i-manager Publications

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

Current Issue Vol. 14 Issue 2

Behavioral Studies on Sorptivity of the Concrete Blended with Nano Silica
Optimization of Lane Based Signalized Intersections through VISSIM at Outer Ring Road Bengaluru
Trend Analysis of Rainfall Data using Mann-Kendall Test and Sen's Slope Estimator
A Review on Sustainable Utilization of Bauxite Residue (Red Mud) for the Production of Mortar and Concrete
A Critical Review of Experimental Research on the Durability of Cement Modified with Partial Steel Slag Replacement

Most Cited

Volume 10 Issue 1 December - February 2020

Research Paper

Comparative Thermal Performances of Different Envelope Roof Designs for Sustainable Solar Cooling

Osamuyi Obadolagbonyi* , Ben Oni, Mandoye Ndoye*

, Tuskegee University, Tuskegee, Alabama.

Department of Electrical and Computer Engineering, Tuskegee University, Tuskegee, Alabama.

Obadolagbonyi, O., Oni, B., & Ndoye, M. (2020). Comparative Thermal Performances of Different Envelope Roof Designs for Sustainable Solar Cooling, i-manager's Journal on Civil Engineering, 10(1), 1-12. https://doi.org/10.26634/jce.10.1.16829

Abstract

Countries in the world's hot climatic areas have an abundance of sunshine. While the heat component of solar radiation, along with humidity produces ambient discomfort, its visible light component has great potential for cooling applications in building interior thermal comfort. In this study, the traditional building practice in Sub-Saharan Africa (SSA) has been reviewed. Novel heat flow reduction strategies were investigated with the goal of identifying the best roof design that will minimize heat transmission from roof to building interior and thus make solar cooling more economically-feasible. Scaled-down structures of roof envelopes were constructed using the typical SSA metallic roofing materials. The single roof construction that is traditionally implemented in SSA was replicated for reference. Double roof constructions with different configurations were also constructed for comparison with the typical SSA single roof construction. When compared to the reference “traditional single roof”, the “single roof with radiant heat reflector underneath” configuration with 3.5” (8.89cm) air space separation, lowered the attic peak temperature by 12.26 degree F, and reduced the 24-hr stored interior thermal energy by 66%.

References

[1]. Kylili, A., Theodoridou, M., Ionnou, I.,& Fokaides, P. A. (2016) Numerical heat transfer analysis of phase change material (PCM) - ehanced plasters, In Proceedings of the 2016 COSMOL Conference in Munich. https://doi.org/10.1088/1742-6596/796 /1/012023

[2]. Abraham, J., & George, C. (2007). Full-building radiation shielding for climate control in desert regions. International Journal of Sustainable Energy, 26(3), 167-177. https://doi.org/10.1080/14786450701679381

[3]. Asan, H. (2006). Numerical computation of time lags and decrement factors for different building materials. Building and environment, 41(5), 615-620.https://doi.org /10.1016/j.buildenv.2005.02.020

[4]. Bortoloni, M., Bottarelli, M., & Piva, S. (2017, January). Summer thermal performance of ventilated roofs with tiled coverings. In Journal of Physics: Conference Series (Vol. 796, No. 1, p. 012023). IOP Publishing.

[5]. Cengel, Y. A. (2003). A practical approach. Mc-Graw Hill Education, Columbus, GA, USA.

[6]. Cummings, J. B. (1991). Evaluation of Temperature and Energy Impacts of Insulating Coatings Corporation Astec # 100 White Roof System. Insulating Coatings Corporation.

[7]. Engineering Toolbox. https://www.engineeringtoolbox. com/cooling-heating-equationsd_747. html

[8]. Jin, X., Zhang, X., Cao, Y., & Wang, G. (2012). Thermal performance evaluation of the wall using heat flux time lag and decrement factor. Energy and Buildings, 47, 369- 374.https://doi.org/10.1016/j.enbuild.2011.12.010

[9]. MacDonald, R. D., Armstrong, M., & Fritz, B. (2001). Reducing Solar Heat Gain with Ceramic Coatings. University of Guelph, Guelph,, Canada.

