Structural System Prediction Model Based On Cost – Time – Quality Analysis

0*, Pratul N. Nema**, P. Sathishkumar***, B. S. Shashank****, B. L. Shivakumar*****
*-*** Department of Civil Engineering, Visvesvaraya Technological University, Belgaum, Karnataka, India.
**** Assistant Professor, Department of Civil Engineering, R.V. College of Engineering, Bengaluru, India.
***** Professor and Former Dean, R.V. College of Engineering, Bengaluru, India.
Periodicity:March - May'2018
DOI : https://doi.org/10.26634/jste.7.1.14282

Abstract

In present scenario, most building systems are decided on the basis of experience and preferences of Architects and Structural consultants, but seldom are the Project Management aspects considered. Even when they are considered, it is not done in a systematic manner. Because of this it quite often happens that in the middle of the project there are drastic changes in the building systems adopted, which have implications with time, cost, quality, or all of them. There is a general lack of awareness in industries about the different structural systems available and the knowledge about their reliability in the industry. In this work, the construction management methodologies of various building systems, such as Column beam slab, Post Tensioned slab, Reinforced Concrete Wall, and Flat slab were studied. The authors compared the different systems with respect to parameters like suitability, cost, duration, and quality. The authors have generated a prediction model to identify the suitable building system considering the constraints given by the client. From the overall study, Resource-Cost- Duration analysis of a project is carried out and other parameters are optimized when one parameter becomes a constraint, for efficient construction management.

Keywords

Suitability, Cost, Duration, and Quality

How to Cite this Article?

Shreenidhi, R., Nema, P.N., Sathishkumar, P., Shashank. B.S., and Shivakumar, B.L. (2018). Structural System Prediction Model Based on Cost - Time - Quality Analysis. i-manager’s Journal on Structural Engineering, 7(1), 41-47. https://doi.org/10.26634/jste.7.1.14282

