Developing a predictive model for quantity estimation of tie columns and lintel beams in residential construction
Article Sidebar
Main Article Content
Abstract
Accurate construction cost estimation is at the root of any effective project planning, yet it often requires extensive expertise and time-consuming calculations. This paper discusses a predictive equation for estimating the quantity of tie columns and lintel beams in a two-story residential building. In this study, multiple linear regression analysis was employed to identify the significant variables that impact the quantity of those structural elements using 75 sets of residential drawings, all of which featured conventional two-story brick masonry construction with reinforced concrete frames. The formulated equation, where Y represents the total linear meters of tie columns and lintel beams combined, is expressed as Y = 1.834 + 1.243 (brick wall area in m²) - 0.639 (open space area in m²). The equation was checked against fifteen residential designs with detailed estimates. The percentage error was observed to be between -3.58% and 5.37%, which is considered within an acceptable limit for preliminary estimates. This equation could provide a useful tool for cost estimators, offering a much-simplified approach yet yielding reasonable accuracy for preliminary assessments of the structural quantities of buildings. This research highlights the equation's potential for improving efficiency in project planning and cost estimation within its defined scope, with further validation across a wider range of designs recommended to broaden its applicability.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
Dandan TH, Sweis G, Sukkari LS, Sweis RJ. Factors affecting the accuracy of cost estimate during various design stages. J Eng Des Technol. 2020;18(4):787-819.
Enshassi A, Mohamed S, Madi I. Factors affecting accuracy of cost estimation of building contracts in the Gaza Strip. J Financ Manag Prop Constr. 2005;10(2):115-25.
Kim GH, An SH, Kang KI. Comparison of construction cost estimating models based on regression analysis, neural networks, and case-based reasoning. Build Environ. 2004;39(10):1235-42.
Ibrahim AH, Elshwadfy LM. Factors affecting the accuracy of construction project cost estimation in Egypt. Jordan J Civ Eng. 2021;15(3):329-44.
Elfaki A, Alatawi S, Abushandi E. Using intelligent techniques in construction project cost estimation: 10-year survey. Adv Civ Eng. 2014;2014:107926.
Juszczyk M. The challenges of nonparametric cost estimation of construction work with the use of artificial intelligence tools. Procedia Eng. 2017;196:415-22.
Holm L, Schaufelberger JE, Griffin D, Cole T. Construction cost estimating: process and practices. USA: Pearson Prentice Hall; 2004.
Nimmahnpatchrin W, Wantanakorn D. Cost estimation of 2 storey residential buildings using Monte Carlo simulation technique. Proceeding of the 1st Ratchathani University National Conference; 2016 Jul 29; Ubonratchathani, Thailand. p. 331-42 (In Thai)
Tuptimtong A, Katavettavasak S, Jaipunya K. The study of unit cost of electrical and communication systems. Proceeding of the 22nd National Convention on Civil Engineering; 2017 Jul 18-21; Nakhon Ratchasima, Thailand. p. 716-21. (In Thai)
Project Management Institute. Success in disruptive times: expanding the value delivery landscape to address the high cost of low performance. USA: Project Management Institute; 2018.
Staub-French S, Fischer M, Kunz J, Paulson B. A generic feature-driven activity-based cost estimation process. Adv Eng Inform. 2003;17(1):23-39.
Sayed M, Abdel-Hamid M, El-Dash K. Improving cost estimation in construction projects. Int J Constr Manag. 2023;23(1):135-43.
Thipparate T, Chaisongkroh N, Kulprapa N. Construction cost estimation of residential buildings by implementation of ANFIS. Proceeding of the 22nd National Convention on Civil Engineering; 2017 Jul 18-21; Nakhon Ratchasima, Thailand. p. 818-23. (In Thai)
Rujirayanyong T. Estimate cost for apartment building using regression analysis. Rangsit Univ J Eng Technol. 2012;15(2):33-9. (In Thai)
Suprasi P. Strategy for construction cost analysis. 4th ed. Bangkok: S. Asia Publishing; 2009. (In Thai)
Apinayan K, Perera BAKS, Weerasooriya D, Ranadewa KATO. Leveraging BIM technology for improved accuracy in detailed cost estimation in Sri Lanka. FARU J. 2023;10(2):1-10.