[10]. McQuade, J. M., Rogers, M., Rosenfeld, A., Savitz, M., Westly, S., (2012) Report of the Building Energy Efficiency Subcommittee to the Secretary of Energy Advisory Board https://www.energy.gov/sites/prod/files/Building%20Efficie ncy%2 0Re port.pdf

[11]. Mirrahimi, S., Mohamed, M. F., Haw, L. C., Ibrahim, N. L. N., Yusoff, W. F. M., & Aflaki, A. (2016). The effect of building envelope on the thermal comfort and energy saving for high-rise buildings in hot–humid climate. Renewable and Sustainable Energy Reviews, 53, 1508- 1519.https://doi.org/ 10.1016/j.rser.2015.09.055

[12]. Missoum, A., Elmir, M., Bouanini, M., & Draoui, B. (2016). Numerical simulation of heat transfer through the building facades of buildings located in the city of Bechar. International Journal of Multiphysics 10, 441- 450. https://doi.org/10.2115 2/175 0-9548.10.4.441

[13]. Shaheed, A. A. A., Abdulla, F. A., & Mehdi, Q. S. (2010). Experimental and Theoretical Investigation of Composite Materials as Thermal Insulation for Resident Building. Journal of Engineering and Sustainable Development, 14(3), 105-123.

[14]. Snøhetta, (2019). Harvard Graduate School of Design News, https://www.gsd.harvard.edu/2018/12/harvardcen terforgreenbuildingsandcitiesunveilshousezero-datadriven- infrastructure-set-to-enable-cutting-edge-researchshift- the-design-andoperation-of-buildings/

[15]. Stauffer, W. N (2018, June 7) Cooling buildings World wide, MIT Energy Initiative (MITEI) news, http://energy.mit.e du/news/c ooling-buildings-worldwide/ June 7, 2018

[16]. Suman, B. M., & Srivastava, R. K. (2009). Influence of thermal insulation on conductive heat transfer through roof ceiling construction. http://nopr.niscair.res.in/handle/1 234 56789/3145

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Research Paper

Simple Formula for Determination of Period of Vibration of Shear Wall Dominant Symmetric Buildings

S. G. Joshi* , Naveen Kwatra**

* Department of Civil Engineering, Vishwakarma Institute of Information Technology, Pune, India.

** Department of Civil Engineering, Thapar University, Patiala, Punjab, India.

Joshi, S. G., & Kwatra, N. (2020). Simple Formula for Determination of Period of Vibration of Shear Wall Dominant Symmetric Buildings, i-manager's Journal on Civil Engineering, 10(1), 13-21. https://doi.org/10.26634/jce.10.1.15439

Abstract

Fundamental period of vibration is determined by an empirical equation by many building codes. Many times these values are estimated over-conservatively. Many researchers have attempted modification in these formulae with an aim of predicting the period of vibration as close to the reality. Soft computing technique of Genetic Programming (GP) is applied to arrive at the simple empirical equation for fundamental period of vibration. In the present study, GP models are developed using input parameters which represent mass, stiffness and geometry of the buildings directly or indirectly. Total number of 70 buildings are analyzed out of which, data set of 49 buildings are used to develop the models. The GP technique has given the equations of period of vibration, which are then modified as per the error obtained from the experimentation conducted on mild steel frames. Finally a simple expression for fundamental period of vibration is suggested, which is then verified from the data set found in the literature.

References

[1]. American Society of Civil Engineers (2005). Minimum design loads for buildings and other structures. ASCE 7-10, Reston, Virginia.

[2]. CEN, M. (2004). European Committee for Standardisation. Eurocode 8: Design of Structures for Earthquake Resistance. Part 1: General Rules, Seismic Actions and Rules for Buildings, European Standard EN 1998- 1: 2004 (stage 51).

[3]. Crowley, H., & Pinho, R. (2006, September). Simplified equations for estimating the period of vibration of existing buildings. In First European Conference on Earthquake Engineering and Seismology (Vol. 1122).

