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Planning the time and cost of implementing construction projects using an example of residential buildings

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Warianty tytułu
PL
Planowanie czasu i kosztu realizacji przedsięwzięć budowlanych na przykładzie budynków zbiorowego zamieszkania
Języki publikacji
EN
Abstrakty
EN
The constant increase in the population of cities affects the development of housing. Investors, in their activities related to the profit from the sale of flats, focus on the completion of residential buildings, which must be timely and in accordance with the budget assumptions. Therefore, there is a problem concerning the correct planning of the costs and duration of an investment. The aim of the conducted research was to determine the shape and course of the cost curves for construction projects related to the construction of residential buildings. Based on the analysis of the authors’ own studies carried out in a homogeneous research group of 11 residence buildings, an original attempt was made to determine the area of the curve, which indicates the area of correct planning of cumulative costs and the forecasting of their deviations in the financial outlays of construction projects. By knowing the planned cost and duration of a construction project, and by using the proposed 6th degree polynomial, it is possible to determine the planned monthly work and expenditure amounts, and thus correctly plan the investment costs over time. It was proven that the planned work and expenditure advancement of the housing construction sector is greater in the first stage of its implementation when compared to the actual state. The determined 6th degree polynomials describe the regularity that shows that for half of the planned duration of works, the planned work and expenditure advancement is approx. 46%, while the actual advancement is approx. 35%.
PL
Nieustanny wzrost liczby ludności miast wpływa na rozwój budownictwa mieszkaniowego, głównie w sektorze developerskim. Inwestorzy w swoich działaniach, związanych z czerpaniem zysku ze sprzedaży mieszkan nastawieni są na terminową i zgodną z założeniami budżetowymi realizację budynków mieszkalnych. W związku z tym pojawia się problem prawidłowego zaplanowania kosztów i czasu trwania inwestycji i skutecznego monitorowania realizacji i ponoszonych nakładów finansowych. Istnieje wiele metod do szacowania/prognozowania przepływów finansowych związanych z realizacją robót budowlanych, jednak bardzo często są one skomplikowane i wymagają od Inwestorów określenia różnych i często trudnych do określenia zmiennych o charakterze losowym. Celem prowadzonych badań było określenie kształtu i przebiegu krzywych kosztowych dla przedsięwzięć budowlanych związanych z realizacją budynków zbiorowego zamieszkania. Na podstawie analizy przeprowadzonych badań własnych w jednorodnej grupie badawczej 11 budynków zbiorowego zamieszkania podjęta została autorska próba wyznaczenia pola krzywej, wskazująca obszar poprawnego planowania skumulowanych kosztów i przewidywania ich odchyleń nakładów finansowych przedsięwzięć budowlanych. Znając planowany koszt i czas trwania przedsięwzięcia budowlanego i korzystając z zaproponowanego wielomianu 6-go stopnia możliwe jest określenie planowanych miesięcznych przerobów rzeczowo-finansowych, a tym samym prawidłowe zaplanowanie w czasie kosztów inwestycyjnych.
Rocznik
Strony
243--259
Opis fizyczny
Bibliogr. 36 poz., il., tab.
Twórcy
  • Wrocław University of Science and Technology, Faculty of Civil Engineering, Department of Building Engineering, Wrocław
Bibliografia
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  • [4] E. Radziszewska-Zielina, E. Kania, and G. Śladowski, “Problems of the selection of construction technology for structures in the centres of urban agglomerations”, Archives of Civil Engineering, vol. 64, pp. 55-71, 2018, DOI: 10.2478/ace-2018-0004.
  • [5] B. Nowogońska, “Performance characteristics of buildings in the assessment of revitalization needs”, Civil and Environmental Engineering Reports, vol. 29, pp. 119-127, 2019, DOI: 10.2478/ceer-2019-0009.
  • [6] K. Zima and S. Biel, “The concept of method of detecting and analyzing construction defects in residential buildings”, Archives of Civil Engineering, vol. 65, pp. 81-95, 2019, DOI: 10.2478/ace-2019-0048.
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  • [10] J. Konior, “Random and fuzzy measure of unpredictable construction works”, Archives of Civil Engineering 61, pp. 75-88, 2015, DOI: 10.13189/cea.2019.070202.
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  • [14] A. Leśniak et al.,“Fuzzy AHP application for supporting contractors’ bidding decision”, Symmetry, vol. 10, pp. 642, 2018, DOI: 10.3390/sym10110642.
  • [15] J. Konior, “Enterprise’s risk assessment of complex construction projects”, Archives of Civil Engineering, vol. 61, pp. 63-74, 2015, DOI: 10.1515/ace-2015-0025.
  • [16] W. Lo, Y.T. Chen, “Optimization of contractor’s S-curve”, 24th International Symposium on Automation and Robotics in Construction (ISARC 2007), pp. 417-420, 2007, DOI: 10.22260/ISARC2007/0069.