References

[1]. Ahn, Y. H. (2010). The development of models to identify relationships between first costs of green building strategies and technologies and life cycle costs for public green facilities (Doctoral Dissertation, Virginia Tech).
[2]. Aibinu, A. A., Dassanayake, D., Chan, T. K., & Thangaraj, R. (2015). Cost estimation for electric light and power elements during building design: A neural network approach. Engineering, Construction and Architectural Management, 22(2), 190-213.
[3]. An, S. H., Kim, G. H., & Kang, K. I. (2007). A case-based reasoning cost estimating model using experience by analytic hierarchy process. Building and Environment, 42(7), 2573-2579.
[4]. Bala, K., Ahmad Bustani, S., & Shehu Waziri, B. (2014). A computer-based cost prediction model for institutional building projects in Nigeria: An Artificial Neural Network approach. Journal of Engineering, Design and Technology, 12(4), 519-530.
[5]. Cheng, M. Y., Tsai, H. C., & Hsieh, W. S. (2009). Webbased conceptual cost estimates for construction projects using Evolutionary Fuzzy Neural Inference Model. Automation in Construction, 18(2), 164-172.
[6]. Cheng, M. Y., Tsai, H. C., & Sudjono, E. (2010). Conceptual cost estimates using evolutionary fuzzy hybrid neural network for projects in construction industry. Expert Systems with Applications, 37(6), 4224-4231.
[7]. Eashwar, S., and Geetha, G. (2016). Trade-off Between Time, Cost, Quality, Safety, Environment - Star Optimization Model. International Journal of Computer Technology and Applications, 9(11), 5467-5486.
[8]. Fu, F., & Zhang, T. (2016). A New Model for Solving Time- Cost-Quality Trade-Off Problems in Construction. PloS one, 11(12), e0167142.
[9]. Günayd?n, H. M., & Do?an, S. Z. (2004). A neural network approach for early cost estimation of structural systems of buildings. International Journal of Project Management, 22(7), 595-602.
[10]. Hong, T., Hyun, C., & Moon, H. (2011). CBR-based cost prediction model-II of the design phase for multi-family housing projects. Expert Systems with Applications, 38(3), 2797-2808.
[11]. Ji, S. H., Park, M., & Lee, H. S. (2011). Cost estimation model for building projects using case-based reasoning. Canadian Journal of Civil Engineering, 38(5), 570-581.
[12]. Juszczyk, M. (2013). The use of artificial neural networks for residential buildings conceptual cost estimation. In AIP Conference Proceedings (Vol. 1558, No. 1, pp. 1302-1306). AIP.
[13]. Khang, D. B., & Myint, Y. M. (1999). Time, cost and quality trade-off in project management: A case study. International Journal of Project Management, 17(4), 249- 256.
[14]. Kim, G. H., An, S. H., & Kang, K. I. (2004a). Comparison of construction cost estimating models based on regression analysis, neural networks, and case-based reasoning. Building and Environment, 39(10), 1235-1242.
[15]. Kim, G. H., Yoon, J. E., An, S. H., Cho, H. H., & Kang, K. I. (2004b). Neural network model incorporating a genetic algorithm in estimating construction costs. Building and Environment, 39(11), 1333-1340.
[16]. Kim, S. (2013). Hybrid forecasting system based on case-based reasoning and analytic hierarchy process for cost estimation. Journal of Civil Engineering and Management, 19(1), 86-96.
[17]. Koo, C. W., Hong, T., Hyun, C. T., Park, S. H., & Seo, J. O. (2010). A study on the development of a cost model based on the owner's decision making at the early stages of a construction project. International Journal of Strategic Property Management, 14(2), 121-137.
[18]. Koo, C., Hong, T., & Hyun, C. (2011). The development of a construction cost prediction model with improved prediction capacity using the advanced CBR approach. Expert Systems with Applications, 38(7), 8597-8606.
[19]. Kutner, M., Nachtsheim, C. J., Li. N.W. (2004). Applied th Linear Statistical Models (5 Ed.), McGraw-Hill/Irwin.
[20]. Lai, C. C., & Lee, W. L. (2006). A WICE approach to real-time construction cost estimation. Automation in Construction, 15(1), 12-19.
[21]. Li, H., Shen, Q. P., & Love, P. E. (2005). Cost modelling of office buildings in Hong Kong: An exploratory study. Facilities, 23(9/10), 438-452.
[22]. Lowe, D. J., Emsley, M. W., & Harding, A. (2006). Predicting construction cost using multiple regression techniques. Journal of Construction Engineering and Management, 132(7), 750-758.
[23]. Moon, S. W., Kim, J. S., & Kwon, K. N. (2007). Effectiveness of OLAP-based cost data management in construction cost estimate. Automation in Construction, 16(3), 336-344.
[24]. Saputra, Y. A., & Latiffianti, E. (2015). Project reliability model considering time–cost–resource relationship under uncertainty. Procedia Computer Science, 72, 561-568.
[25]. Shankar, N. R., Raju, M. M. K., Srikanth, G., & Bindu, P. H. (2011). Time, cost and quality trade-off analysis in construction of projects. Contemporary Engineering Sciences, 4(6), 289-299.
[26]. Siqueira, I. (1999). Neural network-based cost estimating (Doctoral Dissertation, Concordia University).
[27]. Sonmez, R. (2004). Conceptual cost estimation of building projects with regression analysis and neural networks. Canadian Journal of Civil Engineering, 31(4), 677-683.
[28]. Sonmez, R. (2005). Review of conceptual cost modeling techniques. AACE International Transactions, ES71.
[29]. Sonmez, R. (2008). Parametric range estimating of building costs using regression models and bootstrap. Journal of Construction Engineering and Management, 134(12), 1011-1016.
[30]. Trost, S. M., & Oberlender, G. D. (2003). Predicting accuracy of early cost estimates using factor analysis and multivariate regression. Journal of Construction Engineering and Management, 129(2), 198-204.
[31]. Yu, W. D. (2006). PIREM: a new model for conceptual cost estimation. Construction Management and Economics, 24(3), 259-270.
[32]. Zayed, T. M., & Halpin, D. W. (2005). Productivity and cost regression models for pile construction. Journal of Construction Engineering and Management, 131(7), 779- 789.
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