[4]. Crowley, H., & Pinho, R. (2010). Revisiting Eurocode 8 formulae for periods of vibration and their employment in linear seismic analysis. Earthquake Engineering and Structural Dynamics, 39(2), 223-235. https://doi.org/10.100 2/eqe.94 9

[5]. Gaur, S., & Deo, M. C. (2008). Real-time wave forecasting using genetic programming. Ocean Engineering, 35(11-12), 1166-1172. https://doi.org/10.10 16/j.2008.04.007

[6]. Gilles, D., & Mcclure, G. (2008), Development of a period database for buildings in Montreal using ambient vibrations. In Proceedings of the 14^th World Conference on Earthquake Engineering, October 12-17, Beijing, China

[7]. Goel, R.K. and Chopra, A.K. (1997) Period Formulas for Moment-Resisting Frame Buildings. Journal of Structural Engineering, 123, 1454-1461. http://dx.doi.org/10.1061/ (ASCE)0733-9445(1997) 123:11(1454)

[8]. Goel, R. K. and Chopra, A. K. (1998), Period Formulas for Concrete Shear Wall Buildings, Journal of Structural Engineering, 124(4), 426-433. https://pdfs.semanti cscholar.org/9a45/21076bd788b00115832931fd2bd8c4 8cd3c2.pdf

[9]. Hatzigeorgiou, G. D., & Kanapitsas, G. (2013). Evaluation of fundamental period of low-rise and mid-rise reinforced concrete buildings. Earthquake Engineering and Structural Dynamics, 42(11), 1599-1616. https://doi.org/ 10.1002/eqe.2289

[10]. Heshmati A. A. R, Salehzade H, Alavi A. H, Gandomi A. H, Badkobeh A, Ghasemi A (2008), On the applicability of linear genetic programming for the formulation of soil classification. American-Eurasian Journal of Agriculture and Environment Science, 4(5), 575-583

[11]. International Conference of Building Officials (1997). Uniform Building Code, International Conference of Building Officials. Pasadena, CA.

[12]. IS 1893 (Part 1): 2016, Criteria for earthquake resistant design of structures-Part 1: General provisions and buildings (fifth revision), Bureau of Indian Standards, New Delhi, India.

[13]. Johari, A., Habibagahi, G., & Ghahramani, A. (2006). Prediction of soil–water characteristic curve using genetic programming. Journal of Geo-technical and Geoenvironmental Engineering, 132(5), 661-665. https://doi.org/10.1061/(ASCE)1090-0241(2006)1 32:5(661)

[14]. Joshi, S. G., Londhe, S. N., & Kwatra, N. (2014). Determination of natural periods of vibration using genetic programming. Earthquakes and Structures, 6(2), 201-216. https://doi.org/10.12989/eas.2014.6.2.201

[15]. Kwon, O. S., & Kim, E. S. (2010). Evaluation of building period formulas for seismic design. Earthquake Engineering and Structural Dynamics, 39(14), 1569-1583. https://doi.org/10.1002/eqe.998

[16]. Lee, L. H., Chang, K. K., & Chun, Y. S. (2000). Experimental formula for the fundamental period of RC buildings with shear-wall dominant systems. The Structural Design of Tall Buildings, 9(4), 295-307. https://doi.org /10.1 002/1099-1794(200009)9:4<295:AID-TAL153 >3.0.CO;2-9

[17]. Londhe, S. N., & Dixit, P. R. (2012). Genetic programming: A novel computing approach in modelling water flows. In Genetic Programming-New approaches and successful applications. Intech Open.

[18]. Morales, M. D. (2000), Fundamental period of vibration for reinforced concrete buildings,(Doctoral dissertation), University of Ottawa, Canada.

[19]. Sezen, H., Whittaker, A. S., Elwood, K. J., & Mosalam, K. M. (2003). Performance of reinforced concrete buildings during the August 17, 1999 Kocaeli, Turkey earthquake, and seismic design and construction practise in Turkey. Engineering Structures, 25(1), 103-114. https://doi.org/10. 1016/S0141-0296(02)00121-9

[20]. Shaw, D., Miles, J., & Gray, A. (2004). Genetic programming within civil engineering. In Adaptive Computing in Design and Manufacture VI (pp. 51-61). Springer, London. https://doi.org/10.1007/978-0-85729-33 8- 1_5

[21]. Sindel, Z., Akbaş, R., & Tezcan, S. S. (1996). Drift control and damage in tall buildings. Engineering Structures, 18(12), 957-966. https://doi.org/10.1016/0141-0296(95)00 215-4

[22]. Solomatine D. P. (2002) Data driven modelling: Paradigm, methods, experiences. In Proceedings of 5 International Conference on Hydroinformatics, Cardiff, UK.

[23]. Sonuvar, M. O., Ozcebe, G., & Ersoy, U. (2004). Rehabilitation of reinforced concrete frames with reinforced concrete infills. Structural Journal, 101(4), 494-500.