Chanwijit J, Teamtong W. Preliminary cost estimation for condominium construction according to segment condominium in Bangkok Metropolitan. Proceeding of the 22nd National Convention on Civil Engineering; 2017 Jul 18-21; Nakhon Ratchasima, Thailand. p. 818-23. (In Thai)
Rajole A, Darade M. Analysis of factor affecting material management in building construction projects by using s-curve analysis. Int J Res Trends Innov. 2018;3(7):1-4.
Donyavi S, Flanagan R. The impact of effective material management on construction site performance for small and medium sized construction enterprises. The 25th annual conference of the association of researchers in construction management; 2009 Sep 7-9; Nottingham, UK. p. 11-20.
Tedla Y, Patel D. Improving effective material management by identifying common factors in building construction project. Int Res J Eng Technol. 2018;5(1):146-50.
Lee C, Lee EB. Prediction method of real discount rate to improve accuracy of life-cycle cost analysis. Energy Build. 2017;135: 225-32.
Shane JS, Molenaar KR, Anderson S, Schexnayder C. Construction project cost escalation factors. J Manag Eng. 2009;25(4):221-9.
Monkaew S, Choosakul C. Develop an estimating factor to help estimating masonry wall area of two stories houses. ASEAN J Sci Tech Rep. 2021;24(2):115-24.
Monkaew S, Choosakul C. A study the ratio of concrete volume per reinforcement quantity in earthquake resistant-An OBEC 4-story reinforced concrete school building. J KMUTNB. 2023;33(1):92-101. (In Thai)
Kitkanchanat C. A study of major component’s ratio in housing construction for approved BOQ [thesis]. Thailand: King Mongkut’s University of Technology Thonburi; 2001. (In Thai)
Ganiyu BO, Zubairu IK. Project cost prediction model using principal component regression for public building projects in Nigeria. J Build Perform. 2010;1(1):21-8.
Cho HG, Kim KG, Kim JY, Kim GH. A comparison of construction cost estimation using multiple regression analysis and neural network in elementary school project. J Korea Inst Build Constr. 2013;13(1):66-74.
Al-Zwainy FMS, Abdulmajeed MH, Aljumaily HSM. Using multivariable linear regression technique for modeling productivity construction in Iraq. Open J Civ Eng. 2013;3(3):127-35.
Varghese S, Kanchana S. Parametric estimation of construction cost using combined bootstrap and regression technique. Int J Civil Eng Technol. 2014;5(12):201-5.
Raheem K. Unit price and cost estimation equations through items percentage of construction works in a desert area. J Eng. 2015;21(2):37-47.
Rasool HS, Al-Zwainy FMS. Estimating productivity of brickwork item using logistic and multiple regression approaches. Sch J Eng Technol. 2016;4(5):234-43.
Al-Zwainy FMS, Hadhal NT. Building a mathematical model for predicting the cost of communication towers projects using multifactor linear regression technique. Int J Constr Eng Manag. 2016;5(1):25-9.
Thomas N, Thomas AV. Regression modelling for prediction of construction cost and duration. Appl Mech Mater. 2016;857:195-9.
Dharwadkar NV, Arage SS. Prediction and estimation of civil construction cost using linear regression and neural network. Int J Intell Syst Des Com. 2018;2(1):28-44.
Abd AM, Jasim NA, Naseef FS. Developing a prediction model to predict the construction project cost by using multiple linear regression technique. Indian J Sci Technol. 2019;12(7):1-6.
Mahamid I. The development of regression model for preliminary prediction of road construction duration. Int J Eng Inf Syst. 2019;3(4):14-20.
