day to 4.22 mm/day and 13.2 mm/dec to 38 mm/dec respectively. The gross water requirement is 939.14 mm/year with an application efficiency of 70%. Therefore the entire land area of 6572 ha requires 61.72 MCM. Thus the dam can conveniently supply the water required for irrigation in the area.

References

[1]. Michael AM (1999). “Irrigation Theory and Practice”. Vikas Publishing House, New Delhi, India. pp 530-539.

[2]. Hess T (2005). “Crop Water Requirements, Water and Agriculture, Water for Agriculture”, WCA infoNET, http:// silsoe.cranfield.ac.uk/iwe/dailyet.htm.

[3]. Food and Agriculture Organization (FAO) (1992). “CROPWAT: A Computer Program for Irrigation Planning and Management”, by M. Smith. FAO Irrigation and Drainage Paper No. 46. Rome.

[4]. Broner I, Schneekloth J (2003). “Seasonal Water Needs and Opportunities for Limited Irrigation for Colorado Crops”, Newsletter of the Extension Irrigation Services, Dept. of Civil Engineering, Colorado State University. No. 4.718, http:/www.google search/water requirement.

[5]. Adeniran K.A., Amodu M.F., Amodu M.O., and Adeniji F.A., (2010). “Water requirements of some selected crops in Kampe dam irrigation project”, Australian Journal of Agricultural Engineering (AJAE), Vol. 1, No.4, pp.119-125.

[6]. Jehanger WA, Turral H and Masih I (2004). “Water Productivity of Rice Crop in Irrigated Areas”, Proceedings of the 4th International Crop Science Congress, Brisbane, Australia (26 Sept – 1 Oct 2004) http:/www.cropscience. org.au

[7]. Luca E, Nagy Z, Berchez M (2003). “Water Requirements of the Main Field Crops in Transylvania”. Journal of Central European Agriculture, Vol. 3, No.2. pp 98-102

[8]. Saravanam K and Saravanam R, (2014). “Determination of Water Requirements of Main crops in the Tank Irrigation Command area using CROPWAT 8.0”, International Journal of Interdisciplinar y and Multidisciplinary Studies (IJIMS), Vol. 1, No.5, pp.266-272.

[9]. Food and Agriculture Organization (FAO) (2002). “Crop Evapotranspiration Guidelines for Computing Crop Water Requirements: Guidelines for Computing Crop Water Requirements”. (FAO Irrigation and Drainage Paper No.56).

[10]. Food and Agriculture Organization (FAO) (2005). “Irrigation water requirements, In: Irrigation Potential in Africa: A Basin Approach”, Chapter 5, FAO Corporate Document Repository, FAO, Rome. http://www.fao.org/ docrep/W4347E /w4347e00.htm

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Research Paper

Optimization of Pipe Network Design using Genetic Algorithm

Sanjay Kumar Srivastava* , Rajesh Kumar, Prabhat Kumar Singh Dikshit*

Ph. D. Scholar, Department of Civil Engineering. IIT (BHU), Varanasi, India.

Associate Professor, Department of Civil Engineering. IIT (BHU), Varanasi, India.

*** Professor, Department of Civil Engineering. IIT (BHU), Varanasi, India.

Srivastava,S,K., Kumar,R., and Dikshit,P,K,S. (2015). Optimization of Pipe Network Design using Genetic Algorithm. i-manager’s Journal on Civil Engineering, 5(2), 31-38. https://doi.org/10.26634/jce.5.2.3352

Abstract

A good water distribution system (WDS) continues to deliver water at all nodes of pipe network to fulfill various water pressure and demand conditions. In this paper, the authors used Genetic Algorithms (GA), a methodology for optimizing pipe networks. A computational code has been developed using Matlab software for optimization of pipe network. To ensure the validity of the code developed, it was tested with the Gessler (1985) pipe network design. This tested code can be further used for design and analysis of pipe network.

References

[1]. Alperovits, E. and Shamir, U. (1977). “Design of optimal water distribution systems”. Water Resour. Res., Vol.13, No.6, pp.885-900.

[2]. Dandy, G.C., Simpson, A.R. and Murphy, L.J. (1996). “An improved genetic algorithm for pipe network optimization”. Water Resour. Res., Vol.32, No.2, pp.449- 458.

[3]. Deb, K.(2000). “An efficient constraint handling method for genetic algorithms”. Computer Methods in Applied Mechanics and Engineering, Vol.186, No.2-4, pp.311-338.

[4]. Fujiwara, O. and Khang, D.B. (1990). “A two-phase decomposition method for optimal design of looped water distribution networks.” Water Resources Research, Vol. 26, No.4, pp.539-549.

[5]. Gessler, J. (1985). “Pipe network optimization by enumeration”. Proc., Computer Applications for Water Resources, ASCE, New York, NY, pp.572-581.

[6]. Gupta, I., Bassin, J.K., Gupta, A. and Khanna, P.(1993). “Optimization of water distribution system”. Environmental Software, Vol.8, pp.101–113.

[7]. Lansey, K.E. and Mays, L.W. (1989). “Optimization model for water distribution system design”. J. Hydr. Engg. ASCE, Vol.115, No.(10, pp.1401–1418.

[8]. Morgan, D.R. and Goulter, I.C. (1985). “Optimal urban water distribution design”. Water Resour. Res., Vol.21, No.5, pp.642-652.

[9]. Quindry, G.E., Liebman, J.C. and Brill, E.D. (1981). “Optimization of looped water distribution systems.” Journal of the Environmental Engineering Division, Vol.107, No.4, pp.665-679.