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Research Paper

Development of Micro Simulated VISSIM Model for Signalized Intersection

Gautham Raj B. R.* , Sunil S., Varuna M.*, Durga Prashanth L.****

* RV College of Engineering, Bengaluru, India.

-** Department of Civil Engineering, RV College of Engineering, Bengaluru, India.

Raj, B. R. G., Sunil, S., Varuna, M., & Prashanth, L. D. (2020). Development of Micro Simulated Vissim Model for Signalized Intersection, i-manager's Journal on Civil Engineering, 10(1), 22-30. https://doi.org/10.26634/jce.10.1.15558

Abstract

The study aims at developing a micro-simulation model for signalized intersection to reduce the travel time by improving the signal program. The data collection includes Classified Turning Volume count at the Intersection, Geometry of the Intersection, Signal Timing and Phasing and Delay and Travel Time across the intersection. The model is developed in PTV (VISSIM) “Verkehr In Städten - SIMulationsmodell” and is calibrated using Genetic Algorithm (GA) through the Component Object Model (COM), Application Programming Interface (API) enabled through MATLAB, also by manual systematic adjustments of parameters, which is then validated with a different dataset with a Mean Absolute Percentage Error (MAPE) within the limit of 15%. Results from the Simulation after signal program optimization indicated a reduction in the travel time upto 25% to the users.

References

[1]. Ashalatha, R., Salini, S., & Sreelekshmi, S. (2018). Calibration and validation of VISSIM for urban heterogeneous traffic. Indian Highways, 46(1).

[2]. Arroju, R., Gaddam, H. K., Vanumu, L. D., Rao, K. R., (2015). Comparative evaluation of roundabout capacities under heterogeneous traffic conditions. Journal of Modern Transportation. 23(4), 310-324. https://doi.org/10.1007/s 40534-015-0089-8

[3]. Doina, K. S., & Chin, H. C. (2007). Traffic simulation modeling: VISSIM. Faculty of Engineering, Civil Engineering Department, National University of Singapore, 3.

[4]. Dowling, R., Skabardonis, A., & Alexiadis, V. (2004). Traffic analysis toolbox, volume III: Guidelines for applying traffic microsimulation modeling software (No. FHWA-HRT-04-040). Federal Highway Administration. Office of Operations, United States.

[5]. Fang, F. C., & Elefteriadou, L. (2005). Some guidelines for selecting microsimulation models for interchange traffic operational analysis. Journal of Transportation Engineering, 131(7), 535-543.https://doi.org/10.1061/(ASCE)0733-947X (2005)131:7(535)

[6]. Manjunatha, P., Vortisch, P., & Mathew, T. V. (2013, January). Methodology for the calibration of VISSIM in mixed traffic. In Transportation Research Board 92^nd Annual Meeting (Vol. 11). Transportation Research Board Washington, DC, United States. https://trid.trb.org/view/124 2159

[7]. Ray Shank (2014). I-5 Mellen Street to blakeslee junction VISSIM model confidence and calibration report existing conditions-PM Peak. Technical Memorandum, WSDOT.

[8]. Khanna, S. K., Justo, C. E. G., & Veeraraghavan, A. (2014). Highway Engineering Chapter 5, Revised 10^th Edition.

[9]. Siddharth, S. P., & Ramadurai, G. (2013). Calibration of VISSIM for Indian heterogeneous traffic conditions. Procedia- Social and Behavioral Sciences, 104(0), 380-389. https://doi.org/10.1016/j.sbspro.2013.11.131

[10]. Mathew, T. V., & Krishna Rao, K. V. (2007). Introduction to Transportation Engineering. Chapter 39. Traffic Intersections, NPTEL.

[11]. Mathew, T. V., & Radhakrishnan, P. (2010)., Calibration of microsimulation models for non-lane-based heterogeneous traffic at signalized intersections. Journal of Urban Planning and Development. 136 (1). ASCE.https://doi.org/1 0.1061/(ASCE)0733-9488(2010) 36:1(59)

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Research Paper

Statistical Calculation for Assessment of Durability of GFRC with SCM by Using Anova

Badrinarayan Rath* , Shirish Deo, Gangadhar Ramtekkar*

* Department of Civil Engineering, Wollega University, Nekemte, Ethiopia.