  • [17] Project Management Institute, “A guide to the project management body of knowledge (PMBOK guide)”, 6th Edition, Project Management Institute (PMI), 2017.
  • [18] IPMA, “IPMA Individual Competence Baseline”, 2015.
  • [19] H.L. Chen, W.T. Chen, and Y.L. Lin, “Earned value project management: Improving the predictive power of planned value”, International Journal of Project Management, vol. 34, pp. 22-29, 2016, DOI: 10.1016/j.ijproman.2015.09.008.
  • [20] D. Przywara and A. Rak, “Monitoring of time and cost variances of schedule using simple earned value method indicators”, Applied Sciences, vol. 1357, 2021, DOI: 10.3390/app11041357.
  • [21] M. Waris, M.F. Khamidi, and A. Idrus, “The cost monitoring of construction projects through Earned Value Analysis”, Journal of Construction Engineering and Project Management, vol. 2, pp. 42-45, 2012, DOI: 10.6106/JCEPM.2012.2.4.042.
  • [22] S.K. Bhosekar and G. Vyas, “Cost controlling using Earned Value Analysis in construction industries”, International Journal of Engineering and Innovative Technology (IJEIT), 1, 2012.
  • [23] J. Konior, “Monitoring of construction projects feasibility by bank investment supervision approach”, Civil Engineering and Architecture, vol. 7, pp. 31-35, 2019, DOI: 10.13189/cea.2019.070105.
  • [24] R. Howes, “Improving the performance of Earned Value Analysis as a construction project management tool”, Engineering Construction and Architectural Management, vol. 7, pp. 399-411, 2000.
  • [25] Z. Yaseen, et al., “Prediction of risk delay in construction projects using a hybrid artificial intelligence model”, Sustainability, vol. 12, p. 1514, 2020, DOI: 10.3390/su12041514.
  • [26] D. Przywara and A. Rak, “The time-cost analysis of schedule monitoring using the earned value method”, Technical Transactions. Civil Engineering, vol. 5, pp. 57-66, 2017, DOI: 10.4467/2353737XCT.17.069.6426.
  • [27] L.C. Chao and H.T. Chen, “Predicting project progress via estimation of S-curve’s key geometric feature values”, Automation in Construction, vol. 57, pp. 33-41, 2015, DOI: 10.1016/j.autcon.2015.04.015.
  • [28] J.R.S. Cristóbal, “The S-curve envelope as a tool for monitoring and control of projects”, Procedia Computer Science, vol. 121, pp. 756-761, 2017, DOI: 10.1016/j.procs.2017.11.097.
  • [29] L.C. Chao and C.F. Chien, “Estimating project S-curves using polynomial function and neural networks”, Journal of Construction Engineering and Management, vol. 135, pp. 169-177, 2009, DOI: 10.1061/(ASCE)0733-9364(2009)135:3(169) .
  • [30] J. Konior and M. Szóstak, “Methodology of planning the course of the cumulative cost curve in construction projects”, Sustainability, vol. 12, pp. 2347, 2020, DOI: 10.3390/su12062347.
  • [31] J. Konior and M. Szóstak, “The S-curve as a tool for planning and controlling of construction process - Case study”, Applied Sciences, vol. 10, p. 2071, 2020, DOI: 10.3390/app10062071.
  • [32] G.A. Barraza, W.E. Back, and F. Mata, “Probabilistic forecasting of project performance using stochastic S curves”, Journal of Construction Engineering and Management, vol. 130, pp. 25-32, 2004, DOI: 10.1061/(ASCE)0733-9364(2004)130:1(25) .
  • [33] J.S. Yao, M.S. Chen, and H-F. Lu, “A fuzzy stochastic single-period model for cash management”, European Journal of Operational Research, vol. 170, pp. 72-90, 2006, DOI: 10.1016/j.ejor.2004.06.017.
  • [34] L.C. Chao and C.F. Chien, “A model for updating project S-curve by using neural networks and matching progress”, Automation in Construction. , vol. 19, pp. 84-91, 2010, DOI: 10.1016/j.autcon.2009.09.006.
  • [35] V. Mohagheghi, S. Meysam Mousavi, and B. Vahdani, “An assessment method for project cash flow under interval-valued fuzzy environment”, Journal of Optimization in Industrial Engineering, vol. 22, pp. 79-80, 2017, DOI: 10.22094/joie.2017.278.
  • [36] E. Plebankiewicz, K. Zima, and D. Wieczorek, “Life cycle cost modelling of buildings with consideration of the risk”, Archives of Civil Engineering, vol. 62, pp. 149-166, 2016, DOI: 10.1515/ace-2015-0071.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-1d9569cb-a8ee-4b86-b4c8-5cefcddac865
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