[10]. Samani, H. M. V. and Mottaghi, A. (2006). “Optimization of water distribution networks using integer linear programming.” Journal of Hydraulic Engineering, Vol.132, No.5, pp.501-509.

[11]. Simpson, A.R., Dandy, G.C. and Murphy, L.J. (1994). “Genetic algorithms compared to other techniques for pipe optimization. Journal of Water Resources Planning and Management, Vol.120 , No.4, pp.423-443.

[12]. Stephenson, D. (1984). “Pipeflow analysis: optimum design of branched pipe networks by linear programming.” Developments in Water Science, Elsevier pp.55-69.

[13]. Vairavamoorthy, K. and Ali, M. (2000). “Optimal design of water distribution systems using genetic algorithms”. Computer-Aided Civil and Infrastructure Engineering, Vol.15, No.5, pp.374-382.

[14]. Zheng, F.,Simpson, A. R. and Zecchin, A. C. (2011e). “Parametric analysis of differential evolution algorithm applied to water distribution system optimization.” Proc., 11th International Conference on Computing and Control for the Water Industry (CCWI 2011), Exeter, UK.

i-manager's Journal on Civil Engineering (JCE)

Current Issue Vol. 13 Issue 4

Most Read

Most Cited

Volume 5 Issue 2 March - May 2015

PCI based Maintenance with Nonlinear Deterioration Rate

Rafiqul A. Tarefder* , Md Mostaqur Rahman**

* Professor, Department of Civil Engineering, University of New Mexico, Albuquerque, New Mexico, USA. ** Graduate Research Assistant, Department of Civil Engineering, University of South Carolina,Columbia, South Carolina, USA.

Tarefder,R.A., and Rahman,M. (2015). PCI based Maintenance with Nonlinear Deterioration Rate. i-manager’s Journal on Civil Engineering, 5(2), 1-8. https://doi.org/10.26634/jce.5.2.3348

Abstract

References

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Design and Analysis of Roller Compacted Concrete Pavements for Low Volume Roads in India

S. Krishna Rao* , P. Sravana**, 0***

*Research Scholar, Jawaharlal Nehru Technological University, Hyderabad, Telangana, India **Professor, Jawaharlal Nehru Technological University, Hyderabad, Telangana, India *** Professor, Daita Madhusudana Sastry Sri Venkateswara Hindu College of Engineering, Machilipatnam. AP, India

Rao,K.S., Sravana.P., and Rao,C.T. (2015). Design and Analysis of Roller Compacted Concrete Pavements for Low Volume Roads in India. i-manager’s Journal on Civil Engineering, 5(2), 9-15. https://doi.org/10.26634/jce.5.2.3349

Abstract

References

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Prediction of Compressive Strength of Concrete by Data-Driven Models

Faezehossadat Khademi* , Mahmoud Akbari**, Sayed Mohammadmehdi Jamal***

Khademi,F., Akbari,M., and Jamal,S,M. (2015). Prediction of Compressive Strength of Concrete by Data-Driven Models. i-manager’s Journal on Civil Engineering, 5(2), 16-23. https://doi.org/10.26634/jce.5.2.3350

Abstract

References

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Water Requirements of Selected Crops in Kunigal Command Area

Nithya B.K* , Shreedhar R**, A.V.Shivapur***

Nithya.B.K., Shreedhar .R., and Shivapur.A.V. (2015). Water Requirements of Selected Crops in Kunigal Command Area.i-manager’s Journal on Civil Engineering, 5(2), 24-30. https://doi.org/10.26634/jce.5.2.3351

Abstract

References

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Optimization of Pipe Network Design using Genetic Algorithm

Sanjay Kumar Srivastava* , Rajesh Kumar**, Prabhat Kumar Singh Dikshit***

** Ph. D. Scholar, Department of Civil Engineering. IIT (BHU), Varanasi, India. ** Associate Professor, Department of Civil Engineering. IIT (BHU), Varanasi, India. *** Professor, Department of Civil Engineering. IIT (BHU), Varanasi, India.

Srivastava,S,K., Kumar,R., and Dikshit,P,K,S. (2015). Optimization of Pipe Network Design using Genetic Algorithm. i-manager’s Journal on Civil Engineering, 5(2), 31-38. https://doi.org/10.26634/jce.5.2.3352

Abstract

References

Full Article (HTML)

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* Professor, Department of Civil Engineering, University of New Mexico, Albuquerque, New Mexico, USA.

** Graduate Research Assistant, Department of Civil Engineering, University of South Carolina,Columbia, South Carolina, USA.

S. Krishna Rao* , P. Sravana, 0*

Research Scholar, Jawaharlal Nehru Technological University, Hyderabad, Telangana, India

Professor, Jawaharlal Nehru Technological University, Hyderabad, Telangana, India

Professor, Daita Madhusudana Sastry Sri Venkateswara Hindu College of Engineering, Machilipatnam. AP, India

Faezehossadat Khademi* , Mahmoud Akbari, Sayed Mohammadmehdi Jamal*

Nithya B.K* , Shreedhar R, A.V.Shivapur*

Sanjay Kumar Srivastava* , Rajesh Kumar, Prabhat Kumar Singh Dikshit*

Ph. D. Scholar, Department of Civil Engineering. IIT (BHU), Varanasi, India.

Associate Professor, Department of Civil Engineering. IIT (BHU), Varanasi, India.

*** Professor, Department of Civil Engineering. IIT (BHU), Varanasi, India.