-* Department of Civil Engineering, NIT, Raipur, Chhattisgarh, India.

Rath, B., Deo, S., & Ramtekkar, G. (2020). Statistical Calculation for Assessment of Durability of GFRC with SCM by Using ANOVA, i-manager's Journal on Civil Engineering, 10(1), 31-37. https://doi.org/10.26634/jce.10.1.17106

Abstract

Durability of concrete has been largely studied in order to prevent steel corrosion of concrete reinforcement for the production of new concrete structures in extreme environments. Both liquid and gas permeability are the factors that lead to the deterioration of concrete and corrosion of the reinforcement. Hence, various researchers have suggested performing Electrical Resistivity Test, Ultrasonic Pulse Velocity Test and Carbonation Test against the durability of the concrete. In this research, durability of concrete is improved by replacing cement and sand partially with fly ash up to 40% by weight and pond ash upto 20% by volume, simultaneously with introduction of glass fiber 0.1% volume of fraction. In this paper, importance of durability tests are analyzed by ANOVA method for measurement of corrosion and service life period is analyzed for various mixes.

References

[1]. Andrade, C. (2010). Types of models of service life of reinforcement: The case of the resistivity. Concrete research letters, 1(2), 73-80.

[2]. Bhargava, A., & Banthia, N. (2008). Permeability of concrete with fiber reinforcement and service life predictions. Materials and Structures, 41(2), 363- 372.https://doi.org/10.1617/s11527-007-9249-6

[3]. Chia, K. S.,& Zhang, M. H. (2002). Water permeability and chloride penetrability of high-strength lightweight aggregate concrete. Cement and concrete research, 32(4), 639-645 https://doi.org/10.1016/S0008-8846 (01)00738-4.

[4]. Hallberg, D. (2005). Quantification of Exposure Classes in the European Standard EN 206-1.http://www.divaportal. org/smash/record.jsf?pid=diva2%3A14281

[5]. Han, S. H., Kim, J. K., & Park, Y. D. (2003). Prediction of compressive strength of fly ash concrete by new apparent activation energy function. Cement and Concrete Research, 33(7), 965-971.https://doi.org/10.1016/S0008- 8846(03)00007-3

[6]. Langley, W. S., Carette, G. G., & Malhotra, V. M. (1992). Strength development and temperature rise in large concrete blocks containing high volumes of low-calcium (ASTM Class F) fly ash. Materials Journal, 89(4), 362-368.

[7]. Malhotra V. M. (2002), “High Performance, High- Volume Fly Ash Concrete: A Solution to the Infrastructure Needs of India”, Indian Concrete Journal, Vol-76:103-108.

[8]. Shao-feng, Z., Chun-hua, L., & Rong-gui, L. (2011). Experimental determination of chloride penetration in cracked concrete beams. Procedia Engineering, 24, 380- 384.https://doi.org/10.1016/j.proeng.2011.11.2661

[9]. Tam, C. M., Tam, V. W., & Ng, K. M. (2012). Assessing drying shrinkage and water permeability of reactive powder concrete produced in Hong Kong. Construction and Building Materials, 26(1), 79-89. https://doi.org/10.101 6/j.conbuildmat.2011.05.006

[10]. Tangchirapat, W., & Jaturapitakkul, C. (2010). Strength, drying shrinkage, and water permeability of concrete incorporating ground palm oil fuel ash. Cement and Concrete Composites, 32(10), 767-774. https://doi.org/ 10.1016/j.cemconcomp.2010.08.008

[11].Zhang, P., Li, Q., & Zhang, H. (2012). Fracture properties of high-performance concrete containing fly ash. Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications, 226(2), 170-176. https://doi.org/10.1177%2F 1464420711435759.

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Case Study

Studies on Relationship between Work Sampling and Labour Productivity in Construction

Arun Kumar A. S* , Sreekeshava K. S., Shashanka Mohan V.*

, Department of Civil Engineering, BMS College of Engineering, Bengaluru, Affiliated to Visvesvaraya Technological University, Belgum, India.

Department of Civil Engineering, Jyothy Institute of Technology, Bengaluru, Affiliated to Visvesvaraya Technological University, Belgum, India.