Kaushal A, Shankar A. House price prediction using multiple linear regression. Proceedings of the International Conference on Innovative Computing and Communication (ICICC); 2021 Feb 20-21; Delhi, India. p. 1-7.
Zhang Q. Housing price prediction based on multiple linear regression. Sci Program. 2021;2021:1-9.
Hai C. Construction and application of multiple linear regression model for construction project cost. International Conference on Advanced Enterprise Information Systems (AEIS); 2021 Jun 18-20; St. Petersburg, Russian Federation. p. 54-8.
Ahmed C. Early cost estimation models based on multiple regression analysis for road and railway tunnel projects. Arab J Geosci. 2021;14:972.
Liu G. Research on prediction and analysis of real estate market based on the multiple linear regression model. Sci Program. 2022;2022:1-8.
Mahamid I. Early cost estimating for road construction projects using multiple Regression techniques. Australasian J Constr Econ Build. 2011;11(4):87-101.
Prasetyono PN, Suryanto HSM, Dani H. Predicting construction cost using regression techniques for residential building. J Phys: Conf Ser. 2021;1899:012120.
Petroutsatou C, Lambropoulos S, Pantouvakis JP. Road tunnel early cost estimates using multiple regression analysis. Oper Res Int J. 2006;6(3):311-22.
Elfahham Y. Estimation and prediction of construction cost index using neural networks, time series, and regression. Alex Eng J. 2019;58(2):499-506.
Huang X, Gao L, Crosbie RS, Zhang N, Fu G Doble R. Groundwater recharge prediction using linear regression, multi-layer perception network, and deep learning. Water. 2019;11(9):1879.
Lowe DJ, Emsley MW, Harding A. Predicting construction cost using multiple Regression techniques. J Constr Eng Manag. 2006;132(7):750-8.
Aslam B, Maqsoom A, Inam H, Basharat M, Ullah F. Forecasting construction cost index through artificial intelligence. Societies. 2023;13(10):219.
Chen WT, Huang YH. Approximately predicting the cost and duration of school reconstruction projects in Taiwan. Constr Manag Econ. 2006;24(12):1231-9.
Kimotho J, Yusuf M, Nyang’au S. Project cost estimation and performance of sports stadia construction projects in Kenya. Int Res J Proj Manag. 2023;5(3):13-25.
The Engineering Institute of Thailand under H.M. the King’s Patronage. Guidelines for measuring the quantity of building construction. Bangkok: EIT; 2005. (In Thai)
Lakkhanaadisor N. Tie column and lintel beam [Internet]. 2024 [cited 2024 Feb 12]. Available from: https://www.tpa.or.th/ tpanews/upload/mag_content/58/ContentFile1057.pdf. (In Thai)
Chanabun S. Statistics and data analysis. [Internet]. 2024 [cited 2024 Jul 19]. Available from: https://www.coursehero.com/ file/44110113/6-2pdf/. (In Thai)
Gupta A, Singhal V. Experimental investigation of confined masonry walls with openings. J Struct Eng. 2024;150(10):13305.
Stavroulaki ME, Liarakos VB. Dynamic analysis of a masonry wall with reinforced concrete lintels or Tie-beams. Eng Struct. 2012;44:23-33.
Bonisha B, Gupta A, Singhak V, Kaushik HB. Design of tie-columns in confined masonry structures for lateral loads. Earthq Spectra. 2024;40(3):2116-36.
Alkhuadhan ANA, Naimi S. A new model for cost estimation construction project using hybrid importance regression ensemble method. Acta Logistica. 2023;10(2):191-8.
Yang SW, Moon SW, Jang H, Choo S, Kim SA. Parametric method and building information modeling-based cost estimation model for construction cost prediction in architectural planning. Appl Sci.2022;12(19):9553.
Saeidlou S, Ghadiminia N. A construction cost estimation framework using DNN and validation unit. Build Res Inf. 2024;52(1-2):38-48.
Akanbi T, Zhang J. Design information extraction from construction specifications to support cost estimation. Autom Constr. 2021;131:103835.