Arunkumar, A. S., Sreekeshava, K. S., & Mohan, V. S. (2020). Studies on Relationship between Work Sampling and Labor Productivity in Construction, i-manager's Journal on Civil Engineering, 10(1), 38-46. https://doi.org/10.26634/jce.10.1.15746

Abstract

Work sampling is a very effective tool for measuring and monitoring the performances of construction crews. Work measurement was conducted at 3 different sites with their unique qualities. Productivity was measured for crews comprising of one mason (skilled worker) and one helper (unskilled worker)-1M (Mason)+1H (Helper). Enough observations were made to ensure a 95% confidence limit and ± 5% accuracy of sampling results. Block work and internal wall plastering were chosen for work study. These works were divided into their elements. Sampling was done by categorizing work elements as Direct Work (DW), Indirect Work (IW) and Not Working or Idle (NW). The regression analysis on the work sampling data shows that observed quantity is having very low correlation with DW content and satisfactory correlation with working content (DW+IW). Results of sampling show that shares of DW, IW and NW are 56%, 27% and 17% for observed works respectively. Labor Utilization Factor (LUF) given by the sum of direct work and half of indirect work is around 69% for all works. The values of LUFs for all sites are in good range and help in monitoring trends. The direct work content is almost twice as indirect contributory work for both works in all sites. 1M+1H crew composition is found to be optimum and economical compared to 2M+1H and 1M+2H combinations. The major cause of delays in all sites is found to be waiting for materials and equipment.

References

[1]. Chancellor, W., Abbott, M., & Carson, C. (2019). Measuring construction industry activity and productivity. Accounting for Construction, 74–86. https://doi.org/10.12 01/9781315231785-5

[2]. Dasgupta, A., & Varghese, K. (2014). Investigation of relationship between results of work sampling and productivity measurement. Indian Institute of Technology, Madras. https://doi.org/10.9734/cjast/2019/v 37i 130270

[3]. Eaton, D. (2015). Lean production productivity improvements for construction professions. Lean Construction. https://doi.org/10.1201/9781482287356

[4]. Ikechukwu, U. F., & Anekwe, M. C. (2015). Establishing appropriate labour output rate for increased site work productivity of building projects in Owerri Metropolis, Imo State. An International Journal of Economic Development Research and Investment: ICIDR. Akwa-lbom, Nigeria, 7(2), 51-62. https://doi.org/10.9734/cjast/2019/v37i130270

[5]. Kanawaty, G. (1992). Introduction to work study. International Labour Organization. Fourth Revised Edition, International Labor Office, Geneva.

[6]. Liou, F. S., & Borcherding, J. D. (1986). Work sampling can predict unit rate productivity. Journal of Construction Engineering and Management, 112(1), 90-103. https://doi.org/0.1061/(ASCE)0733-9364(1986)112:1 (90)

[7]. Pace, C. B. (2003). Labor Availability and Productivity Forecasting. Construction Research. https://doi.org/ 10.1061/40671(2003)111

[8]. Planning Commission Report (2012-17). Employment by Sector - Industries - Planning Commission: Twelfth Five Year Plan Document (2012-17). www.data.gov.in

[9]. Regan, M. (2019). Measuring capital productivity in construction. In Accounting for Construction (pp. 100-128). Routledge.

[10]. Thomas, H. R. (1991). Labor productivity and work sampling: The bottom line. Journal of Construction Engineering and Management, 117(3), 423-444. https://doi.org/10.1061/(ASCE)0733-9364(1991)117: 3(423)

[11]. Thomas, H. R., & Daily, J. (1983). Crew performance measurement via activity sampling. Journal of Construction Engineering and Management, 109(3), 309-320. https://doi. org/10.1061/(ASCE)0733-9364(1983) 109:3(309)

[12]. Thomas, H. R., Guevara, J. M., & Gustenhoven, C. T. (1984). Improving productivity estimates by work sampling. Journal of Construction Engineering and Management, 110(2), 178-188. https://doi.org/10.1061/(ASCE)0733-9364 (1984)110:2(178)

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

Current Issue Vol. 14 Issue 2

Most Read

Most Cited

Volume 10 Issue 1 December - February 2020

Comparative Thermal Performances of Different Envelope Roof Designs for Sustainable Solar Cooling

Osamuyi Obadolagbonyi* , Ben Oni**, Mandoye Ndoye***

*,*** Tuskegee University, Tuskegee, Alabama. ** Department of Electrical and Computer Engineering, Tuskegee University, Tuskegee, Alabama.

Obadolagbonyi, O., Oni, B., & Ndoye, M. (2020). Comparative Thermal Performances of Different Envelope Roof Designs for Sustainable Solar Cooling, i-manager's Journal on Civil Engineering, 10(1), 1-12. https://doi.org/10.26634/jce.10.1.16829

Abstract

References

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Simple Formula for Determination of Period of Vibration of Shear Wall Dominant Symmetric Buildings

S. G. Joshi* , Naveen Kwatra**

* Department of Civil Engineering, Vishwakarma Institute of Information Technology, Pune, India. ** Department of Civil Engineering, Thapar University, Patiala, Punjab, India.

Joshi, S. G., & Kwatra, N. (2020). Simple Formula for Determination of Period of Vibration of Shear Wall Dominant Symmetric Buildings, i-manager's Journal on Civil Engineering, 10(1), 13-21. https://doi.org/10.26634/jce.10.1.15439

Abstract

References

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Development of Micro Simulated VISSIM Model for Signalized Intersection

Gautham Raj B. R.* , Sunil S.**, Varuna M.***, Durga Prashanth L.****

* RV College of Engineering, Bengaluru, India. **-**** Department of Civil Engineering, RV College of Engineering, Bengaluru, India.

Raj, B. R. G., Sunil, S., Varuna, M., & Prashanth, L. D. (2020). Development of Micro Simulated Vissim Model for Signalized Intersection, i-manager's Journal on Civil Engineering, 10(1), 22-30. https://doi.org/10.26634/jce.10.1.15558

Abstract

References

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Statistical Calculation for Assessment of Durability of GFRC with SCM by Using Anova

Badrinarayan Rath* , Shirish Deo**, Gangadhar Ramtekkar***

* Department of Civil Engineering, Wollega University, Nekemte, Ethiopia. **-*** Department of Civil Engineering, NIT, Raipur, Chhattisgarh, India.

Rath, B., Deo, S., & Ramtekkar, G. (2020). Statistical Calculation for Assessment of Durability of GFRC with SCM by Using ANOVA, i-manager's Journal on Civil Engineering, 10(1), 31-37. https://doi.org/10.26634/jce.10.1.17106

Abstract

References

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Pdf

Studies on Relationship between Work Sampling and Labour Productivity in Construction

Arun Kumar A. S* , Sreekeshava K. S.**, Shashanka Mohan V.***

*,*** Department of Civil Engineering, BMS College of Engineering, Bengaluru, Affiliated to Visvesvaraya Technological University, Belgum, India. ** Department of Civil Engineering, Jyothy Institute of Technology, Bengaluru, Affiliated to Visvesvaraya Technological University, Belgum, India.

Arunkumar, A. S., Sreekeshava, K. S., & Mohan, V. S. (2020). Studies on Relationship between Work Sampling and Labor Productivity in Construction, i-manager's Journal on Civil Engineering, 10(1), 38-46. https://doi.org/10.26634/jce.10.1.15746

Abstract

References

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Osamuyi Obadolagbonyi* , Ben Oni, Mandoye Ndoye*

, Tuskegee University, Tuskegee, Alabama.

Department of Electrical and Computer Engineering, Tuskegee University, Tuskegee, Alabama.

* Department of Civil Engineering, Vishwakarma Institute of Information Technology, Pune, India.

** Department of Civil Engineering, Thapar University, Patiala, Punjab, India.

Gautham Raj B. R.* , Sunil S., Varuna M.*, Durga Prashanth L.****

* RV College of Engineering, Bengaluru, India.

-** Department of Civil Engineering, RV College of Engineering, Bengaluru, India.

Badrinarayan Rath* , Shirish Deo, Gangadhar Ramtekkar*

* Department of Civil Engineering, Wollega University, Nekemte, Ethiopia.

-* Department of Civil Engineering, NIT, Raipur, Chhattisgarh, India.

Arun Kumar A. S* , Sreekeshava K. S., Shashanka Mohan V.*

, Department of Civil Engineering, BMS College of Engineering, Bengaluru, Affiliated to Visvesvaraya Technological University, Belgum, India.

Department of Civil Engineering, Jyothy Institute of Technology, Bengaluru, Affiliated to Visvesvaraya Technological University, Belgum, India.